source: liacs/MIR2010/SourceCode/cximage/jpeg/jquant2.c@ 422

/*

 * jquant2.c

 *

 * Copyright (C) 1991-1996, Thomas G. Lane.

 * This file is part of the Independent JPEG Group's software.

 * For conditions of distribution and use, see the accompanying README file.

 *

 * This file contains 2-pass color quantization (color mapping) routines.

 * These routines provide selection of a custom color map for an image,

 * followed by mapping of the image to that color map, with optional

 * Floyd-Steinberg dithering.

 * It is also possible to use just the second pass to map to an arbitrary

 * externally-given color map.

 *

 * Note: ordered dithering is not supported, since there isn't any fast

 * way to compute intercolor distances; it's unclear that ordered dither's

 * fundamental assumptions even hold with an irregularly spaced color map.

 */



#define JPEG_INTERNALS

#include "jinclude.h"

#include "jpeglib.h"



#ifdef QUANT_2PASS_SUPPORTED





/*

 * This module implements the well-known Heckbert paradigm for color

 * quantization.  Most of the ideas used here can be traced back to

 * Heckbert's seminal paper

 *   Heckbert, Paul.  "Color Image Quantization for Frame Buffer Display",

 *   Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304.

 *

 * In the first pass over the image, we accumulate a histogram showing the

 * usage count of each possible color.  To keep the histogram to a reasonable

 * size, we reduce the precision of the input; typical practice is to retain

 * 5 or 6 bits per color, so that 8 or 4 different input values are counted

 * in the same histogram cell.

 *

 * Next, the color-selection step begins with a box representing the whole

 * color space, and repeatedly splits the "largest" remaining box until we

 * have as many boxes as desired colors.  Then the mean color in each

 * remaining box becomes one of the possible output colors.

 * 

 * The second pass over the image maps each input pixel to the closest output

 * color (optionally after applying a Floyd-Steinberg dithering correction).

 * This mapping is logically trivial, but making it go fast enough requires

 * considerable care.

 *

 * Heckbert-style quantizers vary a good deal in their policies for choosing

 * the "largest" box and deciding where to cut it.  The particular policies

 * used here have proved out well in experimental comparisons, but better ones

 * may yet be found.

 *

 * In earlier versions of the IJG code, this module quantized in YCbCr color

 * space, processing the raw upsampled data without a color conversion step.

 * This allowed the color conversion math to be done only once per colormap

 * entry, not once per pixel.  However, that optimization precluded other

 * useful optimizations (such as merging color conversion with upsampling)

 * and it also interfered with desired capabilities such as quantizing to an

 * externally-supplied colormap.  We have therefore abandoned that approach.

 * The present code works in the post-conversion color space, typically RGB.

 *

 * To improve the visual quality of the results, we actually work in scaled

 * RGB space, giving G distances more weight than R, and R in turn more than

 * B.  To do everything in integer math, we must use integer scale factors.

 * The 2/3/1 scale factors used here correspond loosely to the relative

 * weights of the colors in the NTSC grayscale equation.

 * If you want to use this code to quantize a non-RGB color space, you'll

 * probably need to change these scale factors.

 */



#define R_SCALE 2               /* scale R distances by this much */

#define G_SCALE 3               /* scale G distances by this much */

#define B_SCALE 1               /* and B by this much */



/* 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

 * NULL pointer).

 */



METHODDEF(void)

prescan_quantize (j_decompress_ptr cinfo, JSAMPARRAY input_buf,

                  JSAMPARRAY output_buf, int num_rows)

{

  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;

  register JSAMPROW ptr;

  register histptr histp;

  register hist3d histogram = cquantize->histogram;

  int row;

  JDIMENSION col;

  JDIMENSION width = cinfo->output_width;



  for (row = 0; row < num_rows; row++) {

    ptr = input_buf[row];

    for (col = width; col > 0; col--) {

      /* get pixel value and index into the histogram */

      histp = & histogram[GETJSAMPLE(ptr[0]) >> C0_SHIFT]

                         [GETJSAMPLE(ptr[1]) >> C1_SHIFT]

                         [GETJSAMPLE(ptr[2]) >> C2_SHIFT];

      /* increment, check for overflow and undo increment if so. */

      if (++(*histp) <= 0)

        (*histp)--;

      ptr += 3;

    }

  }

}





/*

 * Next we have the really interesting routines: selection of a colormap

 * given the completed histogram.

 * These routines work with a list of "boxes", each representing a rectangular

 * subset of the input color space (to histogram precision).

 */



typedef struct {

  /* The bounds of the box (inclusive); expressed as histogram indexes */

  int c0min, c0max;

  int c1min, c1max;

  int c2min, c2max;

  /* The volume (actually 2-norm) of the box */

  INT32 volume;

  /* The number of nonzero histogram cells within this box */

  long colorcount;

} box;



typedef box * boxptr;





LOCAL(boxptr)

find_biggest_color_pop (boxptr boxlist, int numboxes)

/* Find the splittable box with the largest color population */

/* Returns NULL if no splittable boxes remain */

{

  register boxptr boxp;

  register int i;

  register long maxc = 0;

  boxptr which = NULL;

  

  for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {

    if (boxp->colorcount > maxc && boxp->volume > 0) {

      which = boxp;

      maxc = boxp->colorcount;

    }

  }

  return which;

}





LOCAL(boxptr)

find_biggest_volume (boxptr boxlist, int numboxes)

/* Find the splittable box with the largest (scaled) volume */

/* Returns NULL if no splittable boxes remain */

{

  register boxptr boxp;

  register int i;

  register INT32 maxv = 0;

  boxptr which = NULL;

  

  for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {

    if (boxp->volume > maxv) {

      which = boxp;

      maxv = boxp->volume;

    }

  }

  return which;

}





LOCAL(void)

update_box (j_decompress_ptr cinfo, boxptr boxp)

/* Shrink the min/max bounds of a box to enclose only nonzero elements, */

/* and recompute its volume and population */

{

  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;

  hist3d histogram = cquantize->histogram;

  histptr histp;

  int c0,c1,c2;

  int c0min,c0max,c1min,c1max,c2min,c2max;

  INT32 dist0,dist1,dist2;

  long ccount;

  

  c0min = boxp->c0min;  c0max = boxp->c0max;

  c1min = boxp->c1min;  c1max = boxp->c1max;

  c2min = boxp->c2min;  c2max = boxp->c2max;

  

  if (c0max > c0min)

    for (c0 = c0min; c0 <= c0max; c0++)

      for (c1 = c1min; c1 <= c1max; c1++) {

        histp = & histogram[c0][c1][c2min];

        for (c2 = c2min; c2 <= c2max; c2++)

          if (*histp++ != 0) {

            boxp->c0min = c0min = c0;

            goto have_c0min;

          }

      }

 have_c0min:

  if (c0max > c0min)

    for (c0 = c0max; c0 >= c0min; c0--)

      for (c1 = c1min; c1 <= c1max; c1++) {

        histp = & histogram[c0][c1][c2min];

        for (c2 = c2min; c2 <= c2max; c2++)

          if (*histp++ != 0) {

            boxp->c0max = c0max = c0;

            goto have_c0max;

          }

      }

 have_c0max:

  if (c1max > c1min)

    for (c1 = c1min; c1 <= c1max; c1++)

      for (c0 = c0min; c0 <= c0max; c0++) {

        histp = & histogram[c0][c1][c2min];

        for (c2 = c2min; c2 <= c2max; c2++)

          if (*histp++ != 0) {

            boxp->c1min = c1min = c1;

            goto have_c1min;

          }

      }

 have_c1min:

  if (c1max > c1min)

    for (c1 = c1max; c1 >= c1min; c1--)

      for (c0 = c0min; c0 <= c0max; c0++) {

        histp = & histogram[c0][c1][c2min];

        for (c2 = c2min; c2 <= c2max; c2++)

          if (*histp++ != 0) {

            boxp->c1max = c1max = c1;

            goto have_c1max;

          }

      }

 have_c1max:

  if (c2max > c2min)

    for (c2 = c2min; c2 <= c2max; c2++)

      for (c0 = c0min; c0 <= c0max; c0++) {

        histp = & histogram[c0][c1min][c2];

        for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)

          if (*histp != 0) {

            boxp->c2min = c2min = c2;

            goto have_c2min;

          }

      }

 have_c2min:

  if (c2max > c2min)

    for (c2 = c2max; c2 >= c2min; c2--)

      for (c0 = c0min; c0 <= c0max; c0++) {

        histp = & histogram[c0][c1min][c2];

        for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)

          if (*histp != 0) {

            boxp->c2max = c2max = c2;

            goto have_c2max;

          }

      }

 have_c2max:



  /* Update box volume.

   * We use 2-norm rather than real volume here; this biases the method

   * against making long narrow boxes, and it has the side benefit that

   * a box is splittable iff norm > 0.

   * Since the differences are expressed in histogram-cell units,

   * we have to shift back to JSAMPLE units to get consistent distances;

   * after which, we scale according to the selected distance scale factors.

   */

  dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE;

  dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE;

  dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE;

  boxp->volume = dist0*dist0 + dist1*dist1 + dist2*dist2;

  

  /* Now scan remaining volume of box and compute population */

  ccount = 0;

  for (c0 = c0min; c0 <= c0max; c0++)

    for (c1 = c1min; c1 <= c1max; c1++) {

      histp = & histogram[c0][c1][c2min];

      for (c2 = c2min; c2 <= c2max; c2++, histp++)

        if (*histp != 0) {

          ccount++;

        }

    }

  boxp->colorcount = ccount;

}





LOCAL(int)

median_cut (j_decompress_ptr cinfo, boxptr boxlist, int numboxes,

            int desired_colors)

/* Repeatedly select and split the largest box until we have enough boxes */

{

  int n,lb;

  int c0,c1,c2,cmax;

  register boxptr b1,b2;



  while (numboxes < desired_colors) {

    /* Select box to split.

     * Current algorithm: by population for first half, then by volume.

     */

    if (numboxes*2 <= desired_colors) {

      b1 = find_biggest_color_pop(boxlist, numboxes);

    } else {

      b1 = find_biggest_volume(boxlist, numboxes);

    }

    if (b1 == NULL)             /* no splittable boxes left! */

      break;

    b2 = &boxlist[numboxes];    /* where new box will go */

    /* Copy the color bounds to the new box. */

    b2->c0max = b1->c0max; b2->c1max = b1->c1max; b2->c2max = b1->c2max;

    b2->c0min = b1->c0min; b2->c1min = b1->c1min; b2->c2min = b1->c2min;

    /* Choose which axis to split the box on.

     * Current algorithm: longest scaled axis.

     * See notes in update_box about scaling distances.

     */

    c0 = ((b1->c0max - b1->c0min) << C0_SHIFT) * C0_SCALE;

    c1 = ((b1->c1max - b1->c1min) << C1_SHIFT) * C1_SCALE;

    c2 = ((b1->c2max - b1->c2min) << C2_SHIFT) * C2_SCALE;

    /* We want to break any ties in favor of green, then red, blue last.

     * This code does the right thing for R,G,B or B,G,R color orders only.

     */

#if RGB_RED == 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;

  register INT32 * bptr;        /* pointer into bestdist[] array */

  JSAMPLE * cptr;               /* pointer into bestcolor[] array */

  INT32 dist0, dist1;           /* initial distance values */

  register INT32 dist2;         /* current distance in inner loop */

  INT32 xx0, xx1;               /* distance increments */

  register INT32 xx2;

  INT32 inc0, inc1, inc2;       /* initial values for increments */

  /* This array holds the distance to the nearest-so-far color for each cell */

  INT32 bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];



  /* Initialize best-distance for each cell of the update box */

  bptr = bestdist;

  for (i = BOX_C0_ELEMS*BOX_C1_ELEMS*BOX_C2_ELEMS-1; i >= 0; i--)

    *bptr++ = 0x7FFFFFFFL;

  

  /* For each color selected by find_nearby_colors,

   * compute its distance to the center of each cell in the box.

   * If that's less than best-so-far, update best distance and color number.

   */

  

  /* Nominal steps between cell centers ("x" in Thomas article) */

#define STEP_C0  ((1 << C0_SHIFT) * C0_SCALE)

#define STEP_C1  ((1 << C1_SHIFT) * C1_SCALE)

#define STEP_C2  ((1 << C2_SHIFT) * C2_SCALE)

  

  for (i = 0; i < numcolors; i++) {

    icolor = GETJSAMPLE(colorlist[i]);

    /* Compute (square of) distance from minc0/c1/c2 to this color */

    inc0 = (minc0 - GETJSAMPLE(cinfo->colormap[0][icolor])) * C0_SCALE;

    dist0 = inc0*inc0;

    inc1 = (minc1 - GETJSAMPLE(cinfo->colormap[1][icolor])) * C1_SCALE;

    dist0 += inc1*inc1;

    inc2 = (minc2 - GETJSAMPLE(cinfo->colormap[2][icolor])) * C2_SCALE;

    dist0 += inc2*inc2;

    /* Form the initial difference increments */

    inc0 = inc0 * (2 * STEP_C0) + STEP_C0 * STEP_C0;

    inc1 = inc1 * (2 * STEP_C1) + STEP_C1 * STEP_C1;

    inc2 = inc2 * (2 * STEP_C2) + STEP_C2 * STEP_C2;

    /* Now loop over all cells in box, updating distance per Thomas method */

    bptr = bestdist;

    cptr = bestcolor;

    xx0 = inc0;

    for (ic0 = BOX_C0_ELEMS-1; ic0 >= 0; ic0--) {

      dist1 = dist0;

      xx1 = inc1;

      for (ic1 = BOX_C1_ELEMS-1; ic1 >= 0; ic1--) {

        dist2 = dist1;

        xx2 = inc2;

        for (ic2 = BOX_C2_ELEMS-1; ic2 >= 0; ic2--) {

          if (dist2 < *bptr) {

            *bptr = dist2;

            *cptr = (JSAMPLE) icolor;

          }

          dist2 += xx2;

          xx2 += 2 * STEP_C2 * STEP_C2;

          bptr++;

          cptr++;

        }

        dist1 += xx1;

        xx1 += 2 * STEP_C1 * STEP_C1;

      }

      dist0 += xx0;

      xx0 += 2 * STEP_C0 * STEP_C0;

    }

  }

}





LOCAL(void)

fill_inverse_cmap (j_decompress_ptr cinfo, int c0, int c1, int c2)

/* Fill the inverse-colormap entries in the update box that contains */

/* histogram cell c0/c1/c2.  (Only that one cell MUST be filled, but */

/* we can fill as many others as we wish.) */

{

  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;

  hist3d histogram = cquantize->histogram;

  int minc0, minc1, minc2;      /* lower left corner of update box */

  int ic0, ic1, ic2;

  register JSAMPLE * cptr;      /* pointer into bestcolor[] array */

  register histptr cachep;      /* pointer into main cache array */

  /* This array lists the candidate colormap indexes. */

  JSAMPLE colorlist[MAXNUMCOLORS];

  int numcolors;                /* number of candidate colors */

  /* This array holds the actually closest colormap index for each cell. */

  JSAMPLE bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];



  /* Convert cell coordinates to update box ID */

  c0 >>= BOX_C0_LOG;

  c1 >>= BOX_C1_LOG;

  c2 >>= BOX_C2_LOG;



  /* Compute true coordinates of update box's origin corner.

   * Actually we compute the coordinates of the center of the corner

   * histogram cell, which are the lower bounds of the volume we care about.

   */

  minc0 = (c0 << BOX_C0_SHIFT) + ((1 << C0_SHIFT) >> 1);

  minc1 = (c1 << BOX_C1_SHIFT) + ((1 << C1_SHIFT) >> 1);

  minc2 = (c2 << BOX_C2_SHIFT) + ((1 << C2_SHIFT) >> 1);

  

  /* Determine which colormap entries are close enough to be candidates

   * for the nearest entry to some cell in the update box.

   */

  numcolors = find_nearby_colors(cinfo, minc0, minc1, minc2, colorlist);



  /* Determine the actually nearest colors. */

  find_best_colors(cinfo, minc0, minc1, minc2, numcolors, colorlist,

                   bestcolor);



  /* Save the best color numbers (plus 1) in the main cache array */

  c0 <<= BOX_C0_LOG;            /* convert ID back to base cell indexes */

  c1 <<= BOX_C1_LOG;

  c2 <<= BOX_C2_LOG;

  cptr = bestcolor;

  for (ic0 = 0; ic0 < BOX_C0_ELEMS; ic0++) {

    for (ic1 = 0; ic1 < BOX_C1_ELEMS; ic1++) {

      cachep = & histogram[c0+ic0][c1+ic1][c2];

      for (ic2 = 0; ic2 < BOX_C2_ELEMS; ic2++) {

        *cachep++ = (histcell) (GETJSAMPLE(*cptr++) + 1);

      }

    }

  }

}





/*

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

 */



METHODDEF(void)

pass2_no_dither (j_decompress_ptr cinfo,

                 JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)

/* This version performs no dithering */

{

  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;

  hist3d histogram = cquantize->histogram;

  register JSAMPROW inptr, outptr;

  register histptr cachep;

  register int c0, c1, c2;

  int row;

  JDIMENSION col;

  JDIMENSION width = cinfo->output_width;



  for (row = 0; row < num_rows; row++) {

    inptr = input_buf[row];

    outptr = output_buf[row];

    for (col = width; col > 0; col--) {

      /* get pixel value and index into the cache */

      c0 = GETJSAMPLE(*inptr++) >> C0_SHIFT;

      c1 = GETJSAMPLE(*inptr++) >> C1_SHIFT;

      c2 = GETJSAMPLE(*inptr++) >> C2_SHIFT;

      cachep = & histogram[c0][c1][c2];

      /* If we have not seen this color before, find nearest colormap entry */

      /* and update the cache */

      if (*cachep == 0)

        fill_inverse_cmap(cinfo, c0,c1,c2);

      /* Now emit the colormap index for this cell */

      *outptr++ = (JSAMPLE) (*cachep - 1);

    }

  }

}





METHODDEF(void)

pass2_fs_dither (j_decompress_ptr cinfo,

                 JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)

/* This version performs Floyd-Steinberg dithering */

{

  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;

  hist3d histogram = cquantize->histogram;

  register LOCFSERROR cur0, cur1, cur2; /* current error or pixel value */

  LOCFSERROR belowerr0, belowerr1, belowerr2; /* error for pixel below cur */

  LOCFSERROR bpreverr0, bpreverr1, bpreverr2; /* error for below/prev col */

  register FSERRPTR errorptr;   /* => fserrors[] at column before current */

  JSAMPROW inptr;               /* => current input pixel */

  JSAMPROW outptr;              /* => current output pixel */

  histptr cachep;

  int dir;                      /* +1 or -1 depending on direction */

  int dir3;                     /* 3*dir, for advancing inptr & errorptr */

  int row;

  JDIMENSION col;

  JDIMENSION width = cinfo->output_width;

  JSAMPLE *range_limit = cinfo->sample_range_limit;

  int *error_limit = cquantize->error_limiter;

  JSAMPROW colormap0 = cinfo->colormap[0];

  JSAMPROW colormap1 = cinfo->colormap[1];

  JSAMPROW colormap2 = cinfo->colormap[2];

  SHIFT_TEMPS



  for (row = 0; row < num_rows; row++) {

    inptr = input_buf[row];

    outptr = output_buf[row];

    if (cquantize->on_odd_row) {

      /* work right to left in this row */

      inptr += (width-1) * 3;   /* so point to rightmost pixel */

      outptr += width-1;

      dir = -1;

      dir3 = -3;

      errorptr = cquantize->fserrors + (width+1)*3; /* => entry after last column */

      cquantize->on_odd_row = FALSE; /* flip for next time */

    } else {

      /* work left to right in this row */

      dir = 1;

      dir3 = 3;

      errorptr = cquantize->fserrors; /* => entry before first real column */

      cquantize->on_odd_row = TRUE; /* flip for next time */

    }

    /* Preset error values: no error propagated to first pixel from left */

    cur0 = cur1 = cur2 = 0;

    /* and no error propagated to row below yet */

    belowerr0 = belowerr1 = belowerr2 = 0;

    bpreverr0 = bpreverr1 = bpreverr2 = 0;



    for (col = width; col > 0; col--) {

      /* curN holds the error propagated from the previous pixel on the

       * current line.  Add the error propagated from the previous line

       * to form the complete error correction term for this pixel, and

       * round the error term (which is expressed * 16) to an integer.

       * RIGHT_SHIFT rounds towards minus infinity, so adding 8 is correct

       * for either sign of the error value.

       * Note: errorptr points to *previous* column's array entry.

       */

      cur0 = RIGHT_SHIFT(cur0 + errorptr[dir3+0] + 8, 4);

      cur1 = RIGHT_SHIFT(cur1 + errorptr[dir3+1] + 8, 4);

      cur2 = RIGHT_SHIFT(cur2 + errorptr[dir3+2] + 8, 4);

      /* Limit the error using transfer function set by init_error_limit.

       * See comments with init_error_limit for rationale.

       */

      cur0 = error_limit[cur0];

      cur1 = error_limit[cur1];

      cur2 = error_limit[cur2];

      /* Form pixel value + error, and range-limit to 0..MAXJSAMPLE.

       * The maximum error is +- MAXJSAMPLE (or less with error limiting);

       * this sets the required size of the range_limit array.

       */

      cur0 += GETJSAMPLE(inptr[0]);

      cur1 += GETJSAMPLE(inptr[1]);

      cur2 += GETJSAMPLE(inptr[2]);

      cur0 = GETJSAMPLE(range_limit[cur0]);

      cur1 = GETJSAMPLE(range_limit[cur1]);

      cur2 = GETJSAMPLE(range_limit[cur2]);

      /* Index into the cache with adjusted pixel value */

      cachep = & histogram[cur0>>C0_SHIFT][cur1>>C1_SHIFT][cur2>>C2_SHIFT];

      /* If we have not seen this color before, find nearest colormap */

      /* entry and update the cache */

      if (*cachep == 0)

        fill_inverse_cmap(cinfo, cur0>>C0_SHIFT,cur1>>C1_SHIFT,cur2>>C2_SHIFT);

      /* Now emit the colormap index for this cell */

      { register int pixcode = *cachep - 1;

        *outptr = (JSAMPLE) pixcode;

        /* Compute representation error for this pixel */

        cur0 -= GETJSAMPLE(colormap0[pixcode]);

        cur1 -= GETJSAMPLE(colormap1[pixcode]);

        cur2 -= GETJSAMPLE(colormap2[pixcode]);

      }

      /* Compute error fractions to be propagated to adjacent pixels.

       * Add these into the running sums, and simultaneously shift the

       * next-line error sums left by 1 column.

       */

      { register LOCFSERROR bnexterr, delta;



        bnexterr = cur0;        /* Process component 0 */

        delta = cur0 * 2;

        cur0 += delta;          /* form error * 3 */

        errorptr[0] = (FSERROR) (bpreverr0 + cur0);

        cur0 += delta;          /* form error * 5 */

        bpreverr0 = belowerr0 + cur0;

        belowerr0 = bnexterr;

        cur0 += delta;          /* form error * 7 */

        bnexterr = cur1;        /* Process component 1 */

        delta = cur1 * 2;

        cur1 += delta;          /* form error * 3 */

        errorptr[1] = (FSERROR) (bpreverr1 + cur1);

        cur1 += delta;          /* form error * 5 */

        bpreverr1 = belowerr1 + cur1;

        belowerr1 = bnexterr;

        cur1 += delta;          /* form error * 7 */

        bnexterr = cur2;        /* Process component 2 */

        delta = cur2 * 2;

        cur2 += delta;          /* form error * 3 */

        errorptr[2] = (FSERROR) (bpreverr2 + cur2);

        cur2 += delta;          /* form error * 5 */

        bpreverr2 = belowerr2 + cur2;

        belowerr2 = bnexterr;

        cur2 += delta;          /* form error * 7 */

      }

      /* At this point curN contains the 7/16 error value to be propagated

       * to the next pixel on the current line, and all the errors for the

       * next line have been shifted over.  We are therefore ready to move on.

       */

      inptr += dir3;            /* Advance pixel pointers to next column */

      outptr += dir;

      errorptr += dir3;         /* advance errorptr to current column */

    }

    /* Post-loop cleanup: we must unload the final error values into the

     * final fserrors[] entry.  Note we need not unload belowerrN because

     * it is for the dummy column before or after the actual array.

     */

    errorptr[0] = (FSERROR) bpreverr0; /* unload prev errs into array */

    errorptr[1] = (FSERROR) bpreverr1;

    errorptr[2] = (FSERROR) bpreverr2;

  }

}





/*

 * Initialize the error-limiting transfer function (lookup table).

 * The raw F-S error computation can potentially compute error values of up to

 * +- MAXJSAMPLE.  But we want the maximum correction applied to a pixel to be

 * much less, otherwise obviously wrong pixels will be created.  (Typical

 * effects include weird fringes at color-area boundaries, isolated bright

 * pixels in a dark area, etc.)  The standard advice for avoiding this problem

 * is to ensure that the "corners" of the color cube are allocated as output

 * colors; then repeated errors in the same direction cannot cause cascading

 * error buildup.  However, that only prevents the error from getting

 * completely out of hand; Aaron Giles reports that error limiting improves

 * the results even with corner colors allocated.

 * A simple clamping of the error values to about +- MAXJSAMPLE/8 works pretty

 * well, but the smoother transfer function used below is even better.  Thanks

 * to Aaron Giles for this idea.

 */



LOCAL(void)

init_error_limit (j_decompress_ptr cinfo)

/* Allocate and fill in the error_limiter table */

{

  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;

  int * table;

  int in, out;



  table = (int *) (*cinfo->mem->alloc_small)

    ((j_common_ptr) cinfo, JPOOL_IMAGE, (MAXJSAMPLE*2+1) * SIZEOF(int));

  table += MAXJSAMPLE;          /* so can index -MAXJSAMPLE .. +MAXJSAMPLE */

  cquantize->error_limiter = table;



#define STEPSIZE ((MAXJSAMPLE+1)/16)

  /* Map errors 1:1 up to +- MAXJSAMPLE/16 */

  out = 0;

  for (in = 0; in < STEPSIZE; in++, out++) {

    table[in] = out; table[-in] = -out;

  }

  /* Map errors 1:2 up to +- 3*MAXJSAMPLE/16 */

  for (; in < STEPSIZE*3; in++, out += (in&1) ? 0 : 1) {

    table[in] = out; table[-in] = -out;

  }

  /* Clamp the rest to final out value (which is (MAXJSAMPLE+1)/8) */

  for (; in <= MAXJSAMPLE; in++) {

    table[in] = out; table[-in] = -out;

  }

#undef STEPSIZE

}





/*

 * Finish up at the end of each pass.

 */



METHODDEF(void)

finish_pass1 (j_decompress_ptr cinfo)

{

  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;



  /* Select the representative colors and fill in cinfo->colormap */

  cinfo->colormap = cquantize->sv_colormap;

  select_colors(cinfo, cquantize->desired);

  /* Force next pass to zero the color index table */

  cquantize->needs_zeroed = TRUE;

}





METHODDEF(void)

finish_pass2 (j_decompress_ptr cinfo)

{

  /* no work */

}





/*

 * Initialize for each processing pass.

 */



METHODDEF(void)

start_pass_2_quant (j_decompress_ptr cinfo, boolean is_pre_scan)

{

  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;

  hist3d histogram = cquantize->histogram;

  int i;



  /* Only F-S dithering or no dithering is supported. */

  /* If user asks for ordered dither, give him F-S. */

  if (cinfo->dither_mode != JDITHER_NONE)

    cinfo->dither_mode = JDITHER_FS;



  if (is_pre_scan) {

    /* Set up method pointers */

    cquantize->pub.color_quantize = prescan_quantize;

    cquantize->pub.finish_pass = finish_pass1;

    cquantize->needs_zeroed = TRUE; /* Always zero histogram */

  } else {

    /* Set up method pointers */

    if (cinfo->dither_mode == JDITHER_FS)

      cquantize->pub.color_quantize = pass2_fs_dither;

    else

      cquantize->pub.color_quantize = pass2_no_dither;

    cquantize->pub.finish_pass = finish_pass2;



    /* Make sure color count is acceptable */

    i = cinfo->actual_number_of_colors;

    if (i < 1)

      ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 1);

    if (i > MAXNUMCOLORS)

      ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);



    if (cinfo->dither_mode == JDITHER_FS) {

      size_t arraysize = (size_t) ((cinfo->output_width + 2) *

                                   (3 * SIZEOF(FSERROR)));

      /* Allocate Floyd-Steinberg workspace if we didn't already. */

      if (cquantize->fserrors == NULL)

        cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)

          ((j_common_ptr) cinfo, JPOOL_IMAGE, arraysize);

      /* Initialize the propagated errors to zero. */

      jzero_far((void FAR *) cquantize->fserrors, arraysize);

      /* Make the error-limit table if we didn't already. */

      if (cquantize->error_limiter == NULL)

        init_error_limit(cinfo);

      cquantize->on_odd_row = FALSE;

    }



  }

  /* Zero the histogram or inverse color map, if necessary */

  if (cquantize->needs_zeroed) {

    for (i = 0; i < HIST_C0_ELEMS; i++) {

      jzero_far((void FAR *) histogram[i],

                HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));

    }

    cquantize->needs_zeroed = FALSE;

  }

}





/*

 * Switch to a new external colormap between output passes.

 */



METHODDEF(void)

new_color_map_2_quant (j_decompress_ptr cinfo)

{

  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;



  /* Reset the inverse color map */

  cquantize->needs_zeroed = TRUE;

}





/*

 * Module initialization routine for 2-pass color quantization.

 */



GLOBAL(void)

jinit_2pass_quantizer (j_decompress_ptr cinfo)

{

  my_cquantize_ptr cquantize;

  int i;



  cquantize = (my_cquantize_ptr)

    (*cinfo->mem->alloc_small) ((j_common_ptr) cinfo, JPOOL_IMAGE,

                                SIZEOF(my_cquantizer));

  cinfo->cquantize = (struct jpeg_color_quantizer *) cquantize;

  cquantize->pub.start_pass = start_pass_2_quant;

  cquantize->pub.new_color_map = new_color_map_2_quant;

  cquantize->fserrors = NULL;   /* flag optional arrays not allocated */

  cquantize->error_limiter = NULL;



  /* Make sure jdmaster didn't give me a case I can't handle */

  if (cinfo->out_color_components != 3)

    ERREXIT(cinfo, JERR_NOTIMPL);



  /* Allocate the histogram/inverse colormap storage */

  cquantize->histogram = (hist3d) (*cinfo->mem->alloc_small)

    ((j_common_ptr) cinfo, JPOOL_IMAGE, HIST_C0_ELEMS * SIZEOF(hist2d));

  for (i = 0; i < HIST_C0_ELEMS; i++) {

    cquantize->histogram[i] = (hist2d) (*cinfo->mem->alloc_large)

      ((j_common_ptr) cinfo, JPOOL_IMAGE,

       HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));

  }

  cquantize->needs_zeroed = TRUE; /* histogram is garbage now */



  /* Allocate storage for the completed colormap, if required.

   * We do this now since it is FAR storage and may affect

   * the memory manager's space calculations.

   */

  if (cinfo->enable_2pass_quant) {

    /* Make sure color count is acceptable */

    int desired = cinfo->desired_number_of_colors;

    /* Lower bound on # of colors ... somewhat arbitrary as long as > 0 */

    if (desired < 8)

      ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 8);

    /* Make sure colormap indexes can be represented by JSAMPLEs */

    if (desired > MAXNUMCOLORS)

      ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);

    cquantize->sv_colormap = (*cinfo->mem->alloc_sarray)

      ((j_common_ptr) cinfo,JPOOL_IMAGE, (JDIMENSION) desired, (JDIMENSION) 3);

    cquantize->desired = desired;

  } else

    cquantize->sv_colormap = NULL;



  /* Only F-S dithering or no dithering is supported. */

  /* If user asks for ordered dither, give him F-S. */

  if (cinfo->dither_mode != JDITHER_NONE)

    cinfo->dither_mode = JDITHER_FS;



  /* Allocate Floyd-Steinberg workspace if necessary.

   * This isn't really needed until pass 2, but again it is FAR storage.

   * Although we will cope with a later change in dither_mode,

   * we do not promise to honor max_memory_to_use if dither_mode changes.

   */

  if (cinfo->dither_mode == JDITHER_FS) {

    cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)

      ((j_common_ptr) cinfo, JPOOL_IMAGE,

       (size_t) ((cinfo->output_width + 2) * (3 * SIZEOF(FSERROR))));

    /* Might as well create the error-limiting table too. */

    init_error_limit(cinfo);

  }

}



#endif /* QUANT_2PASS_SUPPORTED */