jquant2.cpp

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/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          http://www.mrpt.org/                          |
   |                                                                        |
   | Copyright (c) 2005-2017, Individual contributors, see AUTHORS file     |
   | See: http://www.mrpt.org/Authors - All rights reserved.                |
   | Released under BSD License. See details in http://www.mrpt.org/License |
   +------------------------------------------------------------------------+ */

#define JPEG_INTERNALS
#include "jinclude.h"
#include "mrpt_jpeglib.h"
#include <mrpt/utils/mrpt_macros.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 */

/* Relabel R/G/B as components 0/1/2, respecting the RGB ordering defined
 * in jmorecfg.h.  As the code stands, it will do the right thing for R,G,B
 * and B,G,R orders.  If you define some other weird order in jmorecfg.h,
 * you'll get compile errors until you extend this logic.  In that case
 * you'll probably want to tweak the histogram sizes too.
 */

#if RGB_RED == 0
#define C0_SCALE R_SCALE
#endif
#if RGB_BLUE == 0
#define C0_SCALE B_SCALE
#endif
#if RGB_GREEN == 1
#define C1_SCALE G_SCALE
#endif
#if RGB_RED == 2
#define C2_SCALE R_SCALE
#endif
#if RGB_BLUE == 2
#define C2_SCALE B_SCALE
#endif

/*
 * First we have the histogram data structure and routines for creating it.
 *
 * The number of bits of precision can be adjusted by changing these symbols.
 * We recommend keeping 6 bits for G and 5 each for R and B.
 * If you have plenty of memory and cycles, 6 bits all around gives marginally
 * better results; if you are short of memory, 5 bits all around will save
 * some space but degrade the results.
 * To maintain a fully accurate histogram, we'd need to allocate a "long"
 * (preferably unsigned long) for each cell.  In practice this is overkill;
 * we can get by with 16 bits per cell.  Few of the cell counts will overflow,
 * and clamping those that do overflow to the maximum value will give close-
 * enough results.  This reduces the recommended histogram size from 256Kb
 * to 128Kb, which is a useful savings on PC-class machines.
 * (In the second pass the histogram space is re-used for pixel mapping data;
 * in that capacity, each cell must be able to store zero to the number of
 * desired colors.  16 bits/cell is plenty for that too.)
 * Since the JPEG code is intended to run in small memory model on 80x86
 * machines, we can't just allocate the histogram in one chunk.  Instead
 * of a true 3-D array, we use a row of pointers to 2-D arrays.  Each
 * pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and
 * each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries.  Note that
 * on 80x86 machines, the pointer row is in near memory but the actual
 * arrays are in far memory (same arrangement as we use for image arrays).
 */

#define MAXNUMCOLORS (MAXJSAMPLE + 1) /* maximum size of colormap */

/* These will do the right thing for either R,G,B or B,G,R color order,
 * but you may not like the results for other color orders.
 */
#define HIST_C0_BITS 5 /* bits of precision in R/B histogram */
#define HIST_C1_BITS 6 /* bits of precision in G histogram */
#define HIST_C2_BITS 5 /* bits of precision in B/R histogram */

/* Number of elements along histogram axes. */
#define HIST_C0_ELEMS (1 << HIST_C0_BITS)
#define HIST_C1_ELEMS (1 << HIST_C1_BITS)
#define HIST_C2_ELEMS (1 << HIST_C2_BITS)

/* These are the amounts to shift an input value to get a histogram index. */
#define C0_SHIFT (BITS_IN_JSAMPLE - HIST_C0_BITS)
#define C1_SHIFT (BITS_IN_JSAMPLE - HIST_C1_BITS)
#define C2_SHIFT (BITS_IN_JSAMPLE - HIST_C2_BITS)

typedef UINT16 histcell; /* histogram cell; prefer an unsigned type */

typedef histcell FAR* histptr; /* for pointers to histogram cells */

typedef histcell hist1d[HIST_C2_ELEMS]; /* typedefs for the array */
typedef hist1d FAR* hist2d; /* type for the 2nd-level pointers */
typedef hist2d* hist3d; /* type for top-level pointer */

/* Declarations for Floyd-Steinberg dithering.
 *
 * Errors are accumulated into the array fserrors[], at a resolution of
 * 1/16th of a pixel count.  The error at a given pixel is propagated
 * to its not-yet-processed neighbors using the standard F-S fractions,
 *              ...     (here)  7/16
 *              3/16    5/16    1/16
 * We work left-to-right on even rows, right-to-left on odd rows.
 *
 * We can get away with a single array (holding one row's worth of errors)
 * by using it to store the current row's errors at pixel columns not yet
 * processed, but the next row's errors at columns already processed.  We
 * need only a few extra variables to hold the errors immediately around the
 * current column.  (If we are lucky, those variables are in registers, but
 * even if not, they're probably cheaper to access than array elements are.)
 *
 * The fserrors[] array has (#columns + 2) entries; the extra entry at
 * each end saves us from special-casing the first and last pixels.
 * Each entry is three values long, one value for each color component.
 *
 * Note: on a wide image, we might not have enough room in a PC's near data
 * segment to hold the error array; so it is allocated with alloc_large.
 */

#if BITS_IN_JSAMPLE == 8
typedef INT16 FSERROR; /* 16 bits should be enough */
typedef int LOCFSERROR; /* use 'int' for calculation temps */
#else
typedef INT32 FSERROR; /* may need more than 16 bits */
typedef INT32 LOCFSERROR; /* be sure calculation temps are big enough */
#endif

typedef FSERROR FAR* FSERRPTR; /* pointer to error array (in FAR storage!) */

/* Private subobject */

typedef struct
{
        struct jpeg_color_quantizer pub; /* public fields */

        /* Space for the eventually created colormap is stashed here */
        JSAMPARRAY sv_colormap; /* colormap allocated at init time */
        int desired; /* desired # of colors = size of colormap */

        /* Variables for accumulating image statistics */
        hist3d histogram; /* pointer to the histogram */

        boolean needs_zeroed; /* TRUE if next pass must zero histogram */

        /* Variables for Floyd-Steinberg dithering */
        FSERRPTR fserrors; /* accumulated errors */
        boolean on_odd_row; /* flag to remember which row we are on */
        int* error_limiter; /* table for clamping the applied error */
} my_cquantizer;

typedef my_cquantizer* my_cquantize_ptr;

/*
 * Prescan some rows of pixels.
 * In this module the prescan simply updates the histogram, which has been
 * initialized to zeroes by start_pass.
 * An output_buf parameter is required by the method signature, but no data
 * is actually output (in fact the buffer controller is probably passing a
 * nullptr pointer).
 */

METHODDEF(void)
prescan_quantize(
        j_decompress_ptr cinfo, JSAMPARRAY input_buf, JSAMPARRAY output_buf,
        int num_rows)
{
        MRPT_UNUSED_PARAM(output_buf);
        MRPT_UNUSED_PARAM(cinfo);
        my_cquantize_ptr cquantize = (my_cquantize_ptr)cinfo->cquantize;
        JSAMPROW ptr;
        histptr histp;
        hist3d histogram = cquantize->histogram;
        int row;
        JDIMENSION col;
        JDIMENSION width = cinfo->output_width;

        for (row = 0; row < num_rows; row++)
        {
                ptr = input_buf[row];
                for (col = width; col > 0; col--)
                {
                        /* get pixel value and index into the histogram */
                        histp = &histogram[GETJSAMPLE(ptr[0]) >> C0_SHIFT]
                                                          [GETJSAMPLE(ptr[1]) >> C1_SHIFT]
                                                          [GETJSAMPLE(ptr[2]) >> C2_SHIFT];
                        /* increment, check for overflow and undo increment if so. */
                        if (++(*histp) <= 0) (*histp)--;
                        ptr += 3;
                }
        }
}

/*
 * Next we have the really interesting routines: selection of a colormap
 * given the completed histogram.
 * These routines work with a list of "boxes", each representing a rectangular
 * subset of the input color space (to histogram precision).
 */

typedef struct
{
        /* The bounds of the box (inclusive); expressed as histogram indexes */
        int c0min, c0max;
        int c1min, c1max;
        int c2min, c2max;
        /* The volume (actually 2-norm) of the box */
        INT32 volume;
        /* The number of nonzero histogram cells within this box */
        long colorcount;
} box;

typedef box* boxptr;

LOCAL(boxptr)
find_biggest_color_pop(boxptr boxlist, int numboxes)
/* Find the splittable box with the largest color population */
/* Returns nullptr if no splittable boxes remain */
{
        boxptr boxp;
        int i;
        long maxc = 0;
        boxptr which = nullptr;

        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 nullptr if no splittable boxes remain */
{
        boxptr boxp;
        int i;
        INT32 maxv = 0;
        boxptr which = nullptr;

        for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++)
        {
                if (boxp->volume > maxv)
                {
                        which = boxp;
                        maxv = boxp->volume;
                }
        }
        return which;
}

LOCAL(void)
update_box(j_decompress_ptr cinfo, boxptr boxp)
/* Shrink the min/max bounds of a box to enclose only nonzero elements, */
/* and recompute its volume and population */
{
        my_cquantize_ptr cquantize = (my_cquantize_ptr)cinfo->cquantize;
        hist3d histogram = cquantize->histogram;
        histptr histp;
        int c0, c1, c2;
        int c0min, c0max, c1min, c1max, c2min, c2max;
        INT32 dist0, dist1, dist2;
        long ccount;

        c0min = boxp->c0min;
        c0max = boxp->c0max;
        c1min = boxp->c1min;
        c1max = boxp->c1max;
        c2min = boxp->c2min;
        c2max = boxp->c2max;

        if (c0max > c0min)
                for (c0 = c0min; c0 <= c0max; c0++)
                        for (c1 = c1min; c1 <= c1max; c1++)
                        {
                                histp = &histogram[c0][c1][c2min];
                                for (c2 = c2min; c2 <= c2max; c2++)
                                        if (*histp++ != 0)
                                        {
                                                boxp->c0min = c0min = c0;
                                                goto have_c0min;
                                        }
                        }
have_c0min:
        if (c0max > c0min)
                for (c0 = c0max; c0 >= c0min; c0--)
                        for (c1 = c1min; c1 <= c1max; c1++)
                        {
                                histp = &histogram[c0][c1][c2min];
                                for (c2 = c2min; c2 <= c2max; c2++)
                                        if (*histp++ != 0)
                                        {
                                                boxp->c0max = c0max = c0;
                                                goto have_c0max;
                                        }
                        }
have_c0max:
        if (c1max > c1min)
                for (c1 = c1min; c1 <= c1max; c1++)
                        for (c0 = c0min; c0 <= c0max; c0++)
                        {
                                histp = &histogram[c0][c1][c2min];
                                for (c2 = c2min; c2 <= c2max; c2++)
                                        if (*histp++ != 0)
                                        {
                                                boxp->c1min = c1min = c1;
                                                goto have_c1min;
                                        }
                        }
have_c1min:
        if (c1max > c1min)
                for (c1 = c1max; c1 >= c1min; c1--)
                        for (c0 = c0min; c0 <= c0max; c0++)
                        {
                                histp = &histogram[c0][c1][c2min];
                                for (c2 = c2min; c2 <= c2max; c2++)
                                        if (*histp++ != 0)
                                        {
                                                boxp->c1max = c1max = c1;
                                                goto have_c1max;
                                        }
                        }
have_c1max:
        if (c2max > c2min)
                for (c2 = c2min; c2 <= c2max; c2++)
                        for (c0 = c0min; c0 <= c0max; c0++)
                        {
                                histp = &histogram[c0][c1min][c2];
                                for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
                                        if (*histp != 0)
                                        {
                                                boxp->c2min = c2min = c2;
                                                goto have_c2min;
                                        }
                        }
have_c2min:
        if (c2max > c2min)
                for (c2 = c2max; c2 >= c2min; c2--)
                        for (c0 = c0min; c0 <= c0max; c0++)
                        {
                                histp = &histogram[c0][c1min][c2];
                                for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
                                        if (*histp != 0)
                                        {
                                                boxp->c2max = c2max = c2;
                                                goto have_c2max;
                                        }
                        }
have_c2max:

        /* Update box volume.
         * We use 2-norm rather than real volume here; this biases the method
         * against making long narrow boxes, and it has the side benefit that
         * a box is splittable iff norm > 0.
         * Since the differences are expressed in histogram-cell units,
         * we have to shift back to JSAMPLE units to get consistent distances;
         * after which, we scale according to the selected distance scale factors.
         */
        dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE;
        dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE;
        dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE;
        boxp->volume = dist0 * dist0 + dist1 * dist1 + dist2 * dist2;

        /* Now scan remaining volume of box and compute population */
        ccount = 0;
        for (c0 = c0min; c0 <= c0max; c0++)
                for (c1 = c1min; c1 <= c1max; c1++)
                {
                        histp = &histogram[c0][c1][c2min];
                        for (c2 = c2min; c2 <= c2max; c2++, histp++)
                                if (*histp != 0)
                                {
                                        ccount++;
                                }
                }
        boxp->colorcount = ccount;
}

LOCAL(int)
median_cut(
        j_decompress_ptr cinfo, boxptr boxlist, int numboxes, int desired_colors)
/* Repeatedly select and split the largest box until we have enough boxes */
{
        int n, lb;
        int c0, c1, c2, cmax;
        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 == nullptr) /* 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 == 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;
                }
#endif
                /* Choose split point along selected axis, and update box bounds.
                 * Current algorithm: split at halfway point.
                 * (Since the box has been shrunk to minimum volume,
                 * any split will produce two nonempty subboxes.)
                 * Note that lb value is max for lower box, so must be < old max.
                 */
                switch (n)
                {
                        case 0:
                                lb = (b1->c0max + b1->c0min) / 2;
                                b1->c0max = lb;
                                b2->c0min = lb + 1;
                                break;
                        case 1:
                                lb = (b1->c1max + b1->c1min) / 2;
                                b1->c1max = lb;
                                b2->c1min = lb + 1;
                                break;
                        case 2:
                                lb = (b1->c2max + b1->c2min) / 2;
                                b1->c2max = lb;
                                b2->c2min = lb + 1;
                                break;
                }
                /* Update stats for boxes */
                update_box(cinfo, b1);
                update_box(cinfo, b2);
                numboxes++;
        }
        return numboxes;
}

LOCAL(void)
compute_color(j_decompress_ptr cinfo, boxptr boxp, int icolor)
/* Compute representative color for a box, put it in colormap[icolor] */
{
        /* Current algorithm: mean weighted by pixels (not colors) */
        /* Note it is important to get the rounding correct! */
        my_cquantize_ptr cquantize = (my_cquantize_ptr)cinfo->cquantize;
        hist3d histogram = cquantize->histogram;
        histptr histp;
        int c0, c1, c2;
        int c0min, c0max, c1min, c1max, c2min, c2max;
        long count;
        long total = 0;
        long c0total = 0;
        long c1total = 0;
        long c2total = 0;

        c0min = boxp->c0min;
        c0max = boxp->c0max;
        c1min = boxp->c1min;
        c1max = boxp->c1max;
        c2min = boxp->c2min;
        c2max = boxp->c2max;

        for (c0 = c0min; c0 <= c0max; c0++)
                for (c1 = c1min; c1 <= c1max; c1++)
                {
                        histp = &histogram[c0][c1][c2min];
                        for (c2 = c2min; c2 <= c2max; c2++)
                        {
                                if ((count = *histp++) != 0)
                                {
                                        total += count;
                                        c0total +=
                                                ((c0 << C0_SHIFT) + ((1 << C0_SHIFT) >> 1)) * count;
                                        c1total +=
                                                ((c1 << C1_SHIFT) + ((1 << C1_SHIFT) >> 1)) * count;
                                        c2total +=
                                                ((c2 << C2_SHIFT) + ((1 << C2_SHIFT) >> 1)) * count;
                                }
                        }
                }

        cinfo->colormap[0][icolor] = (JSAMPLE)((c0total + (total >> 1)) / total);
        cinfo->colormap[1][icolor] = (JSAMPLE)((c1total + (total >> 1)) / total);
        cinfo->colormap[2][icolor] = (JSAMPLE)((c2total + (total >> 1)) / total);
}

LOCAL(void)
select_colors(j_decompress_ptr cinfo, int desired_colors)
/* Master routine for color selection */
{
        boxptr boxlist;
        int numboxes;
        int i;

        /* Allocate workspace for box list */
        boxlist = (boxptr)(*cinfo->mem->alloc_small)(
                (j_common_ptr)cinfo, JPOOL_IMAGE, desired_colors * SIZEOF(box));
        /* Initialize one box containing whole space */
        numboxes = 1;
        boxlist[0].c0min = 0;
        boxlist[0].c0max = MAXJSAMPLE >> C0_SHIFT;
        boxlist[0].c1min = 0;
        boxlist[0].c1max = MAXJSAMPLE >> C1_SHIFT;
        boxlist[0].c2min = 0;
        boxlist[0].c2max = MAXJSAMPLE >> C2_SHIFT;
        /* Shrink it to actually-used volume and set its statistics */
        update_box(cinfo, &boxlist[0]);
        /* Perform median-cut to produce final box list */
        numboxes = median_cut(cinfo, boxlist, numboxes, desired_colors);
        /* Compute the representative color for each box, fill colormap */
        for (i = 0; i < numboxes; i++) compute_color(cinfo, &boxlist[i], i);
        cinfo->actual_number_of_colors = numboxes;
        TRACEMS1(cinfo, 1, JTRC_QUANT_SELECTED, numboxes);
}

/*
 * These routines are concerned with the time-critical task of mapping input
 * colors to the nearest color in the selected colormap.
 *
 * We re-use the histogram space as an "inverse color map", essentially a
 * cache for the results of nearest-color searches.  All colors within a
 * histogram cell will be mapped to the same colormap entry, namely the one
 * closest to the cell's center.  This may not be quite the closest entry to
 * the actual input color, but it's almost as good.  A zero in the cache
 * indicates we haven't found the nearest color for that cell yet; the array
 * is cleared to zeroes before starting the mapping pass.  When we find the
 * nearest color for a cell, its colormap index plus one is recorded in the
 * cache for future use.  The pass2 scanning routines call fill_inverse_cmap
 * when they need to use an unfilled entry in the cache.
 *
 * Our method of efficiently finding nearest colors is based on the "locally
 * sorted search" idea described by Heckbert and on the incremental distance
 * calculation described by Spencer W. Thomas in chapter III.1 of Graphics
 * Gems II (James Arvo, ed.  Academic Press, 1991).  Thomas points out that
 * the distances from a given colormap entry to each cell of the histogram can
 * be computed quickly using an incremental method: the differences between
 * distances to adjacent cells themselves differ by a constant.  This allows a
 * fairly fast implementation of the "brute force" approach of computing the
 * distance from every colormap entry to every histogram cell.  Unfortunately,
 * it needs a work array to hold the best-distance-so-far for each histogram
 * cell (because the inner loop has to be over cells, not colormap entries).
 * The work array elements have to be INT32s, so the work array would need
 * 256Kb at our recommended precision.  This is not feasible in DOS machines.
 *
 * To get around these problems, we apply Thomas' method to compute the
 * nearest colors for only the cells within a small subbox of the histogram.
 * The work array need be only as big as the subbox, so the memory usage
 * problem is solved.  Furthermore, we need not fill subboxes that are never
 * referenced in pass2; many images use only part of the color gamut, so a
 * fair amount of work is saved.  An additional advantage of this
 * approach is that we can apply Heckbert's locality criterion to quickly
 * eliminate colormap entries that are far away from the subbox; typically
 * three-fourths of the colormap entries are rejected by Heckbert's criterion,
 * and we need not compute their distances to individual cells in the subbox.
 * The speed of this approach is heavily influenced by the subbox size: too
 * small means too much overhead, too big loses because Heckbert's criterion
 * can't eliminate as many colormap entries.  Empirically the best subbox
 * size seems to be about 1/512th of the histogram (1/8th in each direction).
 *
 * Thomas' article also describes a refined method which is asymptotically
 * faster than the brute-force method, but it is also far more complex and
 * cannot efficiently be applied to small subboxes.  It is therefore not
 * useful for programs intended to be portable to DOS machines.  On machines
 * with plenty of memory, filling the whole histogram in one shot with Thomas'
 * refined method might be faster than the present code --- but then again,
 * it might not be any faster, and it's certainly more complicated.
 */

/* log2(histogram cells in update box) for each axis; this can be adjusted */
#define BOX_C0_LOG (HIST_C0_BITS - 3)
#define BOX_C1_LOG (HIST_C1_BITS - 3)
#define BOX_C2_LOG (HIST_C2_BITS - 3)

#define BOX_C0_ELEMS (1 << BOX_C0_LOG) /* # of hist cells in update box */
#define BOX_C1_ELEMS (1 << BOX_C1_LOG)
#define BOX_C2_ELEMS (1 << BOX_C2_LOG)

#define BOX_C0_SHIFT (C0_SHIFT + BOX_C0_LOG)
#define BOX_C1_SHIFT (C1_SHIFT + BOX_C1_LOG)
#define BOX_C2_SHIFT (C2_SHIFT + BOX_C2_LOG)

/*
 * The next three routines implement inverse colormap filling.  They could
 * all be folded into one big routine, but splitting them up this way saves
 * some stack space (the mindist[] and bestdist[] arrays need not coexist)
 * and may allow some compilers to produce better code by registerizing more
 * inner-loop variables.
 */

LOCAL(int)
find_nearby_colors(
        j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
        JSAMPLE colorlist[])
/* Locate the colormap entries close enough to an update box to be candidates
 * for the nearest entry to some cell(s) in the update box.  The update box
 * is specified by the center coordinates of its first cell.  The number of
 * candidate colormap entries is returned, and their colormap indexes are
 * placed in colorlist[].
 * This routine uses Heckbert's "locally sorted search" criterion to select
 * the colors that need further consideration.
 */
{
        int numcolors = cinfo->actual_number_of_colors;
        int maxc0, maxc1, maxc2;
        int centerc0, centerc1, centerc2;
        int i, x, ncolors;
        INT32 minmaxdist, min_dist, max_dist, tdist;
        INT32 mindist[MAXNUMCOLORS]; /* min distance to colormap entry i */

        /* Compute true coordinates of update box's upper corner and center.
         * Actually we compute the coordinates of the center of the upper-corner
         * histogram cell, which are the upper bounds of the volume we care about.
         * Note that since ">>" rounds down, the "center" values may be closer to
         * min than to max; hence comparisons to them must be "<=", not "<".
         */
        maxc0 = minc0 + ((1 << BOX_C0_SHIFT) - (1 << C0_SHIFT));
        centerc0 = (minc0 + maxc0) >> 1;
        maxc1 = minc1 + ((1 << BOX_C1_SHIFT) - (1 << C1_SHIFT));
        centerc1 = (minc1 + maxc1) >> 1;
        maxc2 = minc2 + ((1 << BOX_C2_SHIFT) - (1 << C2_SHIFT));
        centerc2 = (minc2 + maxc2) >> 1;

        /* For each color in colormap, find:
         *  1. its minimum squared-distance to any point in the update box
         *     (zero if color is within update box);
         *  2. its maximum squared-distance to any point in the update box.
         * Both of these can be found by considering only the corners of the box.
         * We save the minimum distance for each color in mindist[];
         * only the smallest maximum distance is of interest.
         */
        minmaxdist = 0x7FFFFFFFL;

        for (i = 0; i < numcolors; i++)
        {
                /* We compute the squared-c0-distance term, then add in the other two.
                 */
                x = GETJSAMPLE(cinfo->colormap[0][i]);
                if (x < minc0)
                {
                        tdist = (x - minc0) * C0_SCALE;
                        min_dist = tdist * tdist;
                        tdist = (x - maxc0) * C0_SCALE;
                        max_dist = tdist * tdist;
                }
                else if (x > maxc0)
                {
                        tdist = (x - maxc0) * C0_SCALE;
                        min_dist = tdist * tdist;
                        tdist = (x - minc0) * C0_SCALE;
                        max_dist = tdist * tdist;
                }
                else
                {
                        /* within cell range so no contribution to min_dist */
                        min_dist = 0;
                        if (x <= centerc0)
                        {
                                tdist = (x - maxc0) * C0_SCALE;
                                max_dist = tdist * tdist;
                        }
                        else
                        {
                                tdist = (x - minc0) * C0_SCALE;
                                max_dist = tdist * tdist;
                        }
                }

                x = GETJSAMPLE(cinfo->colormap[1][i]);
                if (x < minc1)
                {
                        tdist = (x - minc1) * C1_SCALE;
                        min_dist += tdist * tdist;
                        tdist = (x - maxc1) * C1_SCALE;
                        max_dist += tdist * tdist;
                }
                else if (x > maxc1)
                {
                        tdist = (x - maxc1) * C1_SCALE;
                        min_dist += tdist * tdist;
                        tdist = (x - minc1) * C1_SCALE;
                        max_dist += tdist * tdist;
                }
                else
                {
                        /* within cell range so no contribution to min_dist */
                        if (x <= centerc1)
                        {
                                tdist = (x - maxc1) * C1_SCALE;
                                max_dist += tdist * tdist;
                        }
                        else
                        {
                                tdist = (x - minc1) * C1_SCALE;
                                max_dist += tdist * tdist;
                        }
                }

                x = GETJSAMPLE(cinfo->colormap[2][i]);
                if (x < minc2)
                {
                        tdist = (x - minc2) * C2_SCALE;
                        min_dist += tdist * tdist;
                        tdist = (x - maxc2) * C2_SCALE;
                        max_dist += tdist * tdist;
                }
                else if (x > maxc2)
                {
                        tdist = (x - maxc2) * C2_SCALE;
                        min_dist += tdist * tdist;
                        tdist = (x - minc2) * C2_SCALE;
                        max_dist += tdist * tdist;
                }
                else
                {
                        /* within cell range so no contribution to min_dist */
                        if (x <= centerc2)
                        {
                                tdist = (x - maxc2) * C2_SCALE;
                                max_dist += tdist * tdist;
                        }
                        else
                        {
                                tdist = (x - minc2) * C2_SCALE;
                                max_dist += tdist * tdist;
                        }
                }

                mindist[i] = min_dist; /* save away the results */
                if (max_dist < minmaxdist) minmaxdist = max_dist;
        }

        /* Now we know that no cell in the update box is more than minmaxdist
         * away from some colormap entry.  Therefore, only colors that are
         * within minmaxdist of some part of the box need be considered.
         */
        ncolors = 0;
        for (i = 0; i < numcolors; i++)
        {
                if (mindist[i] <= minmaxdist) colorlist[ncolors++] = (JSAMPLE)i;
        }
        return ncolors;
}

LOCAL(void)
find_best_colors(
        j_decompress_ptr cinfo, int minc0, int minc1, int minc2, int numcolors,
        JSAMPLE colorlist[], JSAMPLE bestcolor[])
/* Find the closest colormap entry for each cell in the update box,
 * given the list of candidate colors prepared by find_nearby_colors.
 * Return the indexes of the closest entries in the bestcolor[] array.
 * This routine uses Thomas' incremental distance calculation method to
 * find the distance from a colormap entry to successive cells in the box.
 */
{
        int ic0, ic1, ic2;
        int i, icolor;
        INT32* bptr; /* pointer into bestdist[] array */
        JSAMPLE* cptr; /* pointer into bestcolor[] array */
        INT32 dist0, dist1; /* initial distance values */
        INT32 dist2; /* current distance in inner loop */
        INT32 xx0, xx1; /* distance increments */
        INT32 xx2;
        INT32 inc0, inc1, inc2; /* initial values for increments */
        /* This array holds the distance to the nearest-so-far color for each cell
         */
        INT32 bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];

        /* Initialize best-distance for each cell of the update box */
        bptr = bestdist;
        for (i = BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS - 1; i >= 0; i--)
                *bptr++ = 0x7FFFFFFFL;

/* For each color selected by find_nearby_colors,
 * compute its distance to the center of each cell in the box.
 * If that's less than best-so-far, update best distance and color number.
 */

/* Nominal steps between cell centers ("x" in Thomas article) */
#define STEP_C0 ((1 << C0_SHIFT) * C0_SCALE)
#define STEP_C1 ((1 << C1_SHIFT) * C1_SCALE)
#define STEP_C2 ((1 << C2_SHIFT) * C2_SCALE)

        for (i = 0; i < numcolors; i++)
        {
                icolor = GETJSAMPLE(colorlist[i]);
                /* Compute (square of) distance from minc0/c1/c2 to this color */
                inc0 = (minc0 - GETJSAMPLE(cinfo->colormap[0][icolor])) * C0_SCALE;
                dist0 = inc0 * inc0;
                inc1 = (minc1 - GETJSAMPLE(cinfo->colormap[1][icolor])) * C1_SCALE;
                dist0 += inc1 * inc1;
                inc2 = (minc2 - GETJSAMPLE(cinfo->colormap[2][icolor])) * C2_SCALE;
                dist0 += inc2 * inc2;
                /* Form the initial difference increments */
                inc0 = inc0 * (2 * STEP_C0) + STEP_C0 * STEP_C0;
                inc1 = inc1 * (2 * STEP_C1) + STEP_C1 * STEP_C1;
                inc2 = inc2 * (2 * STEP_C2) + STEP_C2 * STEP_C2;
                /* Now loop over all cells in box, updating distance per Thomas method
                 */
                bptr = bestdist;
                cptr = bestcolor;
                xx0 = inc0;
                for (ic0 = BOX_C0_ELEMS - 1; ic0 >= 0; ic0--)
                {
                        dist1 = dist0;
                        xx1 = inc1;
                        for (ic1 = BOX_C1_ELEMS - 1; ic1 >= 0; ic1--)
                        {
                                dist2 = dist1;
                                xx2 = inc2;
                                for (ic2 = BOX_C2_ELEMS - 1; ic2 >= 0; ic2--)
                                {
                                        if (dist2 < *bptr)
                                        {
                                                *bptr = dist2;
                                                *cptr = (JSAMPLE)icolor;
                                        }
                                        dist2 += xx2;
                                        xx2 += 2 * STEP_C2 * STEP_C2;
                                        bptr++;
                                        cptr++;
                                }
                                dist1 += xx1;
                                xx1 += 2 * STEP_C1 * STEP_C1;
                        }
                        dist0 += xx0;
                        xx0 += 2 * STEP_C0 * STEP_C0;
                }
        }
}

LOCAL(void)
fill_inverse_cmap(j_decompress_ptr cinfo, int c0, int c1, int c2)
/* Fill the inverse-colormap entries in the update box that contains */
/* histogram cell c0/c1/c2.  (Only that one cell MUST be filled, but */
/* we can fill as many others as we wish.) */
{
        my_cquantize_ptr cquantize = (my_cquantize_ptr)cinfo->cquantize;
        hist3d histogram = cquantize->histogram;
        int minc0, minc1, minc2; /* lower left corner of update box */
        int ic0, ic1, ic2;
        JSAMPLE* cptr; /* pointer into bestcolor[] array */
        histptr cachep; /* pointer into main cache array */
        /* This array lists the candidate colormap indexes. */
        JSAMPLE colorlist[MAXNUMCOLORS];
        int numcolors; /* number of candidate colors */
        /* This array holds the actually closest colormap index for each cell. */
        JSAMPLE bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];

        /* Convert cell coordinates to update box ID */
        c0 >>= BOX_C0_LOG;
        c1 >>= BOX_C1_LOG;
        c2 >>= BOX_C2_LOG;

        /* Compute true coordinates of update box's origin corner.
         * Actually we compute the coordinates of the center of the corner
         * histogram cell, which are the lower bounds of the volume we care about.
         */
        minc0 = (c0 << BOX_C0_SHIFT) + ((1 << C0_SHIFT) >> 1);
        minc1 = (c1 << BOX_C1_SHIFT) + ((1 << C1_SHIFT) >> 1);
        minc2 = (c2 << BOX_C2_SHIFT) + ((1 << C2_SHIFT) >> 1);

        /* Determine which colormap entries are close enough to be candidates
         * for the nearest entry to some cell in the update box.
         */
        numcolors = find_nearby_colors(cinfo, minc0, minc1, minc2, colorlist);

        /* Determine the actually nearest colors. */
        find_best_colors(
                cinfo, minc0, minc1, minc2, numcolors, colorlist, bestcolor);

        /* Save the best color numbers (plus 1) in the main cache array */
        c0 <<= BOX_C0_LOG; /* convert ID back to base cell indexes */
        c1 <<= BOX_C1_LOG;
        c2 <<= BOX_C2_LOG;
        cptr = bestcolor;
        for (ic0 = 0; ic0 < BOX_C0_ELEMS; ic0++)
        {
                for (ic1 = 0; ic1 < BOX_C1_ELEMS; ic1++)
                {
                        cachep = &histogram[c0 + ic0][c1 + ic1][c2];
                        for (ic2 = 0; ic2 < BOX_C2_ELEMS; ic2++)
                        {
                                *cachep++ = (histcell)(GETJSAMPLE(*cptr++) + 1);
                        }
                }
        }
}

/*
 * Map some rows of pixels to the output colormapped representation.
 */

METHODDEF(void)
pass2_no_dither(
        j_decompress_ptr cinfo, JSAMPARRAY input_buf, JSAMPARRAY output_buf,
        int num_rows)
/* This version performs no dithering */
{
        my_cquantize_ptr cquantize = (my_cquantize_ptr)cinfo->cquantize;
        hist3d histogram = cquantize->histogram;
        JSAMPROW inptr, outptr;
        histptr cachep;
        int c0, c1, c2;
        int row;
        JDIMENSION col;
        JDIMENSION width = cinfo->output_width;

        for (row = 0; row < num_rows; row++)
        {
                inptr = input_buf[row];
                outptr = output_buf[row];
                for (col = width; col > 0; col--)
                {
                        /* get pixel value and index into the cache */
                        c0 = GETJSAMPLE(*inptr++) >> C0_SHIFT;
                        c1 = GETJSAMPLE(*inptr++) >> C1_SHIFT;
                        c2 = GETJSAMPLE(*inptr++) >> C2_SHIFT;
                        cachep = &histogram[c0][c1][c2];
                        /* If we have not seen this color before, find nearest colormap
                         * entry */
                        /* and update the cache */
                        if (*cachep == 0) fill_inverse_cmap(cinfo, c0, c1, c2);
                        /* Now emit the colormap index for this cell */
                        *outptr++ = (JSAMPLE)(*cachep - 1);
                }
        }
}

METHODDEF(void)
pass2_fs_dither(
        j_decompress_ptr cinfo, JSAMPARRAY input_buf, JSAMPARRAY output_buf,
        int num_rows)
/* This version performs Floyd-Steinberg dithering */
{
        my_cquantize_ptr cquantize = (my_cquantize_ptr)cinfo->cquantize;
        hist3d histogram = cquantize->histogram;
        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 */
        FSERRPTR errorptr; /* => fserrors[] at column before current */
        JSAMPROW inptr; /* => current input pixel */
        JSAMPROW outptr; /* => current output pixel */
        histptr cachep;
        int dir; /* +1 or -1 depending on direction */
        int dir3; /* 3*dir, for advancing inptr & errorptr */
        int row;
        JDIMENSION col;
        JDIMENSION width = cinfo->output_width;
        JSAMPLE* range_limit = cinfo->sample_range_limit;
        int* error_limit = cquantize->error_limiter;
        JSAMPROW colormap0 = cinfo->colormap[0];
        JSAMPROW colormap1 = cinfo->colormap[1];
        JSAMPROW colormap2 = cinfo->colormap[2];
        SHIFT_TEMPS

        for (row = 0; row < num_rows; row++)
        {
                inptr = input_buf[row];
                outptr = output_buf[row];
                if (cquantize->on_odd_row)
                {
                        /* work right to left in this row */
                        inptr += (width - 1) * 3; /* so point to rightmost pixel */
                        outptr += width - 1;
                        dir = -1;
                        dir3 = -3;
                        errorptr = cquantize->fserrors +
                                           (width + 1) * 3; /* => entry after last column */
                        cquantize->on_odd_row = FALSE; /* flip for next time */
                }
                else
                {
                        /* work left to right in this row */
                        dir = 1;
                        dir3 = 3;
                        errorptr =
                                cquantize->fserrors; /* => entry before first real column */
                        cquantize->on_odd_row = TRUE; /* flip for next time */
                }
                /* Preset error values: no error propagated to first pixel from left */
                cur0 = cur1 = cur2 = 0;
                /* and no error propagated to row below yet */
                belowerr0 = belowerr1 = belowerr2 = 0;
                bpreverr0 = bpreverr1 = bpreverr2 = 0;

                for (col = width; col > 0; col--)
                {
                        /* curN holds the error propagated from the previous pixel on the
                         * current line.  Add the error propagated from the previous line
                         * to form the complete error correction term for this pixel, and
                         * round the error term (which is expressed * 16) to an integer.
                         * RIGHT_SHIFT rounds towards minus infinity, so adding 8 is correct
                         * for either sign of the error value.
                         * Note: errorptr points to *previous* column's array entry.
                         */
                        cur0 = RIGHT_SHIFT(cur0 + errorptr[dir3 + 0] + 8, 4);
                        cur1 = RIGHT_SHIFT(cur1 + errorptr[dir3 + 1] + 8, 4);
                        cur2 = RIGHT_SHIFT(cur2 + errorptr[dir3 + 2] + 8, 4);
                        /* Limit the error using transfer function set by init_error_limit.
                         * See comments with init_error_limit for rationale.
                         */
                        cur0 = error_limit[cur0];
                        cur1 = error_limit[cur1];
                        cur2 = error_limit[cur2];
                        /* Form pixel value + error, and range-limit to 0..MAXJSAMPLE.
                         * The maximum error is +- MAXJSAMPLE (or less with error limiting);
                         * this sets the required size of the range_limit array.
                         */
                        cur0 += GETJSAMPLE(inptr[0]);
                        cur1 += GETJSAMPLE(inptr[1]);
                        cur2 += GETJSAMPLE(inptr[2]);
                        cur0 = GETJSAMPLE(range_limit[cur0]);
                        cur1 = GETJSAMPLE(range_limit[cur1]);
                        cur2 = GETJSAMPLE(range_limit[cur2]);
                        /* Index into the cache with adjusted pixel value */
                        cachep = &histogram[cur0 >> C0_SHIFT][cur1 >> C1_SHIFT]
                                                           [cur2 >> C2_SHIFT];
                        /* If we have not seen this color before, find nearest colormap */
                        /* entry and update the cache */
                        if (*cachep == 0)
                                fill_inverse_cmap(
                                        cinfo, cur0 >> C0_SHIFT, cur1 >> C1_SHIFT,
                                        cur2 >> C2_SHIFT);
                        /* Now emit the colormap index for this cell */
                        {
                                int pixcode = *cachep - 1;
                                *outptr = (JSAMPLE)pixcode;
                                /* Compute representation error for this pixel */
                                cur0 -= GETJSAMPLE(colormap0[pixcode]);
                                cur1 -= GETJSAMPLE(colormap1[pixcode]);
                                cur2 -= GETJSAMPLE(colormap2[pixcode]);
                        }
                        /* Compute error fractions to be propagated to adjacent pixels.
                         * Add these into the running sums, and simultaneously shift the
                         * next-line error sums left by 1 column.
                         */
                        {
                                LOCFSERROR bnexterr, delta;

                                bnexterr = cur0; /* Process component 0 */
                                delta = cur0 * 2;
                                cur0 += delta; /* form error * 3 */
                                errorptr[0] = (FSERROR)(bpreverr0 + cur0);
                                cur0 += delta; /* form error * 5 */
                                bpreverr0 = belowerr0 + cur0;
                                belowerr0 = bnexterr;
                                cur0 += delta; /* form error * 7 */
                                bnexterr = cur1; /* Process component 1 */
                                delta = cur1 * 2;
                                cur1 += delta; /* form error * 3 */
                                errorptr[1] = (FSERROR)(bpreverr1 + cur1);
                                cur1 += delta; /* form error * 5 */
                                bpreverr1 = belowerr1 + cur1;
                                belowerr1 = bnexterr;
                                cur1 += delta; /* form error * 7 */
                                bnexterr = cur2; /* Process component 2 */
                                delta = cur2 * 2;
                                cur2 += delta; /* form error * 3 */
                                errorptr[2] = (FSERROR)(bpreverr2 + cur2);
                                cur2 += delta; /* form error * 5 */
                                bpreverr2 = belowerr2 + cur2;
                                belowerr2 = bnexterr;
                                cur2 += delta; /* form error * 7 */
                        }
                        /* At this point curN contains the 7/16 error value to be propagated
                         * to the next pixel on the current line, and all the errors for the
                         * next line have been shifted over.  We are therefore ready to move
                         * on.
                         */
                        inptr += dir3; /* Advance pixel pointers to next column */
                        outptr += dir;
                        errorptr += dir3; /* advance errorptr to current column */
                }
                /* Post-loop cleanup: we must unload the final error values into the
                 * final fserrors[] entry.  Note we need not unload belowerrN because
                 * it is for the dummy column before or after the actual array.
                 */
                errorptr[0] = (FSERROR)bpreverr0; /* unload prev errs into array */
                errorptr[1] = (FSERROR)bpreverr1;
                errorptr[2] = (FSERROR)bpreverr2;
        }
}

/*
 * Initialize the error-limiting transfer function (lookup table).
 * The raw F-S error computation can potentially compute error values of up to
 * +- MAXJSAMPLE.  But we want the maximum correction applied to a pixel to be
 * much less, otherwise obviously wrong pixels will be created.  (Typical
 * effects include weird fringes at color-area boundaries, isolated bright
 * pixels in a dark area, etc.)  The standard advice for avoiding this problem
 * is to ensure that the "corners" of the color cube are allocated as output
 * colors; then repeated errors in the same direction cannot cause cascading
 * error buildup.  However, that only prevents the error from getting
 * completely out of hand; Aaron Giles reports that error limiting improves
 * the results even with corner colors allocated.
 * A simple clamping of the error values to about +- MAXJSAMPLE/8 works pretty
 * well, but the smoother transfer function used below is even better.  Thanks
 * to Aaron Giles for this idea.
 */

LOCAL(void)
init_error_limit(j_decompress_ptr cinfo)
/* Allocate and fill in the error_limiter table */
{
        my_cquantize_ptr cquantize = (my_cquantize_ptr)cinfo->cquantize;
        int* table;
        int in, out;

        table = (int*)(*cinfo->mem->alloc_small)(
                (j_common_ptr)cinfo, JPOOL_IMAGE, (MAXJSAMPLE * 2 + 1) * SIZEOF(int));
        table += MAXJSAMPLE; /* so can index -MAXJSAMPLE .. +MAXJSAMPLE */
        cquantize->error_limiter = table;

#define STEPSIZE ((MAXJSAMPLE + 1) / 16)
        /* Map errors 1:1 up to +- MAXJSAMPLE/16 */
        out = 0;
        for (in = 0; in < STEPSIZE; in++, out++)
        {
                table[in] = out;
                table[-in] = -out;
        }
        /* Map errors 1:2 up to +- 3*MAXJSAMPLE/16 */
        for (; in < STEPSIZE * 3; in++, out += (in & 1) ? 0 : 1)
        {
                table[in] = out;
                table[-in] = -out;
        }
        /* Clamp the rest to final out value (which is (MAXJSAMPLE+1)/8) */
        for (; in <= MAXJSAMPLE; in++)
        {
                table[in] = out;
                table[-in] = -out;
        }
#undef STEPSIZE
}

/*
 * Finish up at the end of each pass.
 */

METHODDEF(void)
finish_pass1(j_decompress_ptr cinfo)
{
        my_cquantize_ptr cquantize = (my_cquantize_ptr)cinfo->cquantize;

        /* Select the representative colors and fill in cinfo->colormap */
        cinfo->colormap = cquantize->sv_colormap;
        select_colors(cinfo, cquantize->desired);
        /* Force next pass to zero the color index table */
        cquantize->needs_zeroed = TRUE;
}

METHODDEF(void)
finish_pass2(j_decompress_ptr) { /* no work */}
/*
 * Initialize for each processing pass.
 */

METHODDEF(void)
start_pass_2_quant(j_decompress_ptr cinfo, boolean is_pre_scan)
{
        my_cquantize_ptr cquantize = (my_cquantize_ptr)cinfo->cquantize;
        hist3d histogram = cquantize->histogram;
        int i;

        /* Only F-S dithering or no dithering is supported. */
        /* If user asks for ordered dither, give him F-S. */
        if (cinfo->dither_mode != JDITHER_NONE) cinfo->dither_mode = JDITHER_FS;

        if (is_pre_scan)
        {
                /* Set up method pointers */
                cquantize->pub.color_quantize = prescan_quantize;
                cquantize->pub.finish_pass = finish_pass1;
                cquantize->needs_zeroed = TRUE; /* Always zero histogram */
        }
        else
        {
                /* Set up method pointers */
                if (cinfo->dither_mode == JDITHER_FS)
                        cquantize->pub.color_quantize = pass2_fs_dither;
                else
                        cquantize->pub.color_quantize = pass2_no_dither;
                cquantize->pub.finish_pass = finish_pass2;

                /* Make sure color count is acceptable */
                i = cinfo->actual_number_of_colors;
                if (i < 1) ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 1);
                if (i > MAXNUMCOLORS)
                        ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);

                if (cinfo->dither_mode == JDITHER_FS)
                {
                        size_t arraysize =
                                (size_t)((cinfo->output_width + 2) * (3 * SIZEOF(FSERROR)));
                        /* Allocate Floyd-Steinberg workspace if we didn't already. */
                        if (cquantize->fserrors == nullptr)
                                cquantize->fserrors = (FSERRPTR)(*cinfo->mem->alloc_large)(
                                        (j_common_ptr)cinfo, JPOOL_IMAGE, arraysize);
                        /* Initialize the propagated errors to zero. */
                        jzero_far((void FAR*)cquantize->fserrors, arraysize);
                        /* Make the error-limit table if we didn't already. */
                        if (cquantize->error_limiter == nullptr) init_error_limit(cinfo);
                        cquantize->on_odd_row = FALSE;
                }
        }
        /* Zero the histogram or inverse color map, if necessary */
        if (cquantize->needs_zeroed)
        {
                for (i = 0; i < HIST_C0_ELEMS; i++)
                {
                        jzero_far(
                                (void FAR*)histogram[i],
                                HIST_C1_ELEMS * HIST_C2_ELEMS * SIZEOF(histcell));
                }
                cquantize->needs_zeroed = FALSE;
        }
}

/*
 * Switch to a new external colormap between output passes.
 */

METHODDEF(void)
new_color_map_2_quant(j_decompress_ptr cinfo)
{
        my_cquantize_ptr cquantize = (my_cquantize_ptr)cinfo->cquantize;

        /* Reset the inverse color map */
        cquantize->needs_zeroed = TRUE;
}

/*
 * Module initialization routine for 2-pass color quantization.
 */

GLOBAL(void)
jinit_2pass_quantizer(j_decompress_ptr cinfo)
{
        my_cquantize_ptr cquantize;
        int i;

        cquantize = (my_cquantize_ptr)(*cinfo->mem->alloc_small)(
                (j_common_ptr)cinfo, JPOOL_IMAGE, SIZEOF(my_cquantizer));
        cinfo->cquantize = (struct jpeg_color_quantizer*)cquantize;
        cquantize->pub.start_pass = start_pass_2_quant;
        cquantize->pub.new_color_map = new_color_map_2_quant;
        cquantize->fserrors = nullptr; /* flag optional arrays not allocated */
        cquantize->error_limiter = nullptr;

        /* Make sure jdmaster didn't give me a case I can't handle */
        if (cinfo->out_color_components != 3) ERREXIT(cinfo, JERR_NOTIMPL);

        /* Allocate the histogram/inverse colormap storage */
        cquantize->histogram = (hist3d)(*cinfo->mem->alloc_small)(
                (j_common_ptr)cinfo, JPOOL_IMAGE, HIST_C0_ELEMS * SIZEOF(hist2d));
        for (i = 0; i < HIST_C0_ELEMS; i++)
        {
                cquantize->histogram[i] = (hist2d)(*cinfo->mem->alloc_large)(
                        (j_common_ptr)cinfo, JPOOL_IMAGE,
                        HIST_C1_ELEMS * HIST_C2_ELEMS * SIZEOF(histcell));
        }
        cquantize->needs_zeroed = TRUE; /* histogram is garbage now */

        /* Allocate storage for the completed colormap, if required.
         * We do this now since it is FAR storage and may affect
         * the memory manager's space calculations.
         */
        if (cinfo->enable_2pass_quant)
        {
                /* Make sure color count is acceptable */
                int desired = cinfo->desired_number_of_colors;
                /* Lower bound on # of colors ... somewhat arbitrary as long as > 0 */
                if (desired < 8) ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 8);
                /* Make sure colormap indexes can be represented by JSAMPLEs */
                if (desired > MAXNUMCOLORS)
                        ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
                cquantize->sv_colormap = (*cinfo->mem->alloc_sarray)(
                        (j_common_ptr)cinfo, JPOOL_IMAGE, (JDIMENSION)desired,
                        (JDIMENSION)3);
                cquantize->desired = desired;
        }
        else
                cquantize->sv_colormap = nullptr;

        /* Only F-S dithering or no dithering is supported. */
        /* If user asks for ordered dither, give him F-S. */
        if (cinfo->dither_mode != JDITHER_NONE) cinfo->dither_mode = JDITHER_FS;

        /* Allocate Floyd-Steinberg workspace if necessary.
         * This isn't really needed until pass 2, but again it is FAR storage.
         * Although we will cope with a later change in dither_mode,
         * we do not promise to honor max_memory_to_use if dither_mode changes.
         */
        if (cinfo->dither_mode == JDITHER_FS)
        {
                cquantize->fserrors = (FSERRPTR)(*cinfo->mem->alloc_large)(
                        (j_common_ptr)cinfo, JPOOL_IMAGE,
                        (size_t)((cinfo->output_width + 2) * (3 * SIZEOF(FSERROR))));
                /* Might as well create the error-limiting table too. */
                init_error_limit(cinfo);
        }
}

#endif /* QUANT_2PASS_SUPPORTED */