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jquant2.c (49272B)

---

/*
* 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, 2022-2023, 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"
#include "jsamplecomp.h"

#if defined(QUANT_2PASS_SUPPORTED) && BITS_IN_JSAMPLE != 16


/*
* 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;
       }
     }
   }

 ((_JSAMPARRAY)cinfo->colormap)[0][icolor] =
   (_JSAMPLE)((c0total + (total >> 1)) / total);
 ((_JSAMPARRAY)cinfo->colormap)[1][icolor] =
   (_JSAMPLE)((c1total + (total >> 1)) / total);
 ((_JSAMPARRAY)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 = ((_JSAMPARRAY)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 = ((_JSAMPARRAY)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 = ((_JSAMPARRAY)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 - ((_JSAMPARRAY)cinfo->colormap)[0][icolor]) * C0_SCALE;
   dist0 = inc0 * inc0;
   inc1 = (minc1 - ((_JSAMPARRAY)cinfo->colormap)[1][icolor]) * C1_SCALE;
   dist0 += inc1 * inc1;
   inc2 = (minc2 - ((_JSAMPARRAY)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 = (_JSAMPLE *)cinfo->sample_range_limit;
 int *error_limit = cquantize->error_limiter;
 _JSAMPROW colormap0 = ((_JSAMPARRAY)cinfo->colormap)[0];
 _JSAMPROW colormap1 = ((_JSAMPARRAY)cinfo->colormap)[1];
 _JSAMPROW colormap2 = ((_JSAMPARRAY)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 = (JSAMPARRAY)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;

 if (cinfo->data_precision != BITS_IN_JSAMPLE)
   ERREXIT1(cinfo, JERR_BAD_PRECISION, cinfo->data_precision);

 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 ||
     cinfo->out_color_space == JCS_RGB565 || cinfo->master->lossless)
   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 = (_JSAMPARRAY)(*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 /* defined(QUANT_2PASS_SUPPORTED) && BITS_IN_JSAMPLE != 16 */