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 /*

  * jquant2.c

  *

  * This file was part of the Independent JPEG Group's software:

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

  * libjpeg-turbo Modifications:

  * Copyright (C) 2009, D. R. Commander.

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

  *

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

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

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

  * Floyd-Steinberg dithering.

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

  * externally-given color map.

  *

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

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

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

  */

 #define JPEG_INTERNALS

 #include "jinclude.h"

 #include "jpeglib.h"

 #ifdef QUANT_2PASS_SUPPORTED

 /*

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

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

  * Heckbert's seminal paper

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

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

  *

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

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

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

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

  * in the same histogram cell.

  *

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

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

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

  * remaining box becomes one of the possible output colors.

  * 

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

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

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

  * considerable care.

  *

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

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

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

  * may yet be found.

  *

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

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

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

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

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

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

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

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

  *

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

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

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

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

  * weights of the colors in the NTSC grayscale equation.

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

  * probably need to change these scale factors.

  */

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

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

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

 static const int c_scales[3]={R_SCALE, G_SCALE, B_SCALE};

 #define C0_SCALE c_scales[rgb_red[cinfo->out_color_space]]

 #define C1_SCALE c_scales[rgb_green[cinfo->out_color_space]]

 #define C2_SCALE c_scales[rgb_blue[cinfo->out_color_space]]

 /*

  * First we have the histogram data structure and routines for creating it.

  *

  * The number of bits of precision can be adjusted by changing these symbols.

  * We recommend keeping 6 bits for G and 5 each for R and B.

  * If you have plenty of memory and cycles, 6 bits all around gives marginally

  * better results; if you are short of memory, 5 bits all around will save

  * some space but degrade the results.

  * To maintain a fully accurate histogram, we'd need to allocate a "long"

  * (preferably unsigned long) for each cell.  In practice this is overkill;

  * we can get by with 16 bits per cell.  Few of the cell counts will overflow,

  * and clamping those that do overflow to the maximum value will give close-

  * enough results.  This reduces the recommended histogram size from 256Kb

  * to 128Kb, which is a useful savings on PC-class machines.

  * (In the second pass the histogram space is re-used for pixel mapping data;

  * in that capacity, each cell must be able to store zero to the number of

  * desired colors.  16 bits/cell is plenty for that too.)

  * Since the JPEG code is intended to run in small memory model on 80x86

  * machines, we can't just allocate the histogram in one chunk.  Instead

  * of a true 3-D array, we use a row of pointers to 2-D arrays.  Each

  * pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and

  * each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries.  Note that

  * on 80x86 machines, the pointer row is in near memory but the actual

  * arrays are in far memory (same arrangement as we use for image arrays).

  */

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

 /* These will do the right thing for either R,G,B or B,G,R color order,

  * but you may not like the results for other color orders.

  */

 #define HIST_C0_BITS  5		/* bits of precision in R/B histogram */

 #define HIST_C1_BITS  6		/* bits of precision in G histogram */

 #define HIST_C2_BITS  5		/* bits of precision in B/R histogram */

 /* Number of elements along histogram axes. */

 #define HIST_C0_ELEMS  (1<<HIST_C0_BITS)

 #define HIST_C1_ELEMS  (1<<HIST_C1_BITS)

 #define HIST_C2_ELEMS  (1<<HIST_C2_BITS)

 /* These are the amounts to shift an input value to get a histogram index. */

 #define C0_SHIFT  (BITS_IN_JSAMPLE-HIST_C0_BITS)

 #define C1_SHIFT  (BITS_IN_JSAMPLE-HIST_C1_BITS)

 #define C2_SHIFT  (BITS_IN_JSAMPLE-HIST_C2_BITS)

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

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

 typedef histcell hist1d[HIST_C2_ELEMS]; /* typedefs for the array */

 typedef hist1d FAR * hist2d;	/* type for the 2nd-level pointers */

 typedef hist2d * hist3d;	/* type for top-level pointer */

 /* Declarations for Floyd-Steinberg dithering.

  *

  * Errors are accumulated into the array fserrors[], at a resolution of

  * 1/16th of a pixel count.  The error at a given pixel is propagated

  * to its not-yet-processed neighbors using the standard F-S fractions,

  *		...	(here)	7/16

  *		3/16	5/16	1/16

  * We work left-to-right on even rows, right-to-left on odd rows.

  *

  * We can get away with a single array (holding one row's worth of errors)

  * by using it to store the current row's errors at pixel columns not yet

  * processed, but the next row's errors at columns already processed.  We

  * need only a few extra variables to hold the errors immediately around the

  * current column.  (If we are lucky, those variables are in registers, but

  * even if not, they're probably cheaper to access than array elements are.)

  *

  * The fserrors[] array has (#columns + 2) entries; the extra entry at

  * each end saves us from special-casing the first and last pixels.

  * Each entry is three values long, one value for each color component.

  *

  * Note: on a wide image, we might not have enough room in a PC's near data

  * segment to hold the error array; so it is allocated with alloc_large.

  */

 #if BITS_IN_JSAMPLE == 8

 typedef INT16 FSERROR;		/* 16 bits should be enough */

 typedef int LOCFSERROR;		/* use 'int' for calculation temps */

 #else

 typedef INT32 FSERROR;		/* may need more than 16 bits */

 typedef INT32 LOCFSERROR;	/* be sure calculation temps are big enough */

 #endif

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

 /* Private subobject */

 typedef struct {

   struct jpeg_color_quantizer pub; /* public fields */

   /* Space for the eventually created colormap is stashed here */

   JSAMPARRAY sv_colormap;	/* colormap allocated at init time */

   int desired;			/* desired # of colors = size of colormap */

   /* Variables for accumulating image statistics */

   hist3d histogram;		/* pointer to the histogram */

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

   /* Variables for Floyd-Steinberg dithering */

   FSERRPTR fserrors;		/* accumulated errors */

   boolean on_odd_row;		/* flag to remember which row we are on */

   int * error_limiter;		/* table for clamping the applied error */

 } my_cquantizer;

 typedef my_cquantizer * my_cquantize_ptr;

 /*

  * Prescan some rows of pixels.

  * In this module the prescan simply updates the histogram, which has been

  * initialized to zeroes by start_pass.

  * An output_buf parameter is required by the method signature, but no data

  * is actually output (in fact the buffer controller is probably passing a

  * NULL pointer).

  */

 METHODDEF(void)

 prescan_quantize (j_decompress_ptr cinfo, JSAMPARRAY input_buf,

 		  JSAMPARRAY output_buf, int num_rows)

 {

   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;

   register JSAMPROW ptr;

   register histptr histp;

   register hist3d histogram = cquantize->histogram;

   int row;

   JDIMENSION col;

   JDIMENSION width = cinfo->output_width;

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

     ptr = input_buf[row];

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

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

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

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

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

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

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

 	(*histp)--;

       ptr += 3;

     }

   }

 }

 /*

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

  * given the completed histogram.

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

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

  */

 typedef struct {

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

   int c0min, c0max;

   int c1min, c1max;

   int c2min, c2max;

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

   INT32 volume;

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

   long colorcount;

 } box;

 typedef box * boxptr;

 LOCAL(boxptr)

 find_biggest_color_pop (boxptr boxlist, int numboxes)

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

 /* Returns NULL if no splittable boxes remain */

 {

   register boxptr boxp;

   register int i;

   register long maxc = 0;

   boxptr which = NULL;

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

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

       which = boxp;

       maxc = boxp->colorcount;

     }

   }

   return which;

 }

 LOCAL(boxptr)

 find_biggest_volume (boxptr boxlist, int numboxes)

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

 /* Returns NULL if no splittable boxes remain */

 {

   register boxptr boxp;

   register int i;

   register INT32 maxv = 0;

   boxptr which = NULL;

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

     if (boxp->volume > maxv) {

       which = boxp;

       maxv = boxp->volume;

     }

   }

   return which;

 }

 LOCAL(void)

 update_box (j_decompress_ptr cinfo, boxptr boxp)

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

 /* and recompute its volume and population */

 {

   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;

   hist3d histogram = cquantize->histogram;

   histptr histp;

   int c0,c1,c2;

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

   INT32 dist0,dist1,dist2;

   long ccount;

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

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

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

   if (c0max > c0min)

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

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

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

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

 	  if (*histp++ != 0) {

 	    boxp->c0min = c0min = c0;

 	    goto have_c0min;

 	  }

       }

  have_c0min:

   if (c0max > c0min)

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

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

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

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

 	  if (*histp++ != 0) {

 	    boxp->c0max = c0max = c0;

 	    goto have_c0max;

 	  }

       }

  have_c0max:

   if (c1max > c1min)

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

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

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

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

 	  if (*histp++ != 0) {

 	    boxp->c1min = c1min = c1;

 	    goto have_c1min;

 	  }

       }

  have_c1min:

   if (c1max > c1min)

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

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

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

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

 	  if (*histp++ != 0) {

 	    boxp->c1max = c1max = c1;

 	    goto have_c1max;

 	  }

       }

  have_c1max:

   if (c2max > c2min)

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

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

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

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

 	  if (*histp != 0) {

 	    boxp->c2min = c2min = c2;

 	    goto have_c2min;

 	  }

       }

  have_c2min:

   if (c2max > c2min)

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

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

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

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

 	  if (*histp != 0) {

 	    boxp->c2max = c2max = c2;

 	    goto have_c2max;

 	  }

       }

  have_c2max:

   /* Update box volume.

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

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

    * a box is splittable iff norm > 0.

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

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

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

    */

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

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

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

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

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

   ccount = 0;

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

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

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

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

 	if (*histp != 0) {

 	  ccount++;

 	}

     }

   boxp->colorcount = ccount;

 }

 LOCAL(int)

 median_cut (j_decompress_ptr cinfo, boxptr boxlist, int numboxes,

 	    int desired_colors)

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

 {

   int n,lb;

   int c0,c1,c2,cmax;

   register boxptr b1,b2;

   while (numboxes < desired_colors) {

     /* Select box to split.

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

      */

     if (numboxes*2 <= desired_colors) {

       b1 = find_biggest_color_pop(boxlist, numboxes);

     } else {

       b1 = find_biggest_volume(boxlist, numboxes);

     }

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

       break;

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

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

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

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

     /* Choose which axis to split the box on.

      * Current algorithm: longest scaled axis.

      * See notes in update_box about scaling distances.

      */

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

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

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

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

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

      */

     if (rgb_red[cinfo->out_color_space] == 0) {

       cmax = c1; n = 1;

       if (c0 > cmax) { cmax = c0; n = 0; }

       if (c2 > cmax) { n = 2; }

     }

     else {

       cmax = c1; n = 1;

       if (c2 > cmax) { cmax = c2; n = 2; }

       if (c0 > cmax) { n = 0; }

     }

     /* Choose split point along selected axis, and update box bounds.

      * Current algorithm: split at halfway point.

      * (Since the box has been shrunk to minimum volume,

      * any split will produce two nonempty subboxes.)

      * Note that lb value is max for lower box, so must be < old max.

      */

     switch (n) {

     case 0:

       lb = (b1->c0max + b1->c0min) / 2;

       b1->c0max = lb;

       b2->c0min = lb+1;

       break;

     case 1:

       lb = (b1->c1max + b1->c1min) / 2;

       b1->c1max = lb;

       b2->c1min = lb+1;

       break;

     case 2:

       lb = (b1->c2max + b1->c2min) / 2;

       b1->c2max = lb;

       b2->c2min = lb+1;

       break;

     }

     /* Update stats for boxes */

     update_box(cinfo, b1);

     update_box(cinfo, b2);

     numboxes++;

   }

   return numboxes;

 }

 LOCAL(void)

 compute_color (j_decompress_ptr cinfo, boxptr boxp, int icolor)

 /* Compute representative color for a box, put it in colormap[icolor] */

 {

   /* Current algorithm: mean weighted by pixels (not colors) */

   /* Note it is important to get the rounding correct! */

   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;

   hist3d histogram = cquantize->histogram;

   histptr histp;

   int c0,c1,c2;

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

   long count;

   long total = 0;

   long c0total = 0;

   long c1total = 0;

   long c2total = 0;

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

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

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

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

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

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

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

 	if ((count = *histp++) != 0) {

 	  total += count;

 	  c0total += ((c0 << C0_SHIFT) + ((1<<C0_SHIFT)>>1)) * count;

 	  c1total += ((c1 << C1_SHIFT) + ((1<<C1_SHIFT)>>1)) * count;

 	  c2total += ((c2 << C2_SHIFT) + ((1<<C2_SHIFT)>>1)) * count;

 	}

       }

     }

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

   cinfo->colormap[1][icolor] = (JSAMPLE) ((c1total + (total>>1)) / total);

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

 }

 LOCAL(void)

 select_colors (j_decompress_ptr cinfo, int desired_colors)

 /* Master routine for color selection */

 {

   boxptr boxlist;

   int numboxes;

   int i;

   /* Allocate workspace for box list */

   boxlist = (boxptr) (*cinfo->mem->alloc_small)

     ((j_common_ptr) cinfo, JPOOL_IMAGE, desired_colors * SIZEOF(box));

   /* Initialize one box containing whole space */

   numboxes = 1;

   boxlist[0].c0min = 0;

   boxlist[0].c0max = MAXJSAMPLE >> C0_SHIFT;

   boxlist[0].c1min = 0;

   boxlist[0].c1max = MAXJSAMPLE >> C1_SHIFT;

   boxlist[0].c2min = 0;

   boxlist[0].c2max = MAXJSAMPLE >> C2_SHIFT;

   /* Shrink it to actually-used volume and set its statistics */

   update_box(cinfo, & boxlist[0]);

   /* Perform median-cut to produce final box list */

   numboxes = median_cut(cinfo, boxlist, numboxes, desired_colors);

   /* Compute the representative color for each box, fill colormap */

   for (i = 0; i < numboxes; i++)

     compute_color(cinfo, & boxlist[i], i);

   cinfo->actual_number_of_colors = numboxes;

   TRACEMS1(cinfo, 1, JTRC_QUANT_SELECTED, numboxes);

 }

 /*

  * These routines are concerned with the time-critical task of mapping input

  * colors to the nearest color in the selected colormap.

  *

  * We re-use the histogram space as an "inverse color map", essentially a

  * cache for the results of nearest-color searches.  All colors within a

  * histogram cell will be mapped to the same colormap entry, namely the one

  * closest to the cell's center.  This may not be quite the closest entry to

  * the actual input color, but it's almost as good.  A zero in the cache

  * indicates we haven't found the nearest color for that cell yet; the array

  * is cleared to zeroes before starting the mapping pass.  When we find the

  * nearest color for a cell, its colormap index plus one is recorded in the

  * cache for future use.  The pass2 scanning routines call fill_inverse_cmap

  * when they need to use an unfilled entry in the cache.

  *

  * Our method of efficiently finding nearest colors is based on the "locally

  * sorted search" idea described by Heckbert and on the incremental distance

  * calculation described by Spencer W. Thomas in chapter III.1 of Graphics

  * Gems II (James Arvo, ed.  Academic Press, 1991).  Thomas points out that

  * the distances from a given colormap entry to each cell of the histogram can

  * be computed quickly using an incremental method: the differences between

  * distances to adjacent cells themselves differ by a constant.  This allows a

  * fairly fast implementation of the "brute force" approach of computing the

  * distance from every colormap entry to every histogram cell.  Unfortunately,

  * it needs a work array to hold the best-distance-so-far for each histogram

  * cell (because the inner loop has to be over cells, not colormap entries).

  * The work array elements have to be INT32s, so the work array would need

  * 256Kb at our recommended precision.  This is not feasible in DOS machines.

  *

  * To get around these problems, we apply Thomas' method to compute the

  * nearest colors for only the cells within a small subbox of the histogram.

  * The work array need be only as big as the subbox, so the memory usage

  * problem is solved.  Furthermore, we need not fill subboxes that are never

  * referenced in pass2; many images use only part of the color gamut, so a

  * fair amount of work is saved.  An additional advantage of this

  * approach is that we can apply Heckbert's locality criterion to quickly

  * eliminate colormap entries that are far away from the subbox; typically

  * three-fourths of the colormap entries are rejected by Heckbert's criterion,

  * and we need not compute their distances to individual cells in the subbox.

  * The speed of this approach is heavily influenced by the subbox size: too

  * small means too much overhead, too big loses because Heckbert's criterion

  * can't eliminate as many colormap entries.  Empirically the best subbox

  * size seems to be about 1/512th of the histogram (1/8th in each direction).

  *

  * Thomas' article also describes a refined method which is asymptotically

  * faster than the brute-force method, but it is also far more complex and

  * cannot efficiently be applied to small subboxes.  It is therefore not

  * useful for programs intended to be portable to DOS machines.  On machines

  * with plenty of memory, filling the whole histogram in one shot with Thomas'

  * refined method might be faster than the present code --- but then again,

  * it might not be any faster, and it's certainly more complicated.

  */

 /* log2(histogram cells in update box) for each axis; this can be adjusted */

 #define BOX_C0_LOG  (HIST_C0_BITS-3)

 #define BOX_C1_LOG  (HIST_C1_BITS-3)

 #define BOX_C2_LOG  (HIST_C2_BITS-3)

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

 #define BOX_C1_ELEMS  (1<<BOX_C1_LOG)

 #define BOX_C2_ELEMS  (1<<BOX_C2_LOG)

 #define BOX_C0_SHIFT  (C0_SHIFT + BOX_C0_LOG)

 #define BOX_C1_SHIFT  (C1_SHIFT + BOX_C1_LOG)

 #define BOX_C2_SHIFT  (C2_SHIFT + BOX_C2_LOG)

 /*

  * The next three routines implement inverse colormap filling.  They could

  * all be folded into one big routine, but splitting them up this way saves

  * some stack space (the mindist[] and bestdist[] arrays need not coexist)

  * and may allow some compilers to produce better code by registerizing more

  * inner-loop variables.

  */

 LOCAL(int)

 find_nearby_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,

 		    JSAMPLE colorlist[])

 /* Locate the colormap entries close enough to an update box to be candidates

  * for the nearest entry to some cell(s) in the update box.  The update box

  * is specified by the center coordinates of its first cell.  The number of

  * candidate colormap entries is returned, and their colormap indexes are

  * placed in colorlist[].

  * This routine uses Heckbert's "locally sorted search" criterion to select

  * the colors that need further consideration.

  */

 {

   int numcolors = cinfo->actual_number_of_colors;

   int maxc0, maxc1, maxc2;

   int centerc0, centerc1, centerc2;

   int i, x, ncolors;

   INT32 minmaxdist, min_dist, max_dist, tdist;

   INT32 mindist[MAXNUMCOLORS];	/* min distance to colormap entry i */

   /* Compute true coordinates of update box's upper corner and center.

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

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

    * Note that since ">>" rounds down, the "center" values may be closer to

    * min than to max; hence comparisons to them must be "<=", not "<".

    */

   maxc0 = minc0 + ((1 << BOX_C0_SHIFT) - (1 << C0_SHIFT));

   centerc0 = (minc0 + maxc0) >> 1;

   maxc1 = minc1 + ((1 << BOX_C1_SHIFT) - (1 << C1_SHIFT));

   centerc1 = (minc1 + maxc1) >> 1;

   maxc2 = minc2 + ((1 << BOX_C2_SHIFT) - (1 << C2_SHIFT));

   centerc2 = (minc2 + maxc2) >> 1;

   /* For each color in colormap, find:

    *  1. its minimum squared-distance to any point in the update box

    *     (zero if color is within update box);

    *  2. its maximum squared-distance to any point in the update box.

    * Both of these can be found by considering only the corners of the box.

    * We save the minimum distance for each color in mindist[];

    * only the smallest maximum distance is of interest.

    */

   minmaxdist = 0x7FFFFFFFL;

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

     /* We compute the squared-c0-distance term, then add in the other two. */

     x = GETJSAMPLE(cinfo->colormap[0][i]);

     if (x < minc0) {

       tdist = (x - minc0) * C0_SCALE;

       min_dist = tdist*tdist;

       tdist = (x - maxc0) * C0_SCALE;

       max_dist = tdist*tdist;

     } else if (x > maxc0) {

       tdist = (x - maxc0) * C0_SCALE;

       min_dist = tdist*tdist;

       tdist = (x - minc0) * C0_SCALE;

       max_dist = tdist*tdist;

     } else {

       /* within cell range so no contribution to min_dist */

       min_dist = 0;

       if (x <= centerc0) {

 	tdist = (x - maxc0) * C0_SCALE;

 	max_dist = tdist*tdist;

       } else {

 	tdist = (x - minc0) * C0_SCALE;

 	max_dist = tdist*tdist;

       }

     }

     x = GETJSAMPLE(cinfo->colormap[1][i]);

     if (x < minc1) {

       tdist = (x - minc1) * C1_SCALE;

       min_dist += tdist*tdist;

       tdist = (x - maxc1) * C1_SCALE;

       max_dist += tdist*tdist;

     } else if (x > maxc1) {

       tdist = (x - maxc1) * C1_SCALE;

       min_dist += tdist*tdist;

       tdist = (x - minc1) * C1_SCALE;

       max_dist += tdist*tdist;

     } else {

       /* within cell range so no contribution to min_dist */

       if (x <= centerc1) {

 	tdist = (x - maxc1) * C1_SCALE;

 	max_dist += tdist*tdist;

       } else {

 	tdist = (x - minc1) * C1_SCALE;

 	max_dist += tdist*tdist;

       }

     }

     x = GETJSAMPLE(cinfo->colormap[2][i]);

     if (x < minc2) {

       tdist = (x - minc2) * C2_SCALE;

       min_dist += tdist*tdist;

       tdist = (x - maxc2) * C2_SCALE;

       max_dist += tdist*tdist;

     } else if (x > maxc2) {

       tdist = (x - maxc2) * C2_SCALE;

       min_dist += tdist*tdist;

       tdist = (x - minc2) * C2_SCALE;

       max_dist += tdist*tdist;

     } else {

       /* within cell range so no contribution to min_dist */

       if (x <= centerc2) {

 	tdist = (x - maxc2) * C2_SCALE;

 	max_dist += tdist*tdist;

       } else {

 	tdist = (x - minc2) * C2_SCALE;

 	max_dist += tdist*tdist;

       }

     }

     mindist[i] = min_dist;	/* save away the results */

     if (max_dist < minmaxdist)

       minmaxdist = max_dist;

   }

   /* Now we know that no cell in the update box is more than minmaxdist

    * away from some colormap entry.  Therefore, only colors that are

    * within minmaxdist of some part of the box need be considered.

    */

   ncolors = 0;

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

     if (mindist[i] <= minmaxdist)

       colorlist[ncolors++] = (JSAMPLE) i;

   }

   return ncolors;

 }

 LOCAL(void)

 find_best_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,

 		  int numcolors, JSAMPLE colorlist[], JSAMPLE bestcolor[])

 /* Find the closest colormap entry for each cell in the update box,

  * given the list of candidate colors prepared by find_nearby_colors.

  * Return the indexes of the closest entries in the bestcolor[] array.

  * This routine uses Thomas' incremental distance calculation method to

  * find the distance from a colormap entry to successive cells in the box.

  */

 {

   int ic0, ic1, ic2;

   int i, icolor;

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

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

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

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

   INT32 xx0, xx1;		/* distance increments */

   register INT32 xx2;

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

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

   INT32 bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];

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

   bptr = bestdist;

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

     *bptr++ = 0x7FFFFFFFL;

   /* For each color selected by find_nearby_colors,

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

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

    */

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

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

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

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

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

     icolor = GETJSAMPLE(colorlist[i]);

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

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

     dist0 = inc0*inc0;

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

     dist0 += inc1*inc1;

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

     dist0 += inc2*inc2;

     /* Form the initial difference increments */

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

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

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

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

     bptr = bestdist;

     cptr = bestcolor;

     xx0 = inc0;

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

       dist1 = dist0;

       xx1 = inc1;

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

 	dist2 = dist1;

 	xx2 = inc2;

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

 	  if (dist2 < *bptr) {

 	    *bptr = dist2;

 	    *cptr = (JSAMPLE) icolor;

 	  }

 	  dist2 += xx2;

 	  xx2 += 2 * STEP_C2 * STEP_C2;

 	  bptr++;

 	  cptr++;

 	}

 	dist1 += xx1;

 	xx1 += 2 * STEP_C1 * STEP_C1;

       }

       dist0 += xx0;

       xx0 += 2 * STEP_C0 * STEP_C0;

     }

   }

 }

 LOCAL(void)

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

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

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

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

 {

   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;

   hist3d histogram = cquantize->histogram;

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

   int ic0, ic1, ic2;

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

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

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

   JSAMPLE colorlist[MAXNUMCOLORS];

   int numcolors;		/* number of candidate colors */

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

   JSAMPLE bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];

   /* Convert cell coordinates to update box ID */

   c0 >>= BOX_C0_LOG;

   c1 >>= BOX_C1_LOG;

   c2 >>= BOX_C2_LOG;

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

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

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

    */

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

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

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

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

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

    */

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

   /* Determine the actually nearest colors. */

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

 		   bestcolor);

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

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

   c1 <<= BOX_C1_LOG;

   c2 <<= BOX_C2_LOG;

   cptr = bestcolor;

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

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

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

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

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

       }

     }

   }

 }

 /*

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

  */

 METHODDEF(void)

 pass2_no_dither (j_decompress_ptr cinfo,

 		 JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)

 /* This version performs no dithering */

 {

   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;

   hist3d histogram = cquantize->histogram;

   register JSAMPROW inptr, outptr;

   register histptr cachep;

   register int c0, c1, c2;

   int row;

   JDIMENSION col;

   JDIMENSION width = cinfo->output_width;

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

     inptr = input_buf[row];

     outptr = output_buf[row];

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

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

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

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

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

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

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

       /* and update the cache */

       if (*cachep == 0)

 	fill_inverse_cmap(cinfo, c0,c1,c2);

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

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

     }

   }

 }

 METHODDEF(void)

 pass2_fs_dither (j_decompress_ptr cinfo,

 		 JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)

 /* This version performs Floyd-Steinberg dithering */

 {

   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;

   hist3d histogram = cquantize->histogram;

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

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

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

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

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

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

   histptr cachep;

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

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

   int row;

   JDIMENSION col;

   JDIMENSION width = cinfo->output_width;

   JSAMPLE *range_limit = cinfo->sample_range_limit;

   int *error_limit = cquantize->error_limiter;

   JSAMPROW colormap0 = cinfo->colormap[0];

   JSAMPROW colormap1 = cinfo->colormap[1];

   JSAMPROW colormap2 = cinfo->colormap[2];

   SHIFT_TEMPS

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

     inptr = input_buf[row];

     outptr = output_buf[row];

     if (cquantize->on_odd_row) {

       /* work right to left in this row */

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

       outptr += width-1;

       dir = -1;

       dir3 = -3;

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

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

     } else {

       /* work left to right in this row */

       dir = 1;

       dir3 = 3;

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

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

     }

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

     cur0 = cur1 = cur2 = 0;

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

     belowerr0 = belowerr1 = belowerr2 = 0;

     bpreverr0 = bpreverr1 = bpreverr2 = 0;

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

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

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

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

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

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

        * for either sign of the error value.

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

        */

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

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

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

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

        * See comments with init_error_limit for rationale.

        */

       cur0 = error_limit[cur0];

       cur1 = error_limit[cur1];

       cur2 = error_limit[cur2];

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

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

        * this sets the required size of the range_limit array.

        */

       cur0 += GETJSAMPLE(inptr[0]);

       cur1 += GETJSAMPLE(inptr[1]);

       cur2 += GETJSAMPLE(inptr[2]);

       cur0 = GETJSAMPLE(range_limit[cur0]);

       cur1 = GETJSAMPLE(range_limit[cur1]);

       cur2 = GETJSAMPLE(range_limit[cur2]);

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

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

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

       /* entry and update the cache */

       if (*cachep == 0)

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

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

       { register int pixcode = *cachep - 1;

 	*outptr = (JSAMPLE) pixcode;

 	/* Compute representation error for this pixel */

 	cur0 -= GETJSAMPLE(colormap0[pixcode]);

 	cur1 -= GETJSAMPLE(colormap1[pixcode]);

 	cur2 -= GETJSAMPLE(colormap2[pixcode]);

       }

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

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

        * next-line error sums left by 1 column.

        */

       { register LOCFSERROR bnexterr, delta;

 	bnexterr = cur0;	/* Process component 0 */

 	delta = cur0 * 2;

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

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

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

 	bpreverr0 = belowerr0 + cur0;

 	belowerr0 = bnexterr;

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

 	bnexterr = cur1;	/* Process component 1 */

 	delta = cur1 * 2;

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

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

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

 	bpreverr1 = belowerr1 + cur1;

 	belowerr1 = bnexterr;

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

 	bnexterr = cur2;	/* Process component 2 */

 	delta = cur2 * 2;

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

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

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

 	bpreverr2 = belowerr2 + cur2;

 	belowerr2 = bnexterr;

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

       }

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

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

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

        */

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

       outptr += dir;

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

     }

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

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

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

      */

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

     errorptr[1] = (FSERROR) bpreverr1;

     errorptr[2] = (FSERROR) bpreverr2;

   }

 }

 /*

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

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

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

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

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

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

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

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

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

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

  * the results even with corner colors allocated.

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

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

  * to Aaron Giles for this idea.

  */

 LOCAL(void)

 init_error_limit (j_decompress_ptr cinfo)

 /* Allocate and fill in the error_limiter table */

 {

   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;

   int * table;

   int in, out;

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

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

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

   cquantize->error_limiter = table;

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

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

   out = 0;

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

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

   }

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

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

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

   }

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

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

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

   }

 #undef STEPSIZE

 }

 /*

  * Finish up at the end of each pass.

  */

 METHODDEF(void)

 finish_pass1 (j_decompress_ptr cinfo)

 {

   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;

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

   cinfo->colormap = cquantize->sv_colormap;

   select_colors(cinfo, cquantize->desired);

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

   cquantize->needs_zeroed = TRUE;

 }

 METHODDEF(void)

 finish_pass2 (j_decompress_ptr cinfo)

 {

   /* no work */

 }

 /*

  * Initialize for each processing pass.

  */

 METHODDEF(void)

 start_pass_2_quant (j_decompress_ptr cinfo, boolean is_pre_scan)

 {

   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;

   hist3d histogram = cquantize->histogram;

   int i;

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

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

   if (cinfo->dither_mode != JDITHER_NONE)

     cinfo->dither_mode = JDITHER_FS;

   if (is_pre_scan) {

     /* Set up method pointers */

     cquantize->pub.color_quantize = prescan_quantize;

     cquantize->pub.finish_pass = finish_pass1;

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

   } else {

     /* Set up method pointers */

     if (cinfo->dither_mode == JDITHER_FS)

       cquantize->pub.color_quantize = pass2_fs_dither;

     else

       cquantize->pub.color_quantize = pass2_no_dither;

     cquantize->pub.finish_pass = finish_pass2;

     /* Make sure color count is acceptable */

     i = cinfo->actual_number_of_colors;

     if (i < 1)

       ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 1);

     if (i > MAXNUMCOLORS)

       ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);

     if (cinfo->dither_mode == JDITHER_FS) {

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

 				   (3 * SIZEOF(FSERROR)));

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

       if (cquantize->fserrors == NULL)

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

 	  ((j_common_ptr) cinfo, JPOOL_IMAGE, arraysize);

       /* Initialize the propagated errors to zero. */

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

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

       if (cquantize->error_limiter == NULL)

 	init_error_limit(cinfo);

       cquantize->on_odd_row = FALSE;

     }

   }

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

   if (cquantize->needs_zeroed) {

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

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

 		HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));

     }

     cquantize->needs_zeroed = FALSE;

   }

 }

 /*

  * Switch to a new external colormap between output passes.

  */

 METHODDEF(void)

 new_color_map_2_quant (j_decompress_ptr cinfo)

 {

   my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;

   /* Reset the inverse color map */

   cquantize->needs_zeroed = TRUE;

 }

 /*

  * Module initialization routine for 2-pass color quantization.

  */

 GLOBAL(void)

 jinit_2pass_quantizer (j_decompress_ptr cinfo)

 {

   my_cquantize_ptr cquantize;

   int i;

   cquantize = (my_cquantize_ptr)

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

 				SIZEOF(my_cquantizer));

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

   cquantize->pub.start_pass = start_pass_2_quant;

   cquantize->pub.new_color_map = new_color_map_2_quant;

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

   cquantize->error_limiter = NULL;

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

   if (cinfo->out_color_components != 3)

     ERREXIT(cinfo, JERR_NOTIMPL);

   /* Allocate the histogram/inverse colormap storage */

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

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

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

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

       ((j_common_ptr) cinfo, JPOOL_IMAGE,

        HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));

   }

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

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

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

    * the memory manager's space calculations.

    */

   if (cinfo->enable_2pass_quant) {

     /* Make sure color count is acceptable */

     int desired = cinfo->desired_number_of_colors;

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

     if (desired < 8)

       ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 8);

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

     if (desired > MAXNUMCOLORS)

       ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);

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

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

     cquantize->desired = desired;

   } else

     cquantize->sv_colormap = NULL;

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

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

   if (cinfo->dither_mode != JDITHER_NONE)

     cinfo->dither_mode = JDITHER_FS;

   /* Allocate Floyd-Steinberg workspace if necessary.

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

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

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

    */

   if (cinfo->dither_mode == JDITHER_FS) {

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

       ((j_common_ptr) cinfo, JPOOL_IMAGE,

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

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

     init_error_limit(cinfo);

   }

 }

 #endif /* QUANT_2PASS_SUPPORTED */