LCOV - app.info - media/libjpeg/jquant2.c

LCOV - code coverage report
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| Date: | 2012-04-21 | Functions: | 18 | 0 | 0.0 %

           1                 : /*
           2                 :  * jquant2.c
           3                 :  *
           4                 :  * Copyright (C) 1991-1996, Thomas G. Lane.
           5                 :  * Copyright (C) 2009, D. R. Commander.
           6                 :  * This file is part of the Independent JPEG Group's software.
           7                 :  * For conditions of distribution and use, see the accompanying README file.
           8                 :  *
           9                 :  * This file contains 2-pass color quantization (color mapping) routines.
          10                 :  * These routines provide selection of a custom color map for an image,
          11                 :  * followed by mapping of the image to that color map, with optional
          12                 :  * Floyd-Steinberg dithering.
          13                 :  * It is also possible to use just the second pass to map to an arbitrary
          14                 :  * externally-given color map.
          15                 :  *
          16                 :  * Note: ordered dithering is not supported, since there isn't any fast
          17                 :  * way to compute intercolor distances; it's unclear that ordered dither's
          18                 :  * fundamental assumptions even hold with an irregularly spaced color map.
          19                 :  */
          20                 :
          21                 : #define JPEG_INTERNALS
          22                 : #include "jinclude.h"
          23                 : #include "jpeglib.h"
          24                 :
          25                 : #ifdef QUANT_2PASS_SUPPORTED
          26                 :
          27                 :
          28                 : /*
          29                 :  * This module implements the well-known Heckbert paradigm for color
          30                 :  * quantization.  Most of the ideas used here can be traced back to
          31                 :  * Heckbert's seminal paper
          32                 :  *   Heckbert, Paul.  "Color Image Quantization for Frame Buffer Display",
          33                 :  *   Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304.
          34                 :  *
          35                 :  * In the first pass over the image, we accumulate a histogram showing the
          36                 :  * usage count of each possible color.  To keep the histogram to a reasonable
          37                 :  * size, we reduce the precision of the input; typical practice is to retain
          38                 :  * 5 or 6 bits per color, so that 8 or 4 different input values are counted
          39                 :  * in the same histogram cell.
          40                 :  *
          41                 :  * Next, the color-selection step begins with a box representing the whole
          42                 :  * color space, and repeatedly splits the "largest" remaining box until we
          43                 :  * have as many boxes as desired colors.  Then the mean color in each
          44                 :  * remaining box becomes one of the possible output colors.
          45                 :  *
          46                 :  * The second pass over the image maps each input pixel to the closest output
          47                 :  * color (optionally after applying a Floyd-Steinberg dithering correction).
          48                 :  * This mapping is logically trivial, but making it go fast enough requires
          49                 :  * considerable care.
          50                 :  *
          51                 :  * Heckbert-style quantizers vary a good deal in their policies for choosing
          52                 :  * the "largest" box and deciding where to cut it.  The particular policies
          53                 :  * used here have proved out well in experimental comparisons, but better ones
          54                 :  * may yet be found.
          55                 :  *
          56                 :  * In earlier versions of the IJG code, this module quantized in YCbCr color
          57                 :  * space, processing the raw upsampled data without a color conversion step.
          58                 :  * This allowed the color conversion math to be done only once per colormap
          59                 :  * entry, not once per pixel.  However, that optimization precluded other
          60                 :  * useful optimizations (such as merging color conversion with upsampling)
          61                 :  * and it also interfered with desired capabilities such as quantizing to an
          62                 :  * externally-supplied colormap.  We have therefore abandoned that approach.
          63                 :  * The present code works in the post-conversion color space, typically RGB.
          64                 :  *
          65                 :  * To improve the visual quality of the results, we actually work in scaled
          66                 :  * RGB space, giving G distances more weight than R, and R in turn more than
          67                 :  * B.  To do everything in integer math, we must use integer scale factors.
          68                 :  * The 2/3/1 scale factors used here correspond loosely to the relative
          69                 :  * weights of the colors in the NTSC grayscale equation.
          70                 :  * If you want to use this code to quantize a non-RGB color space, you'll
          71                 :  * probably need to change these scale factors.
          72                 :  */
          73                 :
          74                 : #define R_SCALE 2               /* scale R distances by this much */
          75                 : #define G_SCALE 3               /* scale G distances by this much */
          76                 : #define B_SCALE 1               /* and B by this much */
          77                 :
          78                 : static const int c_scales[3]={R_SCALE, G_SCALE, B_SCALE};
          79                 : #define C0_SCALE c_scales[rgb_red[cinfo->out_color_space]]
          80                 : #define C1_SCALE c_scales[rgb_green[cinfo->out_color_space]]
          81                 : #define C2_SCALE c_scales[rgb_blue[cinfo->out_color_space]]
          82                 :
          83                 : /*
          84                 :  * First we have the histogram data structure and routines for creating it.
          85                 :  *
          86                 :  * The number of bits of precision can be adjusted by changing these symbols.
          87                 :  * We recommend keeping 6 bits for G and 5 each for R and B.
          88                 :  * If you have plenty of memory and cycles, 6 bits all around gives marginally
          89                 :  * better results; if you are short of memory, 5 bits all around will save
          90                 :  * some space but degrade the results.
          91                 :  * To maintain a fully accurate histogram, we'd need to allocate a "long"
          92                 :  * (preferably unsigned long) for each cell.  In practice this is overkill;
          93                 :  * we can get by with 16 bits per cell.  Few of the cell counts will overflow,
          94                 :  * and clamping those that do overflow to the maximum value will give close-
          95                 :  * enough results.  This reduces the recommended histogram size from 256Kb
          96                 :  * to 128Kb, which is a useful savings on PC-class machines.
          97                 :  * (In the second pass the histogram space is re-used for pixel mapping data;
          98                 :  * in that capacity, each cell must be able to store zero to the number of
          99                 :  * desired colors.  16 bits/cell is plenty for that too.)
         100                 :  * Since the JPEG code is intended to run in small memory model on 80x86
         101                 :  * machines, we can't just allocate the histogram in one chunk.  Instead
         102                 :  * of a true 3-D array, we use a row of pointers to 2-D arrays.  Each
         103                 :  * pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and
         104                 :  * each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries.  Note that
         105                 :  * on 80x86 machines, the pointer row is in near memory but the actual
         106                 :  * arrays are in far memory (same arrangement as we use for image arrays).
         107                 :  */
         108                 :
         109                 : #define MAXNUMCOLORS  (MAXJSAMPLE+1) /* maximum size of colormap */
         110                 :
         111                 : /* These will do the right thing for either R,G,B or B,G,R color order,
         112                 :  * but you may not like the results for other color orders.
         113                 :  */
         114                 : #define HIST_C0_BITS  5         /* bits of precision in R/B histogram */
         115                 : #define HIST_C1_BITS  6         /* bits of precision in G histogram */
         116                 : #define HIST_C2_BITS  5         /* bits of precision in B/R histogram */
         117                 :
         118                 : /* Number of elements along histogram axes. */
         119                 : #define HIST_C0_ELEMS  (1<<HIST_C0_BITS)
         120                 : #define HIST_C1_ELEMS  (1<<HIST_C1_BITS)
         121                 : #define HIST_C2_ELEMS  (1<<HIST_C2_BITS)
         122                 :
         123                 : /* These are the amounts to shift an input value to get a histogram index. */
         124                 : #define C0_SHIFT  (BITS_IN_JSAMPLE-HIST_C0_BITS)
         125                 : #define C1_SHIFT  (BITS_IN_JSAMPLE-HIST_C1_BITS)
         126                 : #define C2_SHIFT  (BITS_IN_JSAMPLE-HIST_C2_BITS)
         127                 :
         128                 :
         129                 : typedef UINT16 histcell;        /* histogram cell; prefer an unsigned type */
         130                 :
         131                 : typedef histcell FAR * histptr; /* for pointers to histogram cells */
         132                 :
         133                 : typedef histcell hist1d[HIST_C2_ELEMS]; /* typedefs for the array */
         134                 : typedef hist1d FAR * hist2d;    /* type for the 2nd-level pointers */
         135                 : typedef hist2d * hist3d;        /* type for top-level pointer */
         136                 :
         137                 :
         138                 : /* Declarations for Floyd-Steinberg dithering.
         139                 :  *
         140                 :  * Errors are accumulated into the array fserrors[], at a resolution of
         141                 :  * 1/16th of a pixel count.  The error at a given pixel is propagated
         142                 :  * to its not-yet-processed neighbors using the standard F-S fractions,
         143                 :  *              ...     (here)  7/16
         144                 :  *              3/16    5/16    1/16
         145                 :  * We work left-to-right on even rows, right-to-left on odd rows.
         146                 :  *
         147                 :  * We can get away with a single array (holding one row's worth of errors)
         148                 :  * by using it to store the current row's errors at pixel columns not yet
         149                 :  * processed, but the next row's errors at columns already processed.  We
         150                 :  * need only a few extra variables to hold the errors immediately around the
         151                 :  * current column.  (If we are lucky, those variables are in registers, but
         152                 :  * even if not, they're probably cheaper to access than array elements are.)
         153                 :  *
         154                 :  * The fserrors[] array has (#columns + 2) entries; the extra entry at
         155                 :  * each end saves us from special-casing the first and last pixels.
         156                 :  * Each entry is three values long, one value for each color component.
         157                 :  *
         158                 :  * Note: on a wide image, we might not have enough room in a PC's near data
         159                 :  * segment to hold the error array; so it is allocated with alloc_large.
         160                 :  */
         161                 :
         162                 : #if BITS_IN_JSAMPLE == 8
         163                 : typedef INT16 FSERROR;          /* 16 bits should be enough */
         164                 : typedef int LOCFSERROR;         /* use 'int' for calculation temps */
         165                 : #else
         166                 : typedef INT32 FSERROR;          /* may need more than 16 bits */
         167                 : typedef INT32 LOCFSERROR;       /* be sure calculation temps are big enough */
         168                 : #endif
         169                 :
         170                 : typedef FSERROR FAR *FSERRPTR;  /* pointer to error array (in FAR storage!) */
         171                 :
         172                 :
         173                 : /* Private subobject */
         174                 :
         175                 : typedef struct {
         176                 :   struct jpeg_color_quantizer pub; /* public fields */
         177                 :
         178                 :   /* Space for the eventually created colormap is stashed here */
         179                 :   JSAMPARRAY sv_colormap;       /* colormap allocated at init time */
         180                 :   int desired;                  /* desired # of colors = size of colormap */
         181                 :
         182                 :   /* Variables for accumulating image statistics */
         183                 :   hist3d histogram;             /* pointer to the histogram */
         184                 :
         185                 :   boolean needs_zeroed;         /* TRUE if next pass must zero histogram */
         186                 :
         187                 :   /* Variables for Floyd-Steinberg dithering */
         188                 :   FSERRPTR fserrors;            /* accumulated errors */
         189                 :   boolean on_odd_row;           /* flag to remember which row we are on */
         190                 :   int * error_limiter;          /* table for clamping the applied error */
         191                 : } my_cquantizer;
         192                 :
         193                 : typedef my_cquantizer * my_cquantize_ptr;
         194                 :
         195                 :
         196                 : /*
         197                 :  * Prescan some rows of pixels.
         198                 :  * In this module the prescan simply updates the histogram, which has been
         199                 :  * initialized to zeroes by start_pass.
         200                 :  * An output_buf parameter is required by the method signature, but no data
         201                 :  * is actually output (in fact the buffer controller is probably passing a
         202                 :  * NULL pointer).
         203                 :  */
         204                 :
         205                 : METHODDEF(void)
         206               0 : prescan_quantize (j_decompress_ptr cinfo, JSAMPARRAY input_buf,
         207                 :                   JSAMPARRAY output_buf, int num_rows)
         208                 : {
         209               0 :   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
         210                 :   register JSAMPROW ptr;
         211                 :   register histptr histp;
         212               0 :   register hist3d histogram = cquantize->histogram;
         213                 :   int row;
         214                 :   JDIMENSION col;
         215               0 :   JDIMENSION width = cinfo->output_width;
         216                 :
         217               0 :   for (row = 0; row < num_rows; row++) {
         218               0 :     ptr = input_buf[row];
         219               0 :     for (col = width; col > 0; col--) {
         220                 :       /* get pixel value and index into the histogram */
         221               0 :       histp = & histogram[GETJSAMPLE(ptr[0]) >> C0_SHIFT]
         222               0 :                          [GETJSAMPLE(ptr[1]) >> C1_SHIFT]
         223               0 :                          [GETJSAMPLE(ptr[2]) >> C2_SHIFT];
         224                 :       /* increment, check for overflow and undo increment if so. */
         225               0 :       if (++(*histp) <= 0)
         226               0 :         (*histp)--;
         227               0 :       ptr += 3;
         228                 :     }
         229                 :   }
         230               0 : }
         231                 :
         232                 :
         233                 : /*
         234                 :  * Next we have the really interesting routines: selection of a colormap
         235                 :  * given the completed histogram.
         236                 :  * These routines work with a list of "boxes", each representing a rectangular
         237                 :  * subset of the input color space (to histogram precision).
         238                 :  */
         239                 :
         240                 : typedef struct {
         241                 :   /* The bounds of the box (inclusive); expressed as histogram indexes */
         242                 :   int c0min, c0max;
         243                 :   int c1min, c1max;
         244                 :   int c2min, c2max;
         245                 :   /* The volume (actually 2-norm) of the box */
         246                 :   INT32 volume;
         247                 :   /* The number of nonzero histogram cells within this box */
         248                 :   long colorcount;
         249                 : } box;
         250                 :
         251                 : typedef box * boxptr;
         252                 :
         253                 :
         254                 : LOCAL(boxptr)
         255               0 : find_biggest_color_pop (boxptr boxlist, int numboxes)
         256                 : /* Find the splittable box with the largest color population */
         257                 : /* Returns NULL if no splittable boxes remain */
         258                 : {
         259                 :   register boxptr boxp;
         260                 :   register int i;
         261               0 :   register long maxc = 0;
         262               0 :   boxptr which = NULL;
         263                 :
         264               0 :   for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
         265               0 :     if (boxp->colorcount > maxc && boxp->volume > 0) {
         266               0 :       which = boxp;
         267               0 :       maxc = boxp->colorcount;
         268                 :     }
         269                 :   }
         270               0 :   return which;
         271                 : }
         272                 :
         273                 :
         274                 : LOCAL(boxptr)
         275               0 : find_biggest_volume (boxptr boxlist, int numboxes)
         276                 : /* Find the splittable box with the largest (scaled) volume */
         277                 : /* Returns NULL if no splittable boxes remain */
         278                 : {
         279                 :   register boxptr boxp;
         280                 :   register int i;
         281               0 :   register INT32 maxv = 0;
         282               0 :   boxptr which = NULL;
         283                 :
         284               0 :   for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
         285               0 :     if (boxp->volume > maxv) {
         286               0 :       which = boxp;
         287               0 :       maxv = boxp->volume;
         288                 :     }
         289                 :   }
         290               0 :   return which;
         291                 : }
         292                 :
         293                 :
         294                 : LOCAL(void)
         295               0 : update_box (j_decompress_ptr cinfo, boxptr boxp)
         296                 : /* Shrink the min/max bounds of a box to enclose only nonzero elements, */
         297                 : /* and recompute its volume and population */
         298                 : {
         299               0 :   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
         300               0 :   hist3d histogram = cquantize->histogram;
         301                 :   histptr histp;
         302                 :   int c0,c1,c2;
         303                 :   int c0min,c0max,c1min,c1max,c2min,c2max;
         304                 :   INT32 dist0,dist1,dist2;
         305                 :   long ccount;
         306                 :
         307               0 :   c0min = boxp->c0min;  c0max = boxp->c0max;
         308               0 :   c1min = boxp->c1min;  c1max = boxp->c1max;
         309               0 :   c2min = boxp->c2min;  c2max = boxp->c2max;
         310                 :
         311               0 :   if (c0max > c0min)
         312               0 :     for (c0 = c0min; c0 <= c0max; c0++)
         313               0 :       for (c1 = c1min; c1 <= c1max; c1++) {
         314               0 :         histp = & histogram[c0][c1][c2min];
         315               0 :         for (c2 = c2min; c2 <= c2max; c2++)
         316               0 :           if (*histp++ != 0) {
         317               0 :             boxp->c0min = c0min = c0;
         318               0 :             goto have_c0min;
         319                 :           }
         320                 :       }
         321                 :  have_c0min:
         322               0 :   if (c0max > c0min)
         323               0 :     for (c0 = c0max; c0 >= c0min; c0--)
         324               0 :       for (c1 = c1min; c1 <= c1max; c1++) {
         325               0 :         histp = & histogram[c0][c1][c2min];
         326               0 :         for (c2 = c2min; c2 <= c2max; c2++)
         327               0 :           if (*histp++ != 0) {
         328               0 :             boxp->c0max = c0max = c0;
         329               0 :             goto have_c0max;
         330                 :           }
         331                 :       }
         332                 :  have_c0max:
         333               0 :   if (c1max > c1min)
         334               0 :     for (c1 = c1min; c1 <= c1max; c1++)
         335               0 :       for (c0 = c0min; c0 <= c0max; c0++) {
         336               0 :         histp = & histogram[c0][c1][c2min];
         337               0 :         for (c2 = c2min; c2 <= c2max; c2++)
         338               0 :           if (*histp++ != 0) {
         339               0 :             boxp->c1min = c1min = c1;
         340               0 :             goto have_c1min;
         341                 :           }
         342                 :       }
         343                 :  have_c1min:
         344               0 :   if (c1max > c1min)
         345               0 :     for (c1 = c1max; c1 >= c1min; c1--)
         346               0 :       for (c0 = c0min; c0 <= c0max; c0++) {
         347               0 :         histp = & histogram[c0][c1][c2min];
         348               0 :         for (c2 = c2min; c2 <= c2max; c2++)
         349               0 :           if (*histp++ != 0) {
         350               0 :             boxp->c1max = c1max = c1;
         351               0 :             goto have_c1max;
         352                 :           }
         353                 :       }
         354                 :  have_c1max:
         355               0 :   if (c2max > c2min)
         356               0 :     for (c2 = c2min; c2 <= c2max; c2++)
         357               0 :       for (c0 = c0min; c0 <= c0max; c0++) {
         358               0 :         histp = & histogram[c0][c1min][c2];
         359               0 :         for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
         360               0 :           if (*histp != 0) {
         361               0 :             boxp->c2min = c2min = c2;
         362               0 :             goto have_c2min;
         363                 :           }
         364                 :       }
         365                 :  have_c2min:
         366               0 :   if (c2max > c2min)
         367               0 :     for (c2 = c2max; c2 >= c2min; c2--)
         368               0 :       for (c0 = c0min; c0 <= c0max; c0++) {
         369               0 :         histp = & histogram[c0][c1min][c2];
         370               0 :         for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
         371               0 :           if (*histp != 0) {
         372               0 :             boxp->c2max = c2max = c2;
         373               0 :             goto have_c2max;
         374                 :           }
         375                 :       }
         376                 :  have_c2max:
         377                 :
         378                 :   /* Update box volume.
         379                 :    * We use 2-norm rather than real volume here; this biases the method
         380                 :    * against making long narrow boxes, and it has the side benefit that
         381                 :    * a box is splittable iff norm > 0.
         382                 :    * Since the differences are expressed in histogram-cell units,
         383                 :    * we have to shift back to JSAMPLE units to get consistent distances;
         384                 :    * after which, we scale according to the selected distance scale factors.
         385                 :    */
         386               0 :   dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE;
         387               0 :   dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE;
         388               0 :   dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE;
         389               0 :   boxp->volume = dist0*dist0 + dist1*dist1 + dist2*dist2;
         390                 :
         391                 :   /* Now scan remaining volume of box and compute population */
         392               0 :   ccount = 0;
         393               0 :   for (c0 = c0min; c0 <= c0max; c0++)
         394               0 :     for (c1 = c1min; c1 <= c1max; c1++) {
         395               0 :       histp = & histogram[c0][c1][c2min];
         396               0 :       for (c2 = c2min; c2 <= c2max; c2++, histp++)
         397               0 :         if (*histp != 0) {
         398               0 :           ccount++;
         399                 :         }
         400                 :     }
         401               0 :   boxp->colorcount = ccount;
         402               0 : }
         403                 :
         404                 :
         405                 : LOCAL(int)
         406               0 : median_cut (j_decompress_ptr cinfo, boxptr boxlist, int numboxes,
         407                 :             int desired_colors)
         408                 : /* Repeatedly select and split the largest box until we have enough boxes */
         409                 : {
         410                 :   int n,lb;
         411                 :   int c0,c1,c2,cmax;
         412                 :   register boxptr b1,b2;
         413                 :
         414               0 :   while (numboxes < desired_colors) {
         415                 :     /* Select box to split.
         416                 :      * Current algorithm: by population for first half, then by volume.
         417                 :      */
         418               0 :     if (numboxes*2 <= desired_colors) {
         419               0 :       b1 = find_biggest_color_pop(boxlist, numboxes);
         420                 :     } else {
         421               0 :       b1 = find_biggest_volume(boxlist, numboxes);
         422                 :     }
         423               0 :     if (b1 == NULL)             /* no splittable boxes left! */
         424               0 :       break;
         425               0 :     b2 = &boxlist[numboxes];        /* where new box will go */
         426                 :     /* Copy the color bounds to the new box. */
         427               0 :     b2->c0max = b1->c0max; b2->c1max = b1->c1max; b2->c2max = b1->c2max;
         428               0 :     b2->c0min = b1->c0min; b2->c1min = b1->c1min; b2->c2min = b1->c2min;
         429                 :     /* Choose which axis to split the box on.
         430                 :      * Current algorithm: longest scaled axis.
         431                 :      * See notes in update_box about scaling distances.
         432                 :      */
         433               0 :     c0 = ((b1->c0max - b1->c0min) << C0_SHIFT) * C0_SCALE;
         434               0 :     c1 = ((b1->c1max - b1->c1min) << C1_SHIFT) * C1_SCALE;
         435               0 :     c2 = ((b1->c2max - b1->c2min) << C2_SHIFT) * C2_SCALE;
         436                 :     /* We want to break any ties in favor of green, then red, blue last.
         437                 :      * This code does the right thing for R,G,B or B,G,R color orders only.
         438                 :      */
         439               0 :     if (rgb_red[cinfo->out_color_space] == 0) {
         440               0 :       cmax = c1; n = 1;
         441               0 :       if (c0 > cmax) { cmax = c0; n = 0; }
         442               0 :       if (c2 > cmax) { n = 2; }
         443                 :     }
         444                 :     else {
         445               0 :       cmax = c1; n = 1;
         446               0 :       if (c2 > cmax) { cmax = c2; n = 2; }
         447               0 :       if (c0 > cmax) { n = 0; }
         448                 :     }
         449                 :     /* Choose split point along selected axis, and update box bounds.
         450                 :      * Current algorithm: split at halfway point.
         451                 :      * (Since the box has been shrunk to minimum volume,
         452                 :      * any split will produce two nonempty subboxes.)
         453                 :      * Note that lb value is max for lower box, so must be < old max.
         454                 :      */
         455               0 :     switch (n) {
         456                 :     case 0:
         457               0 :       lb = (b1->c0max + b1->c0min) / 2;
         458               0 :       b1->c0max = lb;
         459               0 :       b2->c0min = lb+1;
         460               0 :       break;
         461                 :     case 1:
         462               0 :       lb = (b1->c1max + b1->c1min) / 2;
         463               0 :       b1->c1max = lb;
         464               0 :       b2->c1min = lb+1;
         465               0 :       break;
         466                 :     case 2:
         467               0 :       lb = (b1->c2max + b1->c2min) / 2;
         468               0 :       b1->c2max = lb;
         469               0 :       b2->c2min = lb+1;
         470               0 :       break;
         471                 :     }
         472                 :     /* Update stats for boxes */
         473               0 :     update_box(cinfo, b1);
         474               0 :     update_box(cinfo, b2);
         475               0 :     numboxes++;
         476                 :   }
         477               0 :   return numboxes;
         478                 : }
         479                 :
         480                 :
         481                 : LOCAL(void)
         482               0 : compute_color (j_decompress_ptr cinfo, boxptr boxp, int icolor)
         483                 : /* Compute representative color for a box, put it in colormap[icolor] */
         484                 : {
         485                 :   /* Current algorithm: mean weighted by pixels (not colors) */
         486                 :   /* Note it is important to get the rounding correct! */
         487               0 :   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
         488               0 :   hist3d histogram = cquantize->histogram;
         489                 :   histptr histp;
         490                 :   int c0,c1,c2;
         491                 :   int c0min,c0max,c1min,c1max,c2min,c2max;
         492                 :   long count;
         493               0 :   long total = 0;
         494               0 :   long c0total = 0;
         495               0 :   long c1total = 0;
         496               0 :   long c2total = 0;
         497                 :
         498               0 :   c0min = boxp->c0min;  c0max = boxp->c0max;
         499               0 :   c1min = boxp->c1min;  c1max = boxp->c1max;
         500               0 :   c2min = boxp->c2min;  c2max = boxp->c2max;
         501                 :
         502               0 :   for (c0 = c0min; c0 <= c0max; c0++)
         503               0 :     for (c1 = c1min; c1 <= c1max; c1++) {
         504               0 :       histp = & histogram[c0][c1][c2min];
         505               0 :       for (c2 = c2min; c2 <= c2max; c2++) {
         506               0 :         if ((count = *histp++) != 0) {
         507               0 :           total += count;
         508               0 :           c0total += ((c0 << C0_SHIFT) + ((1<<C0_SHIFT)>>1)) * count;
         509               0 :           c1total += ((c1 << C1_SHIFT) + ((1<<C1_SHIFT)>>1)) * count;
         510               0 :           c2total += ((c2 << C2_SHIFT) + ((1<<C2_SHIFT)>>1)) * count;
         511                 :         }
         512                 :       }
         513                 :     }
         514                 :
         515               0 :   cinfo->colormap[0][icolor] = (JSAMPLE) ((c0total + (total>>1)) / total);
         516               0 :   cinfo->colormap[1][icolor] = (JSAMPLE) ((c1total + (total>>1)) / total);
         517               0 :   cinfo->colormap[2][icolor] = (JSAMPLE) ((c2total + (total>>1)) / total);
         518               0 : }
         519                 :
         520                 :
         521                 : LOCAL(void)
         522               0 : select_colors (j_decompress_ptr cinfo, int desired_colors)
         523                 : /* Master routine for color selection */
         524                 : {
         525                 :   boxptr boxlist;
         526                 :   int numboxes;
         527                 :   int i;
         528                 :
         529                 :   /* Allocate workspace for box list */
         530               0 :   boxlist = (boxptr) (*cinfo->mem->alloc_small)
         531               0 :     ((j_common_ptr) cinfo, JPOOL_IMAGE, desired_colors * SIZEOF(box));
         532                 :   /* Initialize one box containing whole space */
         533               0 :   numboxes = 1;
         534               0 :   boxlist[0].c0min = 0;
         535               0 :   boxlist[0].c0max = MAXJSAMPLE >> C0_SHIFT;
         536               0 :   boxlist[0].c1min = 0;
         537               0 :   boxlist[0].c1max = MAXJSAMPLE >> C1_SHIFT;
         538               0 :   boxlist[0].c2min = 0;
         539               0 :   boxlist[0].c2max = MAXJSAMPLE >> C2_SHIFT;
         540                 :   /* Shrink it to actually-used volume and set its statistics */
         541               0 :   update_box(cinfo, & boxlist[0]);
         542                 :   /* Perform median-cut to produce final box list */
         543               0 :   numboxes = median_cut(cinfo, boxlist, numboxes, desired_colors);
         544                 :   /* Compute the representative color for each box, fill colormap */
         545               0 :   for (i = 0; i < numboxes; i++)
         546               0 :     compute_color(cinfo, & boxlist[i], i);
         547               0 :   cinfo->actual_number_of_colors = numboxes;
         548               0 :   TRACEMS1(cinfo, 1, JTRC_QUANT_SELECTED, numboxes);
         549               0 : }
         550                 :
         551                 :
         552                 : /*
         553                 :  * These routines are concerned with the time-critical task of mapping input
         554                 :  * colors to the nearest color in the selected colormap.
         555                 :  *
         556                 :  * We re-use the histogram space as an "inverse color map", essentially a
         557                 :  * cache for the results of nearest-color searches.  All colors within a
         558                 :  * histogram cell will be mapped to the same colormap entry, namely the one
         559                 :  * closest to the cell's center.  This may not be quite the closest entry to
         560                 :  * the actual input color, but it's almost as good.  A zero in the cache
         561                 :  * indicates we haven't found the nearest color for that cell yet; the array
         562                 :  * is cleared to zeroes before starting the mapping pass.  When we find the
         563                 :  * nearest color for a cell, its colormap index plus one is recorded in the
         564                 :  * cache for future use.  The pass2 scanning routines call fill_inverse_cmap
         565                 :  * when they need to use an unfilled entry in the cache.
         566                 :  *
         567                 :  * Our method of efficiently finding nearest colors is based on the "locally
         568                 :  * sorted search" idea described by Heckbert and on the incremental distance
         569                 :  * calculation described by Spencer W. Thomas in chapter III.1 of Graphics
         570                 :  * Gems II (James Arvo, ed.  Academic Press, 1991).  Thomas points out that
         571                 :  * the distances from a given colormap entry to each cell of the histogram can
         572                 :  * be computed quickly using an incremental method: the differences between
         573                 :  * distances to adjacent cells themselves differ by a constant.  This allows a
         574                 :  * fairly fast implementation of the "brute force" approach of computing the
         575                 :  * distance from every colormap entry to every histogram cell.  Unfortunately,
         576                 :  * it needs a work array to hold the best-distance-so-far for each histogram
         577                 :  * cell (because the inner loop has to be over cells, not colormap entries).
         578                 :  * The work array elements have to be INT32s, so the work array would need
         579                 :  * 256Kb at our recommended precision.  This is not feasible in DOS machines.
         580                 :  *
         581                 :  * To get around these problems, we apply Thomas' method to compute the
         582                 :  * nearest colors for only the cells within a small subbox of the histogram.
         583                 :  * The work array need be only as big as the subbox, so the memory usage
         584                 :  * problem is solved.  Furthermore, we need not fill subboxes that are never
         585                 :  * referenced in pass2; many images use only part of the color gamut, so a
         586                 :  * fair amount of work is saved.  An additional advantage of this
         587                 :  * approach is that we can apply Heckbert's locality criterion to quickly
         588                 :  * eliminate colormap entries that are far away from the subbox; typically
         589                 :  * three-fourths of the colormap entries are rejected by Heckbert's criterion,
         590                 :  * and we need not compute their distances to individual cells in the subbox.
         591                 :  * The speed of this approach is heavily influenced by the subbox size: too
         592                 :  * small means too much overhead, too big loses because Heckbert's criterion
         593                 :  * can't eliminate as many colormap entries.  Empirically the best subbox
         594                 :  * size seems to be about 1/512th of the histogram (1/8th in each direction).
         595                 :  *
         596                 :  * Thomas' article also describes a refined method which is asymptotically
         597                 :  * faster than the brute-force method, but it is also far more complex and
         598                 :  * cannot efficiently be applied to small subboxes.  It is therefore not
         599                 :  * useful for programs intended to be portable to DOS machines.  On machines
         600                 :  * with plenty of memory, filling the whole histogram in one shot with Thomas'
         601                 :  * refined method might be faster than the present code --- but then again,
         602                 :  * it might not be any faster, and it's certainly more complicated.
         603                 :  */
         604                 :
         605                 :
         606                 : /* log2(histogram cells in update box) for each axis; this can be adjusted */
         607                 : #define BOX_C0_LOG  (HIST_C0_BITS-3)
         608                 : #define BOX_C1_LOG  (HIST_C1_BITS-3)
         609                 : #define BOX_C2_LOG  (HIST_C2_BITS-3)
         610                 :
         611                 : #define BOX_C0_ELEMS  (1<<BOX_C0_LOG) /* # of hist cells in update box */
         612                 : #define BOX_C1_ELEMS  (1<<BOX_C1_LOG)
         613                 : #define BOX_C2_ELEMS  (1<<BOX_C2_LOG)
         614                 :
         615                 : #define BOX_C0_SHIFT  (C0_SHIFT + BOX_C0_LOG)
         616                 : #define BOX_C1_SHIFT  (C1_SHIFT + BOX_C1_LOG)
         617                 : #define BOX_C2_SHIFT  (C2_SHIFT + BOX_C2_LOG)
         618                 :
         619                 :
         620                 : /*
         621                 :  * The next three routines implement inverse colormap filling.  They could
         622                 :  * all be folded into one big routine, but splitting them up this way saves
         623                 :  * some stack space (the mindist[] and bestdist[] arrays need not coexist)
         624                 :  * and may allow some compilers to produce better code by registerizing more
         625                 :  * inner-loop variables.
         626                 :  */
         627                 :
         628                 : LOCAL(int)
         629               0 : find_nearby_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
         630                 :                     JSAMPLE colorlist[])
         631                 : /* Locate the colormap entries close enough to an update box to be candidates
         632                 :  * for the nearest entry to some cell(s) in the update box.  The update box
         633                 :  * is specified by the center coordinates of its first cell.  The number of
         634                 :  * candidate colormap entries is returned, and their colormap indexes are
         635                 :  * placed in colorlist[].
         636                 :  * This routine uses Heckbert's "locally sorted search" criterion to select
         637                 :  * the colors that need further consideration.
         638                 :  */
         639                 : {
         640               0 :   int numcolors = cinfo->actual_number_of_colors;
         641                 :   int maxc0, maxc1, maxc2;
         642                 :   int centerc0, centerc1, centerc2;
         643                 :   int i, x, ncolors;
         644                 :   INT32 minmaxdist, min_dist, max_dist, tdist;
         645                 :   INT32 mindist[MAXNUMCOLORS];  /* min distance to colormap entry i */
         646                 :
         647                 :   /* Compute true coordinates of update box's upper corner and center.
         648                 :    * Actually we compute the coordinates of the center of the upper-corner
         649                 :    * histogram cell, which are the upper bounds of the volume we care about.
         650                 :    * Note that since ">>" rounds down, the "center" values may be closer to
         651                 :    * min than to max; hence comparisons to them must be "<=", not "<".
         652                 :    */
         653               0 :   maxc0 = minc0 + ((1 << BOX_C0_SHIFT) - (1 << C0_SHIFT));
         654               0 :   centerc0 = (minc0 + maxc0) >> 1;
         655               0 :   maxc1 = minc1 + ((1 << BOX_C1_SHIFT) - (1 << C1_SHIFT));
         656               0 :   centerc1 = (minc1 + maxc1) >> 1;
         657               0 :   maxc2 = minc2 + ((1 << BOX_C2_SHIFT) - (1 << C2_SHIFT));
         658               0 :   centerc2 = (minc2 + maxc2) >> 1;
         659                 :
         660                 :   /* For each color in colormap, find:
         661                 :    *  1. its minimum squared-distance to any point in the update box
         662                 :    *     (zero if color is within update box);
         663                 :    *  2. its maximum squared-distance to any point in the update box.
         664                 :    * Both of these can be found by considering only the corners of the box.
         665                 :    * We save the minimum distance for each color in mindist[];
         666                 :    * only the smallest maximum distance is of interest.
         667                 :    */
         668               0 :   minmaxdist = 0x7FFFFFFFL;
         669                 :
         670               0 :   for (i = 0; i < numcolors; i++) {
         671                 :     /* We compute the squared-c0-distance term, then add in the other two. */
         672               0 :     x = GETJSAMPLE(cinfo->colormap[0][i]);
         673               0 :     if (x < minc0) {
         674               0 :       tdist = (x - minc0) * C0_SCALE;
         675               0 :       min_dist = tdist*tdist;
         676               0 :       tdist = (x - maxc0) * C0_SCALE;
         677               0 :       max_dist = tdist*tdist;
         678               0 :     } else if (x > maxc0) {
         679               0 :       tdist = (x - maxc0) * C0_SCALE;
         680               0 :       min_dist = tdist*tdist;
         681               0 :       tdist = (x - minc0) * C0_SCALE;
         682               0 :       max_dist = tdist*tdist;
         683                 :     } else {
         684                 :       /* within cell range so no contribution to min_dist */
         685               0 :       min_dist = 0;
         686               0 :       if (x <= centerc0) {
         687               0 :         tdist = (x - maxc0) * C0_SCALE;
         688               0 :         max_dist = tdist*tdist;
         689                 :       } else {
         690               0 :         tdist = (x - minc0) * C0_SCALE;
         691               0 :         max_dist = tdist*tdist;
         692                 :       }
         693                 :     }
         694                 :
         695               0 :     x = GETJSAMPLE(cinfo->colormap[1][i]);
         696               0 :     if (x < minc1) {
         697               0 :       tdist = (x - minc1) * C1_SCALE;
         698               0 :       min_dist += tdist*tdist;
         699               0 :       tdist = (x - maxc1) * C1_SCALE;
         700               0 :       max_dist += tdist*tdist;
         701               0 :     } else if (x > maxc1) {
         702               0 :       tdist = (x - maxc1) * C1_SCALE;
         703               0 :       min_dist += tdist*tdist;
         704               0 :       tdist = (x - minc1) * C1_SCALE;
         705               0 :       max_dist += tdist*tdist;
         706                 :     } else {
         707                 :       /* within cell range so no contribution to min_dist */
         708               0 :       if (x <= centerc1) {
         709               0 :         tdist = (x - maxc1) * C1_SCALE;
         710               0 :         max_dist += tdist*tdist;
         711                 :       } else {
         712               0 :         tdist = (x - minc1) * C1_SCALE;
         713               0 :         max_dist += tdist*tdist;
         714                 :       }
         715                 :     }
         716                 :
         717               0 :     x = GETJSAMPLE(cinfo->colormap[2][i]);
         718               0 :     if (x < minc2) {
         719               0 :       tdist = (x - minc2) * C2_SCALE;
         720               0 :       min_dist += tdist*tdist;
         721               0 :       tdist = (x - maxc2) * C2_SCALE;
         722               0 :       max_dist += tdist*tdist;
         723               0 :     } else if (x > maxc2) {
         724               0 :       tdist = (x - maxc2) * C2_SCALE;
         725               0 :       min_dist += tdist*tdist;
         726               0 :       tdist = (x - minc2) * C2_SCALE;
         727               0 :       max_dist += tdist*tdist;
         728                 :     } else {
         729                 :       /* within cell range so no contribution to min_dist */
         730               0 :       if (x <= centerc2) {
         731               0 :         tdist = (x - maxc2) * C2_SCALE;
         732               0 :         max_dist += tdist*tdist;
         733                 :       } else {
         734               0 :         tdist = (x - minc2) * C2_SCALE;
         735               0 :         max_dist += tdist*tdist;
         736                 :       }
         737                 :     }
         738                 :
         739               0 :     mindist[i] = min_dist;      /* save away the results */
         740               0 :     if (max_dist < minmaxdist)
         741               0 :       minmaxdist = max_dist;
         742                 :   }
         743                 :
         744                 :   /* Now we know that no cell in the update box is more than minmaxdist
         745                 :    * away from some colormap entry.  Therefore, only colors that are
         746                 :    * within minmaxdist of some part of the box need be considered.
         747                 :    */
         748               0 :   ncolors = 0;
         749               0 :   for (i = 0; i < numcolors; i++) {
         750               0 :     if (mindist[i] <= minmaxdist)
         751               0 :       colorlist[ncolors++] = (JSAMPLE) i;
         752                 :   }
         753               0 :   return ncolors;
         754                 : }
         755                 :
         756                 :
         757                 : LOCAL(void)
         758               0 : find_best_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
         759                 :                   int numcolors, JSAMPLE colorlist[], JSAMPLE bestcolor[])
         760                 : /* Find the closest colormap entry for each cell in the update box,
         761                 :  * given the list of candidate colors prepared by find_nearby_colors.
         762                 :  * Return the indexes of the closest entries in the bestcolor[] array.
         763                 :  * This routine uses Thomas' incremental distance calculation method to
         764                 :  * find the distance from a colormap entry to successive cells in the box.
         765                 :  */
         766                 : {
         767                 :   int ic0, ic1, ic2;
         768                 :   int i, icolor;
         769                 :   register INT32 * bptr;        /* pointer into bestdist[] array */
         770                 :   JSAMPLE * cptr;               /* pointer into bestcolor[] array */
         771                 :   INT32 dist0, dist1;           /* initial distance values */
         772                 :   register INT32 dist2;         /* current distance in inner loop */
         773                 :   INT32 xx0, xx1;               /* distance increments */
         774                 :   register INT32 xx2;
         775                 :   INT32 inc0, inc1, inc2;       /* initial values for increments */
         776                 :   /* This array holds the distance to the nearest-so-far color for each cell */
         777                 :   INT32 bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
         778                 :
         779                 :   /* Initialize best-distance for each cell of the update box */
         780               0 :   bptr = bestdist;
         781               0 :   for (i = BOX_C0_ELEMS*BOX_C1_ELEMS*BOX_C2_ELEMS-1; i >= 0; i--)
         782               0 :     *bptr++ = 0x7FFFFFFFL;
         783                 :
         784                 :   /* For each color selected by find_nearby_colors,
         785                 :    * compute its distance to the center of each cell in the box.
         786                 :    * If that's less than best-so-far, update best distance and color number.
         787                 :    */
         788                 :
         789                 :   /* Nominal steps between cell centers ("x" in Thomas article) */
         790                 : #define STEP_C0  ((1 << C0_SHIFT) * C0_SCALE)
         791                 : #define STEP_C1  ((1 << C1_SHIFT) * C1_SCALE)
         792                 : #define STEP_C2  ((1 << C2_SHIFT) * C2_SCALE)
         793                 :
         794               0 :   for (i = 0; i < numcolors; i++) {
         795               0 :     icolor = GETJSAMPLE(colorlist[i]);
         796                 :     /* Compute (square of) distance from minc0/c1/c2 to this color */
         797               0 :     inc0 = (minc0 - GETJSAMPLE(cinfo->colormap[0][icolor])) * C0_SCALE;
         798               0 :     dist0 = inc0*inc0;
         799               0 :     inc1 = (minc1 - GETJSAMPLE(cinfo->colormap[1][icolor])) * C1_SCALE;
         800               0 :     dist0 += inc1*inc1;
         801               0 :     inc2 = (minc2 - GETJSAMPLE(cinfo->colormap[2][icolor])) * C2_SCALE;
         802               0 :     dist0 += inc2*inc2;
         803                 :     /* Form the initial difference increments */
         804               0 :     inc0 = inc0 * (2 * STEP_C0) + STEP_C0 * STEP_C0;
         805               0 :     inc1 = inc1 * (2 * STEP_C1) + STEP_C1 * STEP_C1;
         806               0 :     inc2 = inc2 * (2 * STEP_C2) + STEP_C2 * STEP_C2;
         807                 :     /* Now loop over all cells in box, updating distance per Thomas method */
         808               0 :     bptr = bestdist;
         809               0 :     cptr = bestcolor;
         810               0 :     xx0 = inc0;
         811               0 :     for (ic0 = BOX_C0_ELEMS-1; ic0 >= 0; ic0--) {
         812               0 :       dist1 = dist0;
         813               0 :       xx1 = inc1;
         814               0 :       for (ic1 = BOX_C1_ELEMS-1; ic1 >= 0; ic1--) {
         815               0 :         dist2 = dist1;
         816               0 :         xx2 = inc2;
         817               0 :         for (ic2 = BOX_C2_ELEMS-1; ic2 >= 0; ic2--) {
         818               0 :           if (dist2 < *bptr) {
         819               0 :             *bptr = dist2;
         820               0 :             *cptr = (JSAMPLE) icolor;
         821                 :           }
         822               0 :           dist2 += xx2;
         823               0 :           xx2 += 2 * STEP_C2 * STEP_C2;
         824               0 :           bptr++;
         825               0 :           cptr++;
         826                 :         }
         827               0 :         dist1 += xx1;
         828               0 :         xx1 += 2 * STEP_C1 * STEP_C1;
         829                 :       }
         830               0 :       dist0 += xx0;
         831               0 :       xx0 += 2 * STEP_C0 * STEP_C0;
         832                 :     }
         833                 :   }
         834               0 : }
         835                 :
         836                 :
         837                 : LOCAL(void)
         838               0 : fill_inverse_cmap (j_decompress_ptr cinfo, int c0, int c1, int c2)
         839                 : /* Fill the inverse-colormap entries in the update box that contains */
         840                 : /* histogram cell c0/c1/c2.  (Only that one cell MUST be filled, but */
         841                 : /* we can fill as many others as we wish.) */
         842                 : {
         843               0 :   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
         844               0 :   hist3d histogram = cquantize->histogram;
         845                 :   int minc0, minc1, minc2;      /* lower left corner of update box */
         846                 :   int ic0, ic1, ic2;
         847                 :   register JSAMPLE * cptr;      /* pointer into bestcolor[] array */
         848                 :   register histptr cachep;      /* pointer into main cache array */
         849                 :   /* This array lists the candidate colormap indexes. */
         850                 :   JSAMPLE colorlist[MAXNUMCOLORS];
         851                 :   int numcolors;                /* number of candidate colors */
         852                 :   /* This array holds the actually closest colormap index for each cell. */
         853                 :   JSAMPLE bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
         854                 :
         855                 :   /* Convert cell coordinates to update box ID */
         856               0 :   c0 >>= BOX_C0_LOG;
         857               0 :   c1 >>= BOX_C1_LOG;
         858               0 :   c2 >>= BOX_C2_LOG;
         859                 :
         860                 :   /* Compute true coordinates of update box's origin corner.
         861                 :    * Actually we compute the coordinates of the center of the corner
         862                 :    * histogram cell, which are the lower bounds of the volume we care about.
         863                 :    */
         864               0 :   minc0 = (c0 << BOX_C0_SHIFT) + ((1 << C0_SHIFT) >> 1);
         865               0 :   minc1 = (c1 << BOX_C1_SHIFT) + ((1 << C1_SHIFT) >> 1);
         866               0 :   minc2 = (c2 << BOX_C2_SHIFT) + ((1 << C2_SHIFT) >> 1);
         867                 :
         868                 :   /* Determine which colormap entries are close enough to be candidates
         869                 :    * for the nearest entry to some cell in the update box.
         870                 :    */
         871               0 :   numcolors = find_nearby_colors(cinfo, minc0, minc1, minc2, colorlist);
         872                 :
         873                 :   /* Determine the actually nearest colors. */
         874               0 :   find_best_colors(cinfo, minc0, minc1, minc2, numcolors, colorlist,
         875                 :                    bestcolor);
         876                 :
         877                 :   /* Save the best color numbers (plus 1) in the main cache array */
         878               0 :   c0 <<= BOX_C0_LOG;              /* convert ID back to base cell indexes */
         879               0 :   c1 <<= BOX_C1_LOG;
         880               0 :   c2 <<= BOX_C2_LOG;
         881               0 :   cptr = bestcolor;
         882               0 :   for (ic0 = 0; ic0 < BOX_C0_ELEMS; ic0++) {
         883               0 :     for (ic1 = 0; ic1 < BOX_C1_ELEMS; ic1++) {
         884               0 :       cachep = & histogram[c0+ic0][c1+ic1][c2];
         885               0 :       for (ic2 = 0; ic2 < BOX_C2_ELEMS; ic2++) {
         886               0 :         *cachep++ = (histcell) (GETJSAMPLE(*cptr++) + 1);
         887                 :       }
         888                 :     }
         889                 :   }
         890               0 : }
         891                 :
         892                 :
         893                 : /*
         894                 :  * Map some rows of pixels to the output colormapped representation.
         895                 :  */
         896                 :
         897                 : METHODDEF(void)
         898               0 : pass2_no_dither (j_decompress_ptr cinfo,
         899                 :                  JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)
         900                 : /* This version performs no dithering */
         901                 : {
         902               0 :   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
         903               0 :   hist3d histogram = cquantize->histogram;
         904                 :   register JSAMPROW inptr, outptr;
         905                 :   register histptr cachep;
         906                 :   register int c0, c1, c2;
         907                 :   int row;
         908                 :   JDIMENSION col;
         909               0 :   JDIMENSION width = cinfo->output_width;
         910                 :
         911               0 :   for (row = 0; row < num_rows; row++) {
         912               0 :     inptr = input_buf[row];
         913               0 :     outptr = output_buf[row];
         914               0 :     for (col = width; col > 0; col--) {
         915                 :       /* get pixel value and index into the cache */
         916               0 :       c0 = GETJSAMPLE(*inptr++) >> C0_SHIFT;
         917               0 :       c1 = GETJSAMPLE(*inptr++) >> C1_SHIFT;
         918               0 :       c2 = GETJSAMPLE(*inptr++) >> C2_SHIFT;
         919               0 :       cachep = & histogram[c0][c1][c2];
         920                 :       /* If we have not seen this color before, find nearest colormap entry */
         921                 :       /* and update the cache */
         922               0 :       if (*cachep == 0)
         923               0 :         fill_inverse_cmap(cinfo, c0,c1,c2);
         924                 :       /* Now emit the colormap index for this cell */
         925               0 :       *outptr++ = (JSAMPLE) (*cachep - 1);
         926                 :     }
         927                 :   }
         928               0 : }
         929                 :
         930                 :
         931                 : METHODDEF(void)
         932               0 : pass2_fs_dither (j_decompress_ptr cinfo,
         933                 :                  JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)
         934                 : /* This version performs Floyd-Steinberg dithering */
         935                 : {
         936               0 :   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
         937               0 :   hist3d histogram = cquantize->histogram;
         938                 :   register LOCFSERROR cur0, cur1, cur2; /* current error or pixel value */
         939                 :   LOCFSERROR belowerr0, belowerr1, belowerr2; /* error for pixel below cur */
         940                 :   LOCFSERROR bpreverr0, bpreverr1, bpreverr2; /* error for below/prev col */
         941                 :   register FSERRPTR errorptr;   /* => fserrors[] at column before current */
         942                 :   JSAMPROW inptr;               /* => current input pixel */
         943                 :   JSAMPROW outptr;              /* => current output pixel */
         944                 :   histptr cachep;
         945                 :   int dir;                      /* +1 or -1 depending on direction */
         946                 :   int dir3;                     /* 3*dir, for advancing inptr & errorptr */
         947                 :   int row;
         948                 :   JDIMENSION col;
         949               0 :   JDIMENSION width = cinfo->output_width;
         950               0 :   JSAMPLE *range_limit = cinfo->sample_range_limit;
         951               0 :   int *error_limit = cquantize->error_limiter;
         952               0 :   JSAMPROW colormap0 = cinfo->colormap[0];
         953               0 :   JSAMPROW colormap1 = cinfo->colormap[1];
         954               0 :   JSAMPROW colormap2 = cinfo->colormap[2];
         955                 :   SHIFT_TEMPS
         956                 :
         957               0 :   for (row = 0; row < num_rows; row++) {
         958               0 :     inptr = input_buf[row];
         959               0 :     outptr = output_buf[row];
         960               0 :     if (cquantize->on_odd_row) {
         961                 :       /* work right to left in this row */
         962               0 :       inptr += (width-1) * 3;   /* so point to rightmost pixel */
         963               0 :       outptr += width-1;
         964               0 :       dir = -1;
         965               0 :       dir3 = -3;
         966               0 :       errorptr = cquantize->fserrors + (width+1)*3; /* => entry after last column */
         967               0 :       cquantize->on_odd_row = FALSE; /* flip for next time */
         968                 :     } else {
         969                 :       /* work left to right in this row */
         970               0 :       dir = 1;
         971               0 :       dir3 = 3;
         972               0 :       errorptr = cquantize->fserrors; /* => entry before first real column */
         973               0 :       cquantize->on_odd_row = TRUE; /* flip for next time */
         974                 :     }
         975                 :     /* Preset error values: no error propagated to first pixel from left */
         976               0 :     cur0 = cur1 = cur2 = 0;
         977                 :     /* and no error propagated to row below yet */
         978               0 :     belowerr0 = belowerr1 = belowerr2 = 0;
         979               0 :     bpreverr0 = bpreverr1 = bpreverr2 = 0;
         980                 :
         981               0 :     for (col = width; col > 0; col--) {
         982                 :       /* curN holds the error propagated from the previous pixel on the
         983                 :        * current line.  Add the error propagated from the previous line
         984                 :        * to form the complete error correction term for this pixel, and
         985                 :        * round the error term (which is expressed * 16) to an integer.
         986                 :        * RIGHT_SHIFT rounds towards minus infinity, so adding 8 is correct
         987                 :        * for either sign of the error value.
         988                 :        * Note: errorptr points to *previous* column's array entry.
         989                 :        */
         990               0 :       cur0 = RIGHT_SHIFT(cur0 + errorptr[dir3+0] + 8, 4);
         991               0 :       cur1 = RIGHT_SHIFT(cur1 + errorptr[dir3+1] + 8, 4);
         992               0 :       cur2 = RIGHT_SHIFT(cur2 + errorptr[dir3+2] + 8, 4);
         993                 :       /* Limit the error using transfer function set by init_error_limit.
         994                 :        * See comments with init_error_limit for rationale.
         995                 :        */
         996               0 :       cur0 = error_limit[cur0];
         997               0 :       cur1 = error_limit[cur1];
         998               0 :       cur2 = error_limit[cur2];
         999                 :       /* Form pixel value + error, and range-limit to 0..MAXJSAMPLE.
        1000                 :        * The maximum error is +- MAXJSAMPLE (or less with error limiting);
        1001                 :        * this sets the required size of the range_limit array.
        1002                 :        */
        1003               0 :       cur0 += GETJSAMPLE(inptr[0]);
        1004               0 :       cur1 += GETJSAMPLE(inptr[1]);
        1005               0 :       cur2 += GETJSAMPLE(inptr[2]);
        1006               0 :       cur0 = GETJSAMPLE(range_limit[cur0]);
        1007               0 :       cur1 = GETJSAMPLE(range_limit[cur1]);
        1008               0 :       cur2 = GETJSAMPLE(range_limit[cur2]);
        1009                 :       /* Index into the cache with adjusted pixel value */
        1010               0 :       cachep = & histogram[cur0>>C0_SHIFT][cur1>>C1_SHIFT][cur2>>C2_SHIFT];
        1011                 :       /* If we have not seen this color before, find nearest colormap */
        1012                 :       /* entry and update the cache */
        1013               0 :       if (*cachep == 0)
        1014               0 :         fill_inverse_cmap(cinfo, cur0>>C0_SHIFT,cur1>>C1_SHIFT,cur2>>C2_SHIFT);
        1015                 :       /* Now emit the colormap index for this cell */
        1016               0 :       { register int pixcode = *cachep - 1;
        1017               0 :         *outptr = (JSAMPLE) pixcode;
        1018                 :         /* Compute representation error for this pixel */
        1019               0 :         cur0 -= GETJSAMPLE(colormap0[pixcode]);
        1020               0 :         cur1 -= GETJSAMPLE(colormap1[pixcode]);
        1021               0 :         cur2 -= GETJSAMPLE(colormap2[pixcode]);
        1022                 :       }
        1023                 :       /* Compute error fractions to be propagated to adjacent pixels.
        1024                 :        * Add these into the running sums, and simultaneously shift the
        1025                 :        * next-line error sums left by 1 column.
        1026                 :        */
        1027                 :       { register LOCFSERROR bnexterr, delta;
        1028                 :
        1029               0 :         bnexterr = cur0;        /* Process component 0 */
        1030               0 :         delta = cur0 * 2;
        1031               0 :         cur0 += delta;          /* form error * 3 */
        1032               0 :         errorptr[0] = (FSERROR) (bpreverr0 + cur0);
        1033               0 :         cur0 += delta;          /* form error * 5 */
        1034               0 :         bpreverr0 = belowerr0 + cur0;
        1035               0 :         belowerr0 = bnexterr;
        1036               0 :         cur0 += delta;          /* form error * 7 */
        1037               0 :         bnexterr = cur1;        /* Process component 1 */
        1038               0 :         delta = cur1 * 2;
        1039               0 :         cur1 += delta;          /* form error * 3 */
        1040               0 :         errorptr[1] = (FSERROR) (bpreverr1 + cur1);
        1041               0 :         cur1 += delta;          /* form error * 5 */
        1042               0 :         bpreverr1 = belowerr1 + cur1;
        1043               0 :         belowerr1 = bnexterr;
        1044               0 :         cur1 += delta;          /* form error * 7 */
        1045               0 :         bnexterr = cur2;        /* Process component 2 */
        1046               0 :         delta = cur2 * 2;
        1047               0 :         cur2 += delta;          /* form error * 3 */
        1048               0 :         errorptr[2] = (FSERROR) (bpreverr2 + cur2);
        1049               0 :         cur2 += delta;          /* form error * 5 */
        1050               0 :         bpreverr2 = belowerr2 + cur2;
        1051               0 :         belowerr2 = bnexterr;
        1052               0 :         cur2 += delta;          /* form error * 7 */
        1053                 :       }
        1054                 :       /* At this point curN contains the 7/16 error value to be propagated
        1055                 :        * to the next pixel on the current line, and all the errors for the
        1056                 :        * next line have been shifted over.  We are therefore ready to move on.
        1057                 :        */
        1058               0 :       inptr += dir3;            /* Advance pixel pointers to next column */
        1059               0 :       outptr += dir;
        1060               0 :       errorptr += dir3;         /* advance errorptr to current column */
        1061                 :     }
        1062                 :     /* Post-loop cleanup: we must unload the final error values into the
        1063                 :      * final fserrors[] entry.  Note we need not unload belowerrN because
        1064                 :      * it is for the dummy column before or after the actual array.
        1065                 :      */
        1066               0 :     errorptr[0] = (FSERROR) bpreverr0; /* unload prev errs into array */
        1067               0 :     errorptr[1] = (FSERROR) bpreverr1;
        1068               0 :     errorptr[2] = (FSERROR) bpreverr2;
        1069                 :   }
        1070               0 : }
        1071                 :
        1072                 :
        1073                 : /*
        1074                 :  * Initialize the error-limiting transfer function (lookup table).
        1075                 :  * The raw F-S error computation can potentially compute error values of up to
        1076                 :  * +- MAXJSAMPLE.  But we want the maximum correction applied to a pixel to be
        1077                 :  * much less, otherwise obviously wrong pixels will be created.  (Typical
        1078                 :  * effects include weird fringes at color-area boundaries, isolated bright
        1079                 :  * pixels in a dark area, etc.)  The standard advice for avoiding this problem
        1080                 :  * is to ensure that the "corners" of the color cube are allocated as output
        1081                 :  * colors; then repeated errors in the same direction cannot cause cascading
        1082                 :  * error buildup.  However, that only prevents the error from getting
        1083                 :  * completely out of hand; Aaron Giles reports that error limiting improves
        1084                 :  * the results even with corner colors allocated.
        1085                 :  * A simple clamping of the error values to about +- MAXJSAMPLE/8 works pretty
        1086                 :  * well, but the smoother transfer function used below is even better.  Thanks
        1087                 :  * to Aaron Giles for this idea.
        1088                 :  */
        1089                 :
        1090                 : LOCAL(void)
        1091               0 : init_error_limit (j_decompress_ptr cinfo)
        1092                 : /* Allocate and fill in the error_limiter table */
        1093                 : {
        1094               0 :   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
        1095                 :   int * table;
        1096                 :   int in, out;
        1097                 :
        1098               0 :   table = (int *) (*cinfo->mem->alloc_small)
        1099               0 :     ((j_common_ptr) cinfo, JPOOL_IMAGE, (MAXJSAMPLE*2+1) * SIZEOF(int));
        1100               0 :   table += MAXJSAMPLE;          /* so can index -MAXJSAMPLE .. +MAXJSAMPLE */
        1101               0 :   cquantize->error_limiter = table;
        1102                 :
        1103                 : #define STEPSIZE ((MAXJSAMPLE+1)/16)
        1104                 :   /* Map errors 1:1 up to +- MAXJSAMPLE/16 */
        1105               0 :   out = 0;
        1106               0 :   for (in = 0; in < STEPSIZE; in++, out++) {
        1107               0 :     table[in] = out; table[-in] = -out;
        1108                 :   }
        1109                 :   /* Map errors 1:2 up to +- 3*MAXJSAMPLE/16 */
        1110               0 :   for (; in < STEPSIZE*3; in++, out += (in&1) ? 0 : 1) {
        1111               0 :     table[in] = out; table[-in] = -out;
        1112                 :   }
        1113                 :   /* Clamp the rest to final out value (which is (MAXJSAMPLE+1)/8) */
        1114               0 :   for (; in <= MAXJSAMPLE; in++) {
        1115               0 :     table[in] = out; table[-in] = -out;
        1116                 :   }
        1117                 : #undef STEPSIZE
        1118               0 : }
        1119                 :
        1120                 :
        1121                 : /*
        1122                 :  * Finish up at the end of each pass.
        1123                 :  */
        1124                 :
        1125                 : METHODDEF(void)
        1126               0 : finish_pass1 (j_decompress_ptr cinfo)
        1127                 : {
        1128               0 :   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
        1129                 :
        1130                 :   /* Select the representative colors and fill in cinfo->colormap */
        1131               0 :   cinfo->colormap = cquantize->sv_colormap;
        1132               0 :   select_colors(cinfo, cquantize->desired);
        1133                 :   /* Force next pass to zero the color index table */
        1134               0 :   cquantize->needs_zeroed = TRUE;
        1135               0 : }
        1136                 :
        1137                 :
        1138                 : METHODDEF(void)
        1139               0 : finish_pass2 (j_decompress_ptr cinfo)
        1140                 : {
        1141                 :   /* no work */
        1142               0 : }
        1143                 :
        1144                 :
        1145                 : /*
        1146                 :  * Initialize for each processing pass.
        1147                 :  */
        1148                 :
        1149                 : METHODDEF(void)
        1150               0 : start_pass_2_quant (j_decompress_ptr cinfo, boolean is_pre_scan)
        1151                 : {
        1152               0 :   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
        1153               0 :   hist3d histogram = cquantize->histogram;
        1154                 :   int i;
        1155                 :
        1156                 :   /* Only F-S dithering or no dithering is supported. */
        1157                 :   /* If user asks for ordered dither, give him F-S. */
        1158               0 :   if (cinfo->dither_mode != JDITHER_NONE)
        1159               0 :     cinfo->dither_mode = JDITHER_FS;
        1160                 :
        1161               0 :   if (is_pre_scan) {
        1162                 :     /* Set up method pointers */
        1163               0 :     cquantize->pub.color_quantize = prescan_quantize;
        1164               0 :     cquantize->pub.finish_pass = finish_pass1;
        1165               0 :     cquantize->needs_zeroed = TRUE; /* Always zero histogram */
        1166                 :   } else {
        1167                 :     /* Set up method pointers */
        1168               0 :     if (cinfo->dither_mode == JDITHER_FS)
        1169               0 :       cquantize->pub.color_quantize = pass2_fs_dither;
        1170                 :     else
        1171               0 :       cquantize->pub.color_quantize = pass2_no_dither;
        1172               0 :     cquantize->pub.finish_pass = finish_pass2;
        1173                 :
        1174                 :     /* Make sure color count is acceptable */
        1175               0 :     i = cinfo->actual_number_of_colors;
        1176               0 :     if (i < 1)
        1177               0 :       ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 1);
        1178               0 :     if (i > MAXNUMCOLORS)
        1179               0 :       ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
        1180                 :
        1181               0 :     if (cinfo->dither_mode == JDITHER_FS) {
        1182               0 :       size_t arraysize = (size_t) ((cinfo->output_width + 2) *
        1183                 :                                    (3 * SIZEOF(FSERROR)));
        1184                 :       /* Allocate Floyd-Steinberg workspace if we didn't already. */
        1185               0 :       if (cquantize->fserrors == NULL)
        1186               0 :         cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)
        1187               0 :           ((j_common_ptr) cinfo, JPOOL_IMAGE, arraysize);
        1188                 :       /* Initialize the propagated errors to zero. */
        1189               0 :       jzero_far((void FAR *) cquantize->fserrors, arraysize);
        1190                 :       /* Make the error-limit table if we didn't already. */
        1191               0 :       if (cquantize->error_limiter == NULL)
        1192               0 :         init_error_limit(cinfo);
        1193               0 :       cquantize->on_odd_row = FALSE;
        1194                 :     }
        1195                 :
        1196                 :   }
        1197                 :   /* Zero the histogram or inverse color map, if necessary */
        1198               0 :   if (cquantize->needs_zeroed) {
        1199               0 :     for (i = 0; i < HIST_C0_ELEMS; i++) {
        1200               0 :       jzero_far((void FAR *) histogram[i],
        1201                 :                 HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));
        1202                 :     }
        1203               0 :     cquantize->needs_zeroed = FALSE;
        1204                 :   }
        1205               0 : }
        1206                 :
        1207                 :
        1208                 : /*
        1209                 :  * Switch to a new external colormap between output passes.
        1210                 :  */
        1211                 :
        1212                 : METHODDEF(void)
        1213               0 : new_color_map_2_quant (j_decompress_ptr cinfo)
        1214                 : {
        1215               0 :   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
        1216                 :
        1217                 :   /* Reset the inverse color map */
        1218               0 :   cquantize->needs_zeroed = TRUE;
        1219               0 : }
        1220                 :
        1221                 :
        1222                 : /*
        1223                 :  * Module initialization routine for 2-pass color quantization.
        1224                 :  */
        1225                 :
        1226                 : GLOBAL(void)
        1227               0 : jinit_2pass_quantizer (j_decompress_ptr cinfo)
        1228                 : {
        1229                 :   my_cquantize_ptr cquantize;
        1230                 :   int i;
        1231                 :
        1232               0 :   cquantize = (my_cquantize_ptr)
        1233               0 :     (*cinfo->mem->alloc_small) ((j_common_ptr) cinfo, JPOOL_IMAGE,
        1234                 :                                 SIZEOF(my_cquantizer));
        1235               0 :   cinfo->cquantize = (struct jpeg_color_quantizer *) cquantize;
        1236               0 :   cquantize->pub.start_pass = start_pass_2_quant;
        1237               0 :   cquantize->pub.new_color_map = new_color_map_2_quant;
        1238               0 :   cquantize->fserrors = NULL;        /* flag optional arrays not allocated */
        1239               0 :   cquantize->error_limiter = NULL;
        1240                 :
        1241                 :   /* Make sure jdmaster didn't give me a case I can't handle */
        1242               0 :   if (cinfo->out_color_components != 3)
        1243               0 :     ERREXIT(cinfo, JERR_NOTIMPL);
        1244                 :
        1245                 :   /* Allocate the histogram/inverse colormap storage */
        1246               0 :   cquantize->histogram = (hist3d) (*cinfo->mem->alloc_small)
        1247               0 :     ((j_common_ptr) cinfo, JPOOL_IMAGE, HIST_C0_ELEMS * SIZEOF(hist2d));
        1248               0 :   for (i = 0; i < HIST_C0_ELEMS; i++) {
        1249               0 :     cquantize->histogram[i] = (hist2d) (*cinfo->mem->alloc_large)
        1250               0 :       ((j_common_ptr) cinfo, JPOOL_IMAGE,
        1251                 :        HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));
        1252                 :   }
        1253               0 :   cquantize->needs_zeroed = TRUE; /* histogram is garbage now */
        1254                 :
        1255                 :   /* Allocate storage for the completed colormap, if required.
        1256                 :    * We do this now since it is FAR storage and may affect
        1257                 :    * the memory manager's space calculations.
        1258                 :    */
        1259               0 :   if (cinfo->enable_2pass_quant) {
        1260                 :     /* Make sure color count is acceptable */
        1261               0 :     int desired = cinfo->desired_number_of_colors;
        1262                 :     /* Lower bound on # of colors ... somewhat arbitrary as long as > 0 */
        1263               0 :     if (desired < 8)
        1264               0 :       ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 8);
        1265                 :     /* Make sure colormap indexes can be represented by JSAMPLEs */
        1266               0 :     if (desired > MAXNUMCOLORS)
        1267               0 :       ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
        1268               0 :     cquantize->sv_colormap = (*cinfo->mem->alloc_sarray)
        1269               0 :       ((j_common_ptr) cinfo,JPOOL_IMAGE, (JDIMENSION) desired, (JDIMENSION) 3);
        1270               0 :     cquantize->desired = desired;
        1271                 :   } else
        1272               0 :     cquantize->sv_colormap = NULL;
        1273                 :
        1274                 :   /* Only F-S dithering or no dithering is supported. */
        1275                 :   /* If user asks for ordered dither, give him F-S. */
        1276               0 :   if (cinfo->dither_mode != JDITHER_NONE)
        1277               0 :     cinfo->dither_mode = JDITHER_FS;
        1278                 :
        1279                 :   /* Allocate Floyd-Steinberg workspace if necessary.
        1280                 :    * This isn't really needed until pass 2, but again it is FAR storage.
        1281                 :    * Although we will cope with a later change in dither_mode,
        1282                 :    * we do not promise to honor max_memory_to_use if dither_mode changes.
        1283                 :    */
        1284               0 :   if (cinfo->dither_mode == JDITHER_FS) {
        1285               0 :     cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)
        1286               0 :       ((j_common_ptr) cinfo, JPOOL_IMAGE,
        1287               0 :        (size_t) ((cinfo->output_width + 2) * (3 * SIZEOF(FSERROR))));
        1288                 :     /* Might as well create the error-limiting table too. */
        1289               0 :     init_error_limit(cinfo);
        1290                 :   }
        1291               0 : }
        1292                 :
        1293                 : #endif /* QUANT_2PASS_SUPPORTED */

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