The Tor Browser: media/libjpeg/jquant2.c@b8a032363ba2 (annotated)

media/libjpeg/jquant2.c@b8a032363ba2 (annotated)

media/libjpeg/jquant2.c

Thu, 22 Jan 2015 13:21:57 +0100

author: Michael Schloh von Bennewitz <michael@schloh.com>
date: Thu, 22 Jan 2015 13:21:57 +0100
branch: TOR_BUG_9701
changeset 15: b8a032363ba2
permissions: -rw-r--r--

Incorporate requested changes from Mozilla in review:
https://bugzilla.mozilla.org/show_bug.cgi?id=1123480#c6

 /*
  * jquant2.c
  *
  * This file was part of the Independent JPEG Group's software:
  * Copyright (C) 1991-1996, Thomas G. Lane.
  * libjpeg-turbo Modifications:
  * Copyright (C) 2009, D. R. Commander.
  * For conditions of distribution and use, see the accompanying README file.
  *
  * This file contains 2-pass color quantization (color mapping) routines.
  * These routines provide selection of a custom color map for an image,
  * followed by mapping of the image to that color map, with optional
  * Floyd-Steinberg dithering.
  * It is also possible to use just the second pass to map to an arbitrary
  * externally-given color map.
  *
  * Note: ordered dithering is not supported, since there isn't any fast
  * way to compute intercolor distances; it's unclear that ordered dither's
  * fundamental assumptions even hold with an irregularly spaced color map.
  */
 #define JPEG_INTERNALS
 #include "jinclude.h"
 #include "jpeglib.h"
 #ifdef QUANT_2PASS_SUPPORTED
 /*
  * This module implements the well-known Heckbert paradigm for color
  * quantization.  Most of the ideas used here can be traced back to
  * Heckbert's seminal paper
  *   Heckbert, Paul.  "Color Image Quantization for Frame Buffer Display",
  *   Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304.
  *
  * In the first pass over the image, we accumulate a histogram showing the
  * usage count of each possible color.  To keep the histogram to a reasonable
  * size, we reduce the precision of the input; typical practice is to retain
  * 5 or 6 bits per color, so that 8 or 4 different input values are counted
  * in the same histogram cell.
  *
  * Next, the color-selection step begins with a box representing the whole
  * color space, and repeatedly splits the "largest" remaining box until we
  * have as many boxes as desired colors.  Then the mean color in each
  * remaining box becomes one of the possible output colors.
  *
  * The second pass over the image maps each input pixel to the closest output
  * color (optionally after applying a Floyd-Steinberg dithering correction).
  * This mapping is logically trivial, but making it go fast enough requires
  * considerable care.
  *
  * Heckbert-style quantizers vary a good deal in their policies for choosing
  * the "largest" box and deciding where to cut it.  The particular policies
  * used here have proved out well in experimental comparisons, but better ones
  * may yet be found.
  *
  * In earlier versions of the IJG code, this module quantized in YCbCr color
  * space, processing the raw upsampled data without a color conversion step.
  * This allowed the color conversion math to be done only once per colormap
  * entry, not once per pixel.  However, that optimization precluded other
  * useful optimizations (such as merging color conversion with upsampling)
  * and it also interfered with desired capabilities such as quantizing to an
  * externally-supplied colormap.  We have therefore abandoned that approach.
  * The present code works in the post-conversion color space, typically RGB.
  *
  * To improve the visual quality of the results, we actually work in scaled
  * RGB space, giving G distances more weight than R, and R in turn more than
  * B.  To do everything in integer math, we must use integer scale factors.
  * The 2/3/1 scale factors used here correspond loosely to the relative
  * weights of the colors in the NTSC grayscale equation.
  * If you want to use this code to quantize a non-RGB color space, you'll
  * probably need to change these scale factors.
  */
 #define R_SCALE 2		/* scale R distances by this much */
 #define G_SCALE 3		/* scale G distances by this much */
 #define B_SCALE 1		/* and B by this much */
 static const int c_scales[3]={R_SCALE, G_SCALE, B_SCALE};
 #define C0_SCALE c_scales[rgb_red[cinfo->out_color_space]]
 #define C1_SCALE c_scales[rgb_green[cinfo->out_color_space]]
 #define C2_SCALE c_scales[rgb_blue[cinfo->out_color_space]]
 /*
  * First we have the histogram data structure and routines for creating it.
  *
  * The number of bits of precision can be adjusted by changing these symbols.
  * We recommend keeping 6 bits for G and 5 each for R and B.
  * If you have plenty of memory and cycles, 6 bits all around gives marginally
  * better results; if you are short of memory, 5 bits all around will save
  * some space but degrade the results.
  * To maintain a fully accurate histogram, we'd need to allocate a "long"
  * (preferably unsigned long) for each cell.  In practice this is overkill;
  * we can get by with 16 bits per cell.  Few of the cell counts will overflow,
  * and clamping those that do overflow to the maximum value will give close-
  * enough results.  This reduces the recommended histogram size from 256Kb
  * to 128Kb, which is a useful savings on PC-class machines.
  * (In the second pass the histogram space is re-used for pixel mapping data;
  * in that capacity, each cell must be able to store zero to the number of
  * desired colors.  16 bits/cell is plenty for that too.)
  * Since the JPEG code is intended to run in small memory model on 80x86
  * machines, we can't just allocate the histogram in one chunk.  Instead
  * of a true 3-D array, we use a row of pointers to 2-D arrays.  Each
  * pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and
  * each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries.  Note that
  * on 80x86 machines, the pointer row is in near memory but the actual
  * arrays are in far memory (same arrangement as we use for image arrays).
  */
 #define MAXNUMCOLORS  (MAXJSAMPLE+1) /* maximum size of colormap */
 /* These will do the right thing for either R,G,B or B,G,R color order,
  * but you may not like the results for other color orders.
  */
 #define HIST_C0_BITS  5		/* bits of precision in R/B histogram */
 #define HIST_C1_BITS  6		/* bits of precision in G histogram */
 #define HIST_C2_BITS  5		/* bits of precision in B/R histogram */
 /* Number of elements along histogram axes. */
 #define HIST_C0_ELEMS  (1<<HIST_C0_BITS)
 #define HIST_C1_ELEMS  (1<<HIST_C1_BITS)
 #define HIST_C2_ELEMS  (1<<HIST_C2_BITS)
 /* These are the amounts to shift an input value to get a histogram index. */
 #define C0_SHIFT  (BITS_IN_JSAMPLE-HIST_C0_BITS)
 #define C1_SHIFT  (BITS_IN_JSAMPLE-HIST_C1_BITS)
 #define C2_SHIFT  (BITS_IN_JSAMPLE-HIST_C2_BITS)
 typedef UINT16 histcell;	/* histogram cell; prefer an unsigned type */
 typedef histcell FAR * histptr;	/* for pointers to histogram cells */
 typedef histcell hist1d[HIST_C2_ELEMS]; /* typedefs for the array */
 typedef hist1d FAR * hist2d;	/* type for the 2nd-level pointers */
 typedef hist2d * hist3d;	/* type for top-level pointer */
 /* Declarations for Floyd-Steinberg dithering.
  *
  * Errors are accumulated into the array fserrors[], at a resolution of
  * 1/16th of a pixel count.  The error at a given pixel is propagated
  * to its not-yet-processed neighbors using the standard F-S fractions,
  *		...	(here)	7/16
  *		3/16	5/16	1/16
  * We work left-to-right on even rows, right-to-left on odd rows.
  *
  * We can get away with a single array (holding one row's worth of errors)
  * by using it to store the current row's errors at pixel columns not yet
  * processed, but the next row's errors at columns already processed.  We
  * need only a few extra variables to hold the errors immediately around the
  * current column.  (If we are lucky, those variables are in registers, but
  * even if not, they're probably cheaper to access than array elements are.)
  *
  * The fserrors[] array has (#columns + 2) entries; the extra entry at
  * each end saves us from special-casing the first and last pixels.
  * Each entry is three values long, one value for each color component.
  *
  * Note: on a wide image, we might not have enough room in a PC's near data
  * segment to hold the error array; so it is allocated with alloc_large.
  */
 #if BITS_IN_JSAMPLE == 8
 typedef INT16 FSERROR;		/* 16 bits should be enough */
 typedef int LOCFSERROR;		/* use 'int' for calculation temps */
 #else
 typedef INT32 FSERROR;		/* may need more than 16 bits */
 typedef INT32 LOCFSERROR;	/* be sure calculation temps are big enough */
 #endif
 typedef FSERROR FAR *FSERRPTR;	/* pointer to error array (in FAR storage!) */
 /* Private subobject */
 typedef struct {
   struct jpeg_color_quantizer pub; /* public fields */
   /* Space for the eventually created colormap is stashed here */
   JSAMPARRAY sv_colormap;	/* colormap allocated at init time */
   int desired;			/* desired # of colors = size of colormap */
   /* Variables for accumulating image statistics */
   hist3d histogram;		/* pointer to the histogram */
   boolean needs_zeroed;		/* TRUE if next pass must zero histogram */
   /* Variables for Floyd-Steinberg dithering */
   FSERRPTR fserrors;		/* accumulated errors */
   boolean on_odd_row;		/* flag to remember which row we are on */
   int * error_limiter;		/* table for clamping the applied error */
 } my_cquantizer;
 typedef my_cquantizer * my_cquantize_ptr;
 /*
  * Prescan some rows of pixels.
  * In this module the prescan simply updates the histogram, which has been
  * initialized to zeroes by start_pass.
  * An output_buf parameter is required by the method signature, but no data
  * is actually output (in fact the buffer controller is probably passing a
  * NULL pointer).
  */
 METHODDEF(void)
 prescan_quantize (j_decompress_ptr cinfo, JSAMPARRAY input_buf,
 		  JSAMPARRAY output_buf, int num_rows)
 {
   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
   register JSAMPROW ptr;
   register histptr histp;
   register hist3d histogram = cquantize->histogram;
   int row;
   JDIMENSION col;
   JDIMENSION width = cinfo->output_width;
   for (row = 0; row < num_rows; row++) {
     ptr = input_buf[row];
     for (col = width; col > 0; col--) {
       /* get pixel value and index into the histogram */
       histp = & histogram[GETJSAMPLE(ptr[0]) >> C0_SHIFT]
 			 [GETJSAMPLE(ptr[1]) >> C1_SHIFT]
 			 [GETJSAMPLE(ptr[2]) >> C2_SHIFT];
       /* increment, check for overflow and undo increment if so. */
       if (++(*histp) <= 0)
 	(*histp)--;
       ptr += 3;
     }
   }
 }
 /*
  * Next we have the really interesting routines: selection of a colormap
  * given the completed histogram.
  * These routines work with a list of "boxes", each representing a rectangular
  * subset of the input color space (to histogram precision).
  */
 typedef struct {
   /* The bounds of the box (inclusive); expressed as histogram indexes */
   int c0min, c0max;
   int c1min, c1max;
   int c2min, c2max;
   /* The volume (actually 2-norm) of the box */
   INT32 volume;
   /* The number of nonzero histogram cells within this box */
   long colorcount;
 } box;
 typedef box * boxptr;
 LOCAL(boxptr)
 find_biggest_color_pop (boxptr boxlist, int numboxes)
 /* Find the splittable box with the largest color population */
 /* Returns NULL if no splittable boxes remain */
 {
   register boxptr boxp;
   register int i;
   register long maxc = 0;
   boxptr which = NULL;
   for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
     if (boxp->colorcount > maxc && boxp->volume > 0) {
       which = boxp;
       maxc = boxp->colorcount;
     }
   }
   return which;
 }
 LOCAL(boxptr)
 find_biggest_volume (boxptr boxlist, int numboxes)
 /* Find the splittable box with the largest (scaled) volume */
 /* Returns NULL if no splittable boxes remain */
 {
   register boxptr boxp;
   register int i;
   register INT32 maxv = 0;
   boxptr which = NULL;
   for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
     if (boxp->volume > maxv) {
       which = boxp;
       maxv = boxp->volume;
     }
   }
   return which;
 }
 LOCAL(void)
 update_box (j_decompress_ptr cinfo, boxptr boxp)
 /* Shrink the min/max bounds of a box to enclose only nonzero elements, */
 /* and recompute its volume and population */
 {
   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
   hist3d histogram = cquantize->histogram;
   histptr histp;
   int c0,c1,c2;
   int c0min,c0max,c1min,c1max,c2min,c2max;
   INT32 dist0,dist1,dist2;
   long ccount;
   c0min = boxp->c0min;  c0max = boxp->c0max;
   c1min = boxp->c1min;  c1max = boxp->c1max;
   c2min = boxp->c2min;  c2max = boxp->c2max;
   if (c0max > c0min)
     for (c0 = c0min; c0 <= c0max; c0++)
       for (c1 = c1min; c1 <= c1max; c1++) {
 	histp = & histogram[c0][c1][c2min];
 	for (c2 = c2min; c2 <= c2max; c2++)
 	  if (*histp++ != 0) {
 	    boxp->c0min = c0min = c0;
 	    goto have_c0min;
 	  }
       }
  have_c0min:
   if (c0max > c0min)
     for (c0 = c0max; c0 >= c0min; c0--)
       for (c1 = c1min; c1 <= c1max; c1++) {
 	histp = & histogram[c0][c1][c2min];
 	for (c2 = c2min; c2 <= c2max; c2++)
 	  if (*histp++ != 0) {
 	    boxp->c0max = c0max = c0;
 	    goto have_c0max;
 	  }
       }
  have_c0max:
   if (c1max > c1min)
     for (c1 = c1min; c1 <= c1max; c1++)
       for (c0 = c0min; c0 <= c0max; c0++) {
 	histp = & histogram[c0][c1][c2min];
 	for (c2 = c2min; c2 <= c2max; c2++)
 	  if (*histp++ != 0) {
 	    boxp->c1min = c1min = c1;
 	    goto have_c1min;
 	  }
       }
  have_c1min:
   if (c1max > c1min)
     for (c1 = c1max; c1 >= c1min; c1--)
       for (c0 = c0min; c0 <= c0max; c0++) {
 	histp = & histogram[c0][c1][c2min];
 	for (c2 = c2min; c2 <= c2max; c2++)
 	  if (*histp++ != 0) {
 	    boxp->c1max = c1max = c1;
 	    goto have_c1max;
 	  }
       }
  have_c1max:
   if (c2max > c2min)
     for (c2 = c2min; c2 <= c2max; c2++)
       for (c0 = c0min; c0 <= c0max; c0++) {
 	histp = & histogram[c0][c1min][c2];
 	for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
 	  if (*histp != 0) {
 	    boxp->c2min = c2min = c2;
 	    goto have_c2min;
 	  }
       }
  have_c2min:
   if (c2max > c2min)
     for (c2 = c2max; c2 >= c2min; c2--)
       for (c0 = c0min; c0 <= c0max; c0++) {
 	histp = & histogram[c0][c1min][c2];
 	for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
 	  if (*histp != 0) {
 	    boxp->c2max = c2max = c2;
 	    goto have_c2max;
 	  }
       }
  have_c2max:
   /* Update box volume.
    * We use 2-norm rather than real volume here; this biases the method
    * against making long narrow boxes, and it has the side benefit that
    * a box is splittable iff norm > 0.
    * Since the differences are expressed in histogram-cell units,
    * we have to shift back to JSAMPLE units to get consistent distances;
    * after which, we scale according to the selected distance scale factors.
    */
   dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE;
   dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE;
   dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE;
   boxp->volume = dist0*dist0 + dist1*dist1 + dist2*dist2;
   /* Now scan remaining volume of box and compute population */
   ccount = 0;
   for (c0 = c0min; c0 <= c0max; c0++)
     for (c1 = c1min; c1 <= c1max; c1++) {
       histp = & histogram[c0][c1][c2min];
       for (c2 = c2min; c2 <= c2max; c2++, histp++)
 	if (*histp != 0) {
 	  ccount++;
 	}
     }
   boxp->colorcount = ccount;
 }
 LOCAL(int)
 median_cut (j_decompress_ptr cinfo, boxptr boxlist, int numboxes,
 	    int desired_colors)
 /* Repeatedly select and split the largest box until we have enough boxes */
 {
   int n,lb;
   int c0,c1,c2,cmax;
   register boxptr b1,b2;
   while (numboxes < desired_colors) {
     /* Select box to split.
      * Current algorithm: by population for first half, then by volume.
      */
     if (numboxes*2 <= desired_colors) {
       b1 = find_biggest_color_pop(boxlist, numboxes);
     } else {
       b1 = find_biggest_volume(boxlist, numboxes);
     }
     if (b1 == NULL)		/* no splittable boxes left! */
       break;
     b2 = &boxlist[numboxes];	/* where new box will go */
     /* Copy the color bounds to the new box. */
     b2->c0max = b1->c0max; b2->c1max = b1->c1max; b2->c2max = b1->c2max;
     b2->c0min = b1->c0min; b2->c1min = b1->c1min; b2->c2min = b1->c2min;
     /* Choose which axis to split the box on.
      * Current algorithm: longest scaled axis.
      * See notes in update_box about scaling distances.
      */
     c0 = ((b1->c0max - b1->c0min) << C0_SHIFT) * C0_SCALE;
     c1 = ((b1->c1max - b1->c1min) << C1_SHIFT) * C1_SCALE;
     c2 = ((b1->c2max - b1->c2min) << C2_SHIFT) * C2_SCALE;
     /* We want to break any ties in favor of green, then red, blue last.
      * This code does the right thing for R,G,B or B,G,R color orders only.
      */
     if (rgb_red[cinfo->out_color_space] == 0) {
       cmax = c1; n = 1;
       if (c0 > cmax) { cmax = c0; n = 0; }
       if (c2 > cmax) { n = 2; }
     }
     else {
       cmax = c1; n = 1;
       if (c2 > cmax) { cmax = c2; n = 2; }
       if (c0 > cmax) { n = 0; }
     }
     /* Choose split point along selected axis, and update box bounds.
      * Current algorithm: split at halfway point.
      * (Since the box has been shrunk to minimum volume,
      * any split will produce two nonempty subboxes.)
      * Note that lb value is max for lower box, so must be < old max.
      */
     switch (n) {
     case 0:
       lb = (b1->c0max + b1->c0min) / 2;
       b1->c0max = lb;
       b2->c0min = lb+1;
       break;
     case 1:
       lb = (b1->c1max + b1->c1min) / 2;
       b1->c1max = lb;
       b2->c1min = lb+1;
       break;
     case 2:
       lb = (b1->c2max + b1->c2min) / 2;
       b1->c2max = lb;
       b2->c2min = lb+1;
       break;
     }
     /* Update stats for boxes */
     update_box(cinfo, b1);
     update_box(cinfo, b2);
     numboxes++;
   }
   return numboxes;
 }
 LOCAL(void)
 compute_color (j_decompress_ptr cinfo, boxptr boxp, int icolor)
 /* Compute representative color for a box, put it in colormap[icolor] */
 {
   /* Current algorithm: mean weighted by pixels (not colors) */
   /* Note it is important to get the rounding correct! */
   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
   hist3d histogram = cquantize->histogram;
   histptr histp;
   int c0,c1,c2;
   int c0min,c0max,c1min,c1max,c2min,c2max;
   long count;
   long total = 0;
   long c0total = 0;
   long c1total = 0;
   long c2total = 0;
   c0min = boxp->c0min;  c0max = boxp->c0max;
   c1min = boxp->c1min;  c1max = boxp->c1max;
   c2min = boxp->c2min;  c2max = boxp->c2max;
   for (c0 = c0min; c0 <= c0max; c0++)
     for (c1 = c1min; c1 <= c1max; c1++) {
       histp = & histogram[c0][c1][c2min];
       for (c2 = c2min; c2 <= c2max; c2++) {
 	if ((count = *histp++) != 0) {
 	  total += count;
 	  c0total += ((c0 << C0_SHIFT) + ((1<<C0_SHIFT)>>1)) * count;
 	  c1total += ((c1 << C1_SHIFT) + ((1<<C1_SHIFT)>>1)) * count;
 	  c2total += ((c2 << C2_SHIFT) + ((1<<C2_SHIFT)>>1)) * count;
 	}
       }
     }
   cinfo->colormap[0][icolor] = (JSAMPLE) ((c0total + (total>>1)) / total);
   cinfo->colormap[1][icolor] = (JSAMPLE) ((c1total + (total>>1)) / total);
   cinfo->colormap[2][icolor] = (JSAMPLE) ((c2total + (total>>1)) / total);
 }
 LOCAL(void)
 select_colors (j_decompress_ptr cinfo, int desired_colors)
 /* Master routine for color selection */
 {
   boxptr boxlist;
   int numboxes;
   int i;
   /* Allocate workspace for box list */
   boxlist = (boxptr) (*cinfo->mem->alloc_small)
     ((j_common_ptr) cinfo, JPOOL_IMAGE, desired_colors * SIZEOF(box));
   /* Initialize one box containing whole space */
   numboxes = 1;
   boxlist[0].c0min = 0;
   boxlist[0].c0max = MAXJSAMPLE >> C0_SHIFT;
   boxlist[0].c1min = 0;
   boxlist[0].c1max = MAXJSAMPLE >> C1_SHIFT;
   boxlist[0].c2min = 0;
   boxlist[0].c2max = MAXJSAMPLE >> C2_SHIFT;
   /* Shrink it to actually-used volume and set its statistics */
   update_box(cinfo, & boxlist[0]);
   /* Perform median-cut to produce final box list */
   numboxes = median_cut(cinfo, boxlist, numboxes, desired_colors);
   /* Compute the representative color for each box, fill colormap */
   for (i = 0; i < numboxes; i++)
     compute_color(cinfo, & boxlist[i], i);
   cinfo->actual_number_of_colors = numboxes;
   TRACEMS1(cinfo, 1, JTRC_QUANT_SELECTED, numboxes);
 }
 /*
  * These routines are concerned with the time-critical task of mapping input
  * colors to the nearest color in the selected colormap.
  *
  * We re-use the histogram space as an "inverse color map", essentially a
  * cache for the results of nearest-color searches.  All colors within a
  * histogram cell will be mapped to the same colormap entry, namely the one
  * closest to the cell's center.  This may not be quite the closest entry to
  * the actual input color, but it's almost as good.  A zero in the cache
  * indicates we haven't found the nearest color for that cell yet; the array
  * is cleared to zeroes before starting the mapping pass.  When we find the
  * nearest color for a cell, its colormap index plus one is recorded in the
  * cache for future use.  The pass2 scanning routines call fill_inverse_cmap
  * when they need to use an unfilled entry in the cache.
  *
  * Our method of efficiently finding nearest colors is based on the "locally
  * sorted search" idea described by Heckbert and on the incremental distance
  * calculation described by Spencer W. Thomas in chapter III.1 of Graphics
  * Gems II (James Arvo, ed.  Academic Press, 1991).  Thomas points out that
  * the distances from a given colormap entry to each cell of the histogram can
  * be computed quickly using an incremental method: the differences between
  * distances to adjacent cells themselves differ by a constant.  This allows a
  * fairly fast implementation of the "brute force" approach of computing the
  * distance from every colormap entry to every histogram cell.  Unfortunately,
  * it needs a work array to hold the best-distance-so-far for each histogram
  * cell (because the inner loop has to be over cells, not colormap entries).
  * The work array elements have to be INT32s, so the work array would need
  * 256Kb at our recommended precision.  This is not feasible in DOS machines.
  *
  * To get around these problems, we apply Thomas' method to compute the
  * nearest colors for only the cells within a small subbox of the histogram.
  * The work array need be only as big as the subbox, so the memory usage
  * problem is solved.  Furthermore, we need not fill subboxes that are never
  * referenced in pass2; many images use only part of the color gamut, so a
  * fair amount of work is saved.  An additional advantage of this
  * approach is that we can apply Heckbert's locality criterion to quickly
  * eliminate colormap entries that are far away from the subbox; typically
  * three-fourths of the colormap entries are rejected by Heckbert's criterion,
  * and we need not compute their distances to individual cells in the subbox.
  * The speed of this approach is heavily influenced by the subbox size: too
  * small means too much overhead, too big loses because Heckbert's criterion
  * can't eliminate as many colormap entries.  Empirically the best subbox
  * size seems to be about 1/512th of the histogram (1/8th in each direction).
  *
  * Thomas' article also describes a refined method which is asymptotically
  * faster than the brute-force method, but it is also far more complex and
  * cannot efficiently be applied to small subboxes.  It is therefore not
  * useful for programs intended to be portable to DOS machines.  On machines
  * with plenty of memory, filling the whole histogram in one shot with Thomas'
  * refined method might be faster than the present code --- but then again,
  * it might not be any faster, and it's certainly more complicated.
  */
 /* log2(histogram cells in update box) for each axis; this can be adjusted */
 #define BOX_C0_LOG  (HIST_C0_BITS-3)
 #define BOX_C1_LOG  (HIST_C1_BITS-3)
 #define BOX_C2_LOG  (HIST_C2_BITS-3)
 #define BOX_C0_ELEMS  (1<<BOX_C0_LOG) /* # of hist cells in update box */
 #define BOX_C1_ELEMS  (1<<BOX_C1_LOG)
 #define BOX_C2_ELEMS  (1<<BOX_C2_LOG)
 #define BOX_C0_SHIFT  (C0_SHIFT + BOX_C0_LOG)
 #define BOX_C1_SHIFT  (C1_SHIFT + BOX_C1_LOG)
 #define BOX_C2_SHIFT  (C2_SHIFT + BOX_C2_LOG)
 /*
  * The next three routines implement inverse colormap filling.  They could
  * all be folded into one big routine, but splitting them up this way saves
  * some stack space (the mindist[] and bestdist[] arrays need not coexist)
  * and may allow some compilers to produce better code by registerizing more
  * inner-loop variables.
  */
 LOCAL(int)
 find_nearby_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
 		    JSAMPLE colorlist[])
 /* Locate the colormap entries close enough to an update box to be candidates
  * for the nearest entry to some cell(s) in the update box.  The update box
  * is specified by the center coordinates of its first cell.  The number of
  * candidate colormap entries is returned, and their colormap indexes are
  * placed in colorlist[].
  * This routine uses Heckbert's "locally sorted search" criterion to select
  * the colors that need further consideration.
  */
 {
   int numcolors = cinfo->actual_number_of_colors;
   int maxc0, maxc1, maxc2;
   int centerc0, centerc1, centerc2;
   int i, x, ncolors;
   INT32 minmaxdist, min_dist, max_dist, tdist;
   INT32 mindist[MAXNUMCOLORS];	/* min distance to colormap entry i */
   /* Compute true coordinates of update box's upper corner and center.
    * Actually we compute the coordinates of the center of the upper-corner
    * histogram cell, which are the upper bounds of the volume we care about.
    * Note that since ">>" rounds down, the "center" values may be closer to
    * min than to max; hence comparisons to them must be "<=", not "<".
    */
   maxc0 = minc0 + ((1 << BOX_C0_SHIFT) - (1 << C0_SHIFT));
   centerc0 = (minc0 + maxc0) >> 1;
   maxc1 = minc1 + ((1 << BOX_C1_SHIFT) - (1 << C1_SHIFT));
   centerc1 = (minc1 + maxc1) >> 1;
   maxc2 = minc2 + ((1 << BOX_C2_SHIFT) - (1 << C2_SHIFT));
   centerc2 = (minc2 + maxc2) >> 1;
   /* For each color in colormap, find:
    *  1. its minimum squared-distance to any point in the update box
    *     (zero if color is within update box);
    *  2. its maximum squared-distance to any point in the update box.
    * Both of these can be found by considering only the corners of the box.
    * We save the minimum distance for each color in mindist[];
    * only the smallest maximum distance is of interest.
    */
   minmaxdist = 0x7FFFFFFFL;
   for (i = 0; i < numcolors; i++) {
     /* We compute the squared-c0-distance term, then add in the other two. */
     x = GETJSAMPLE(cinfo->colormap[0][i]);
     if (x < minc0) {
       tdist = (x - minc0) * C0_SCALE;
       min_dist = tdist*tdist;
       tdist = (x - maxc0) * C0_SCALE;
       max_dist = tdist*tdist;
     } else if (x > maxc0) {
       tdist = (x - maxc0) * C0_SCALE;
       min_dist = tdist*tdist;
       tdist = (x - minc0) * C0_SCALE;
       max_dist = tdist*tdist;
     } else {
       /* within cell range so no contribution to min_dist */
       min_dist = 0;
       if (x <= centerc0) {
 	tdist = (x - maxc0) * C0_SCALE;
 	max_dist = tdist*tdist;
       } else {
 	tdist = (x - minc0) * C0_SCALE;
 	max_dist = tdist*tdist;
       }
     }
     x = GETJSAMPLE(cinfo->colormap[1][i]);
     if (x < minc1) {
       tdist = (x - minc1) * C1_SCALE;
       min_dist += tdist*tdist;
       tdist = (x - maxc1) * C1_SCALE;
       max_dist += tdist*tdist;
     } else if (x > maxc1) {
       tdist = (x - maxc1) * C1_SCALE;
       min_dist += tdist*tdist;
       tdist = (x - minc1) * C1_SCALE;
       max_dist += tdist*tdist;
     } else {
       /* within cell range so no contribution to min_dist */
       if (x <= centerc1) {
 	tdist = (x - maxc1) * C1_SCALE;
 	max_dist += tdist*tdist;
       } else {
 	tdist = (x - minc1) * C1_SCALE;
 	max_dist += tdist*tdist;
       }
     }
     x = GETJSAMPLE(cinfo->colormap[2][i]);
     if (x < minc2) {
       tdist = (x - minc2) * C2_SCALE;
       min_dist += tdist*tdist;
       tdist = (x - maxc2) * C2_SCALE;
       max_dist += tdist*tdist;
     } else if (x > maxc2) {
       tdist = (x - maxc2) * C2_SCALE;
       min_dist += tdist*tdist;
       tdist = (x - minc2) * C2_SCALE;
       max_dist += tdist*tdist;
     } else {
       /* within cell range so no contribution to min_dist */
       if (x <= centerc2) {
 	tdist = (x - maxc2) * C2_SCALE;
 	max_dist += tdist*tdist;
       } else {
 	tdist = (x - minc2) * C2_SCALE;
 	max_dist += tdist*tdist;
       }
     }
     mindist[i] = min_dist;	/* save away the results */
     if (max_dist < minmaxdist)
       minmaxdist = max_dist;
   }
   /* Now we know that no cell in the update box is more than minmaxdist
    * away from some colormap entry.  Therefore, only colors that are
    * within minmaxdist of some part of the box need be considered.
    */
   ncolors = 0;
   for (i = 0; i < numcolors; i++) {
     if (mindist[i] <= minmaxdist)
       colorlist[ncolors++] = (JSAMPLE) i;
   }
   return ncolors;
 }
 LOCAL(void)
 find_best_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
 		  int numcolors, JSAMPLE colorlist[], JSAMPLE bestcolor[])
 /* Find the closest colormap entry for each cell in the update box,
  * given the list of candidate colors prepared by find_nearby_colors.
  * Return the indexes of the closest entries in the bestcolor[] array.
  * This routine uses Thomas' incremental distance calculation method to
  * find the distance from a colormap entry to successive cells in the box.
  */
 {
   int ic0, ic1, ic2;
   int i, icolor;
   register INT32 * bptr;	/* pointer into bestdist[] array */
   JSAMPLE * cptr;		/* pointer into bestcolor[] array */
   INT32 dist0, dist1;		/* initial distance values */
   register INT32 dist2;		/* current distance in inner loop */
   INT32 xx0, xx1;		/* distance increments */
   register INT32 xx2;
   INT32 inc0, inc1, inc2;	/* initial values for increments */
   /* This array holds the distance to the nearest-so-far color for each cell */
   INT32 bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
   /* Initialize best-distance for each cell of the update box */
   bptr = bestdist;
   for (i = BOX_C0_ELEMS*BOX_C1_ELEMS*BOX_C2_ELEMS-1; i >= 0; i--)
     *bptr++ = 0x7FFFFFFFL;
   /* For each color selected by find_nearby_colors,
    * compute its distance to the center of each cell in the box.
    * If that's less than best-so-far, update best distance and color number.
    */
   /* Nominal steps between cell centers ("x" in Thomas article) */
 #define STEP_C0  ((1 << C0_SHIFT) * C0_SCALE)
 #define STEP_C1  ((1 << C1_SHIFT) * C1_SCALE)
 #define STEP_C2  ((1 << C2_SHIFT) * C2_SCALE)
   for (i = 0; i < numcolors; i++) {
     icolor = GETJSAMPLE(colorlist[i]);
     /* Compute (square of) distance from minc0/c1/c2 to this color */
     inc0 = (minc0 - GETJSAMPLE(cinfo->colormap[0][icolor])) * C0_SCALE;
     dist0 = inc0*inc0;
     inc1 = (minc1 - GETJSAMPLE(cinfo->colormap[1][icolor])) * C1_SCALE;
     dist0 += inc1*inc1;
     inc2 = (minc2 - GETJSAMPLE(cinfo->colormap[2][icolor])) * C2_SCALE;
     dist0 += inc2*inc2;
     /* Form the initial difference increments */
     inc0 = inc0 * (2 * STEP_C0) + STEP_C0 * STEP_C0;
     inc1 = inc1 * (2 * STEP_C1) + STEP_C1 * STEP_C1;
     inc2 = inc2 * (2 * STEP_C2) + STEP_C2 * STEP_C2;
     /* Now loop over all cells in box, updating distance per Thomas method */
     bptr = bestdist;
     cptr = bestcolor;
     xx0 = inc0;
     for (ic0 = BOX_C0_ELEMS-1; ic0 >= 0; ic0--) {
       dist1 = dist0;
       xx1 = inc1;
       for (ic1 = BOX_C1_ELEMS-1; ic1 >= 0; ic1--) {
 	dist2 = dist1;
 	xx2 = inc2;
 	for (ic2 = BOX_C2_ELEMS-1; ic2 >= 0; ic2--) {
 	  if (dist2 < *bptr) {
 	    *bptr = dist2;
 	    *cptr = (JSAMPLE) icolor;
 	  }
 	  dist2 += xx2;
 	  xx2 += 2 * STEP_C2 * STEP_C2;
 	  bptr++;
 	  cptr++;
 	}
 	dist1 += xx1;
 	xx1 += 2 * STEP_C1 * STEP_C1;
       }
       dist0 += xx0;
       xx0 += 2 * STEP_C0 * STEP_C0;
     }
   }
 }
 LOCAL(void)
 fill_inverse_cmap (j_decompress_ptr cinfo, int c0, int c1, int c2)
 /* Fill the inverse-colormap entries in the update box that contains */
 /* histogram cell c0/c1/c2.  (Only that one cell MUST be filled, but */
 /* we can fill as many others as we wish.) */
 {
   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
   hist3d histogram = cquantize->histogram;
   int minc0, minc1, minc2;	/* lower left corner of update box */
   int ic0, ic1, ic2;
   register JSAMPLE * cptr;	/* pointer into bestcolor[] array */
   register histptr cachep;	/* pointer into main cache array */
   /* This array lists the candidate colormap indexes. */
   JSAMPLE colorlist[MAXNUMCOLORS];
   int numcolors;		/* number of candidate colors */
   /* This array holds the actually closest colormap index for each cell. */
   JSAMPLE bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
   /* Convert cell coordinates to update box ID */
   c0 >>= BOX_C0_LOG;
   c1 >>= BOX_C1_LOG;
   c2 >>= BOX_C2_LOG;
   /* Compute true coordinates of update box's origin corner.
    * Actually we compute the coordinates of the center of the corner
    * histogram cell, which are the lower bounds of the volume we care about.
    */
   minc0 = (c0 << BOX_C0_SHIFT) + ((1 << C0_SHIFT) >> 1);
   minc1 = (c1 << BOX_C1_SHIFT) + ((1 << C1_SHIFT) >> 1);
   minc2 = (c2 << BOX_C2_SHIFT) + ((1 << C2_SHIFT) >> 1);
   /* Determine which colormap entries are close enough to be candidates
    * for the nearest entry to some cell in the update box.
    */
   numcolors = find_nearby_colors(cinfo, minc0, minc1, minc2, colorlist);
   /* Determine the actually nearest colors. */
   find_best_colors(cinfo, minc0, minc1, minc2, numcolors, colorlist,
 		   bestcolor);
   /* Save the best color numbers (plus 1) in the main cache array */
   c0 <<= BOX_C0_LOG;		/* convert ID back to base cell indexes */
   c1 <<= BOX_C1_LOG;
   c2 <<= BOX_C2_LOG;
   cptr = bestcolor;
   for (ic0 = 0; ic0 < BOX_C0_ELEMS; ic0++) {
     for (ic1 = 0; ic1 < BOX_C1_ELEMS; ic1++) {
       cachep = & histogram[c0+ic0][c1+ic1][c2];
       for (ic2 = 0; ic2 < BOX_C2_ELEMS; ic2++) {
 	*cachep++ = (histcell) (GETJSAMPLE(*cptr++) + 1);
       }
     }
   }
 }
 /*
  * Map some rows of pixels to the output colormapped representation.
  */
 METHODDEF(void)
 pass2_no_dither (j_decompress_ptr cinfo,
 		 JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)
 /* This version performs no dithering */
 {
   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
   hist3d histogram = cquantize->histogram;
   register JSAMPROW inptr, outptr;
   register histptr cachep;
   register int c0, c1, c2;
   int row;
   JDIMENSION col;
   JDIMENSION width = cinfo->output_width;
   for (row = 0; row < num_rows; row++) {
     inptr = input_buf[row];
     outptr = output_buf[row];
     for (col = width; col > 0; col--) {
       /* get pixel value and index into the cache */
       c0 = GETJSAMPLE(*inptr++) >> C0_SHIFT;
       c1 = GETJSAMPLE(*inptr++) >> C1_SHIFT;
       c2 = GETJSAMPLE(*inptr++) >> C2_SHIFT;
       cachep = & histogram[c0][c1][c2];
       /* If we have not seen this color before, find nearest colormap entry */
       /* and update the cache */
       if (*cachep == 0)
 	fill_inverse_cmap(cinfo, c0,c1,c2);
       /* Now emit the colormap index for this cell */
       *outptr++ = (JSAMPLE) (*cachep - 1);
     }
   }
 }
 METHODDEF(void)
 pass2_fs_dither (j_decompress_ptr cinfo,
 		 JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)
 /* This version performs Floyd-Steinberg dithering */
 {
   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
   hist3d histogram = cquantize->histogram;
   register LOCFSERROR cur0, cur1, cur2;	/* current error or pixel value */
   LOCFSERROR belowerr0, belowerr1, belowerr2; /* error for pixel below cur */
   LOCFSERROR bpreverr0, bpreverr1, bpreverr2; /* error for below/prev col */
   register FSERRPTR errorptr;	/* => fserrors[] at column before current */
   JSAMPROW inptr;		/* => current input pixel */
   JSAMPROW outptr;		/* => current output pixel */
   histptr cachep;
   int dir;			/* +1 or -1 depending on direction */
   int dir3;			/* 3*dir, for advancing inptr & errorptr */
   int row;
   JDIMENSION col;
   JDIMENSION width = cinfo->output_width;
   JSAMPLE *range_limit = cinfo->sample_range_limit;
   int *error_limit = cquantize->error_limiter;
   JSAMPROW colormap0 = cinfo->colormap[0];
   JSAMPROW colormap1 = cinfo->colormap[1];
   JSAMPROW colormap2 = cinfo->colormap[2];
   SHIFT_TEMPS
   for (row = 0; row < num_rows; row++) {
     inptr = input_buf[row];
     outptr = output_buf[row];
     if (cquantize->on_odd_row) {
       /* work right to left in this row */
       inptr += (width-1) * 3;	/* so point to rightmost pixel */
       outptr += width-1;
       dir = -1;
       dir3 = -3;
       errorptr = cquantize->fserrors + (width+1)*3; /* => entry after last column */
       cquantize->on_odd_row = FALSE; /* flip for next time */
     } else {
       /* work left to right in this row */
       dir = 1;
       dir3 = 3;
       errorptr = cquantize->fserrors; /* => entry before first real column */
       cquantize->on_odd_row = TRUE; /* flip for next time */
     }
     /* Preset error values: no error propagated to first pixel from left */
     cur0 = cur1 = cur2 = 0;
     /* and no error propagated to row below yet */
     belowerr0 = belowerr1 = belowerr2 = 0;
     bpreverr0 = bpreverr1 = bpreverr2 = 0;
     for (col = width; col > 0; col--) {
       /* curN holds the error propagated from the previous pixel on the
        * current line.  Add the error propagated from the previous line
        * to form the complete error correction term for this pixel, and
        * round the error term (which is expressed * 16) to an integer.
        * RIGHT_SHIFT rounds towards minus infinity, so adding 8 is correct
        * for either sign of the error value.
        * Note: errorptr points to *previous* column's array entry.
        */
       cur0 = RIGHT_SHIFT(cur0 + errorptr[dir3+0] + 8, 4);
       cur1 = RIGHT_SHIFT(cur1 + errorptr[dir3+1] + 8, 4);
       cur2 = RIGHT_SHIFT(cur2 + errorptr[dir3+2] + 8, 4);
       /* Limit the error using transfer function set by init_error_limit.
        * See comments with init_error_limit for rationale.
        */
       cur0 = error_limit[cur0];
       cur1 = error_limit[cur1];
       cur2 = error_limit[cur2];
       /* Form pixel value + error, and range-limit to 0..MAXJSAMPLE.
        * The maximum error is +- MAXJSAMPLE (or less with error limiting);
        * this sets the required size of the range_limit array.
        */
       cur0 += GETJSAMPLE(inptr[0]);
       cur1 += GETJSAMPLE(inptr[1]);
       cur2 += GETJSAMPLE(inptr[2]);
       cur0 = GETJSAMPLE(range_limit[cur0]);
       cur1 = GETJSAMPLE(range_limit[cur1]);
       cur2 = GETJSAMPLE(range_limit[cur2]);
       /* Index into the cache with adjusted pixel value */
       cachep = & histogram[cur0>>C0_SHIFT][cur1>>C1_SHIFT][cur2>>C2_SHIFT];
       /* If we have not seen this color before, find nearest colormap */
       /* entry and update the cache */
       if (*cachep == 0)
 	fill_inverse_cmap(cinfo, cur0>>C0_SHIFT,cur1>>C1_SHIFT,cur2>>C2_SHIFT);
       /* Now emit the colormap index for this cell */
       { register int pixcode = *cachep - 1;
 	*outptr = (JSAMPLE) pixcode;
 	/* Compute representation error for this pixel */
 	cur0 -= GETJSAMPLE(colormap0[pixcode]);
 	cur1 -= GETJSAMPLE(colormap1[pixcode]);
 	cur2 -= GETJSAMPLE(colormap2[pixcode]);
       }
       /* Compute error fractions to be propagated to adjacent pixels.
        * Add these into the running sums, and simultaneously shift the
        * next-line error sums left by 1 column.
        */
       { register LOCFSERROR bnexterr, delta;
 	bnexterr = cur0;	/* Process component 0 */
 	delta = cur0 * 2;
 	cur0 += delta;		/* form error * 3 */
 	errorptr[0] = (FSERROR) (bpreverr0 + cur0);
 	cur0 += delta;		/* form error * 5 */
 	bpreverr0 = belowerr0 + cur0;
 	belowerr0 = bnexterr;
 	cur0 += delta;		/* form error * 7 */
 	bnexterr = cur1;	/* Process component 1 */
 	delta = cur1 * 2;
 	cur1 += delta;		/* form error * 3 */
 	errorptr[1] = (FSERROR) (bpreverr1 + cur1);
 	cur1 += delta;		/* form error * 5 */
 	bpreverr1 = belowerr1 + cur1;
 	belowerr1 = bnexterr;
 	cur1 += delta;		/* form error * 7 */
 	bnexterr = cur2;	/* Process component 2 */
 	delta = cur2 * 2;
 	cur2 += delta;		/* form error * 3 */
 	errorptr[2] = (FSERROR) (bpreverr2 + cur2);
 	cur2 += delta;		/* form error * 5 */
 	bpreverr2 = belowerr2 + cur2;
 	belowerr2 = bnexterr;
 	cur2 += delta;		/* form error * 7 */
       }
       /* At this point curN contains the 7/16 error value to be propagated
        * to the next pixel on the current line, and all the errors for the
        * next line have been shifted over.  We are therefore ready to move on.
        */
       inptr += dir3;		/* Advance pixel pointers to next column */
       outptr += dir;
       errorptr += dir3;		/* advance errorptr to current column */
     }
     /* Post-loop cleanup: we must unload the final error values into the
      * final fserrors[] entry.  Note we need not unload belowerrN because
      * it is for the dummy column before or after the actual array.
      */
     errorptr[0] = (FSERROR) bpreverr0; /* unload prev errs into array */
     errorptr[1] = (FSERROR) bpreverr1;
     errorptr[2] = (FSERROR) bpreverr2;
   }
 }
 /*
  * Initialize the error-limiting transfer function (lookup table).
  * The raw F-S error computation can potentially compute error values of up to
  * +- MAXJSAMPLE.  But we want the maximum correction applied to a pixel to be
  * much less, otherwise obviously wrong pixels will be created.  (Typical
  * effects include weird fringes at color-area boundaries, isolated bright
  * pixels in a dark area, etc.)  The standard advice for avoiding this problem
  * is to ensure that the "corners" of the color cube are allocated as output
  * colors; then repeated errors in the same direction cannot cause cascading
  * error buildup.  However, that only prevents the error from getting
  * completely out of hand; Aaron Giles reports that error limiting improves
  * the results even with corner colors allocated.
  * A simple clamping of the error values to about +- MAXJSAMPLE/8 works pretty
  * well, but the smoother transfer function used below is even better.  Thanks
  * to Aaron Giles for this idea.
  */
 LOCAL(void)
 init_error_limit (j_decompress_ptr cinfo)
 /* Allocate and fill in the error_limiter table */
 {
   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
   int * table;
   int in, out;
   table = (int *) (*cinfo->mem->alloc_small)
     ((j_common_ptr) cinfo, JPOOL_IMAGE, (MAXJSAMPLE*2+1) * SIZEOF(int));
   table += MAXJSAMPLE;		/* so can index -MAXJSAMPLE .. +MAXJSAMPLE */
   cquantize->error_limiter = table;
 #define STEPSIZE ((MAXJSAMPLE+1)/16)
   /* Map errors 1:1 up to +- MAXJSAMPLE/16 */
   out = 0;
   for (in = 0; in < STEPSIZE; in++, out++) {
     table[in] = out; table[-in] = -out;
   }
   /* Map errors 1:2 up to +- 3*MAXJSAMPLE/16 */
   for (; in < STEPSIZE*3; in++, out += (in&1) ? 0 : 1) {
     table[in] = out; table[-in] = -out;
   }
   /* Clamp the rest to final out value (which is (MAXJSAMPLE+1)/8) */
   for (; in <= MAXJSAMPLE; in++) {
     table[in] = out; table[-in] = -out;
   }
 #undef STEPSIZE
 }
 /*
  * Finish up at the end of each pass.
  */
 METHODDEF(void)
 finish_pass1 (j_decompress_ptr cinfo)
 {
   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
   /* Select the representative colors and fill in cinfo->colormap */
   cinfo->colormap = cquantize->sv_colormap;
   select_colors(cinfo, cquantize->desired);
   /* Force next pass to zero the color index table */
   cquantize->needs_zeroed = TRUE;
 }
 METHODDEF(void)
 finish_pass2 (j_decompress_ptr cinfo)
 {
   /* no work */
 }
 /*
  * Initialize for each processing pass.
  */
 METHODDEF(void)
 start_pass_2_quant (j_decompress_ptr cinfo, boolean is_pre_scan)
 {
   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
   hist3d histogram = cquantize->histogram;
   int i;
   /* Only F-S dithering or no dithering is supported. */
   /* If user asks for ordered dither, give him F-S. */
   if (cinfo->dither_mode != JDITHER_NONE)
     cinfo->dither_mode = JDITHER_FS;
   if (is_pre_scan) {
     /* Set up method pointers */
     cquantize->pub.color_quantize = prescan_quantize;
     cquantize->pub.finish_pass = finish_pass1;
     cquantize->needs_zeroed = TRUE; /* Always zero histogram */
   } else {
     /* Set up method pointers */
     if (cinfo->dither_mode == JDITHER_FS)
       cquantize->pub.color_quantize = pass2_fs_dither;
     else
       cquantize->pub.color_quantize = pass2_no_dither;
     cquantize->pub.finish_pass = finish_pass2;
     /* Make sure color count is acceptable */
     i = cinfo->actual_number_of_colors;
     if (i < 1)
       ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 1);
     if (i > MAXNUMCOLORS)
       ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
     if (cinfo->dither_mode == JDITHER_FS) {
       size_t arraysize = (size_t) ((cinfo->output_width + 2) *
 				   (3 * SIZEOF(FSERROR)));
       /* Allocate Floyd-Steinberg workspace if we didn't already. */
       if (cquantize->fserrors == NULL)
 	cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)
 	  ((j_common_ptr) cinfo, JPOOL_IMAGE, arraysize);
       /* Initialize the propagated errors to zero. */
       jzero_far((void FAR *) cquantize->fserrors, arraysize);
       /* Make the error-limit table if we didn't already. */
       if (cquantize->error_limiter == NULL)
 	init_error_limit(cinfo);
       cquantize->on_odd_row = FALSE;
     }
   }
   /* Zero the histogram or inverse color map, if necessary */
   if (cquantize->needs_zeroed) {
     for (i = 0; i < HIST_C0_ELEMS; i++) {
       jzero_far((void FAR *) histogram[i],
 		HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));
     }
     cquantize->needs_zeroed = FALSE;
   }
 }
 /*
  * Switch to a new external colormap between output passes.
  */
 METHODDEF(void)
 new_color_map_2_quant (j_decompress_ptr cinfo)
 {
   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
   /* Reset the inverse color map */
   cquantize->needs_zeroed = TRUE;
 }
 /*
  * Module initialization routine for 2-pass color quantization.
  */
 GLOBAL(void)
 jinit_2pass_quantizer (j_decompress_ptr cinfo)
 {
   my_cquantize_ptr cquantize;
   int i;
   cquantize = (my_cquantize_ptr)
     (*cinfo->mem->alloc_small) ((j_common_ptr) cinfo, JPOOL_IMAGE,
 				SIZEOF(my_cquantizer));
   cinfo->cquantize = (struct jpeg_color_quantizer *) cquantize;
   cquantize->pub.start_pass = start_pass_2_quant;
   cquantize->pub.new_color_map = new_color_map_2_quant;
   cquantize->fserrors = NULL;	/* flag optional arrays not allocated */
   cquantize->error_limiter = NULL;
   /* Make sure jdmaster didn't give me a case I can't handle */
   if (cinfo->out_color_components != 3)
     ERREXIT(cinfo, JERR_NOTIMPL);
   /* Allocate the histogram/inverse colormap storage */
   cquantize->histogram = (hist3d) (*cinfo->mem->alloc_small)
     ((j_common_ptr) cinfo, JPOOL_IMAGE, HIST_C0_ELEMS * SIZEOF(hist2d));
   for (i = 0; i < HIST_C0_ELEMS; i++) {
     cquantize->histogram[i] = (hist2d) (*cinfo->mem->alloc_large)
       ((j_common_ptr) cinfo, JPOOL_IMAGE,
        HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));
   }
   cquantize->needs_zeroed = TRUE; /* histogram is garbage now */
   /* Allocate storage for the completed colormap, if required.
    * We do this now since it is FAR storage and may affect
    * the memory manager's space calculations.
    */
   if (cinfo->enable_2pass_quant) {
     /* Make sure color count is acceptable */
     int desired = cinfo->desired_number_of_colors;
     /* Lower bound on # of colors ... somewhat arbitrary as long as > 0 */
     if (desired < 8)
       ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 8);
     /* Make sure colormap indexes can be represented by JSAMPLEs */
     if (desired > MAXNUMCOLORS)
       ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
     cquantize->sv_colormap = (*cinfo->mem->alloc_sarray)
       ((j_common_ptr) cinfo,JPOOL_IMAGE, (JDIMENSION) desired, (JDIMENSION) 3);
     cquantize->desired = desired;
   } else
     cquantize->sv_colormap = NULL;
   /* Only F-S dithering or no dithering is supported. */
   /* If user asks for ordered dither, give him F-S. */
   if (cinfo->dither_mode != JDITHER_NONE)
     cinfo->dither_mode = JDITHER_FS;
   /* Allocate Floyd-Steinberg workspace if necessary.
    * This isn't really needed until pass 2, but again it is FAR storage.
    * Although we will cope with a later change in dither_mode,
    * we do not promise to honor max_memory_to_use if dither_mode changes.
    */
   if (cinfo->dither_mode == JDITHER_FS) {
     cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)
       ((j_common_ptr) cinfo, JPOOL_IMAGE,
        (size_t) ((cinfo->output_width + 2) * (3 * SIZEOF(FSERROR))));
     /* Might as well create the error-limiting table too. */
     init_error_limit(cinfo);
   }
 }
 #endif /* QUANT_2PASS_SUPPORTED */

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media/libjpeg/jquant2.c@b8a032363ba2 (annotated)

media/libjpeg/jquant2.c