text: /*
   * 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, 2014-2015, 2020, D. R. Commander.
   * For conditions of distribution and use, see the accompanying README.ijg
   * 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.
   */
  
  #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 *histptr;      /* for pointers to histogram cells */
  
  typedef histcell hist1d[HIST_C2_ELEMS]; /* typedefs for the array */
  typedef hist1d *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.
   */
  
  #if BITS_IN_JSAMPLE == 8
  typedef INT16 FSERROR;          /* 16 bits should be enough */
  typedef int LOCFSERROR;         /* use 'int' for calculation temps */
  #else
  typedef JLONG FSERROR;          /* may need more than 16 bits */
  typedef JLONG LOCFSERROR;       /* be sure calculation temps are big enough */
  #endif
  
  typedef FSERROR *FSERRPTR;      /* pointer to error array */
  
  
  /* 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[ptr[0] >> C0_SHIFT]
                          [ptr[1] >> C1_SHIFT]
                          [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 */
    JLONG 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 JLONG 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;
    JLONG 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 JLONGs, 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;
    JLONG minmaxdist, min_dist, max_dist, tdist;
    JLONG 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 = 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 = 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 = 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 JLONG *bptr;         /* pointer into bestdist[] array */
    JSAMPLE *cptr;                /* pointer into bestcolor[] array */
    JLONG dist0, dist1;           /* initial distance values */
    register JLONG dist2;         /* current distance in inner loop */
    JLONG xx0, xx1;               /* distance increments */
    register JLONG xx2;
    JLONG inc0, inc1, inc2;       /* initial values for increments */
    /* This array holds the distance to the nearest-so-far color for each cell */
    JLONG 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 = colorlist[i];
      /* Compute (square of) distance from minc0/c1/c2 to this color */
      inc0 = (minc0 - cinfo->colormap[0][icolor]) * C0_SCALE;
      dist0 = inc0 * inc0;
      inc1 = (minc1 - cinfo->colormap[1][icolor]) * C1_SCALE;
      dist0 += inc1 * inc1;
      inc2 = (minc2 - 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)((*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 = (*inptr++) >> C0_SHIFT;
        c1 = (*inptr++) >> C1_SHIFT;
        c2 = (*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 += inptr[0];
        cur1 += inptr[1];
        cur2 += inptr[2];
        cur0 = range_limit[cur0];
        cur1 = range_limit[cur1];
        cur2 = 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 -= colormap0[pixcode];
          cur1 -= colormap1[pixcode];
          cur2 -= 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;
  
          bnexterr = cur0;        /* Process component 0 */
          errorptr[0] = (FSERROR)(bpreverr0 + cur0 * 3);
          bpreverr0 = belowerr0 + cur0 * 5;
          belowerr0 = bnexterr;
          cur0 *= 7;
          bnexterr = cur1;        /* Process component 1 */
          errorptr[1] = (FSERROR)(bpreverr1 + cur1 * 3);
          bpreverr1 = belowerr1 + cur1 * 5;
          belowerr1 = bnexterr;
          cur1 *= 7;
          bnexterr = cur2;        /* Process component 2 */
          errorptr[2] = (FSERROR)(bpreverr2 + cur2 * 3);
          bpreverr2 = belowerr2 + cur2 * 5;
          belowerr2 = bnexterr;
          cur2 *= 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 them 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 *)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 *)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 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 them 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 may affect the memory
     * manager's space calculations.  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 */
  