
//
// wquant2.c
//
// Copyright (C) 1991, 1992, 1993, 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 are invoked via the methods color_quant_prescan,
// color_quant_doit, and color_quant_init/term.
//

#include "winclude.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.
//
// The most significant difference between this quantizer and others is that
// this one is intended to operate in YCbCr colorspace, rather than RGB space
// as is usually done.  Actually we work in scaled YCbCr colorspace, where
// Y distances are inflated by a factor of 2 relative to Cb or Cr distances.
// The empirical evidence is that distances in this space correspond to
// perceptual color differences more closely than do distances in RGB space;
// and working in this space is inexpensive within a JPEG decompressor, since
// the input data is already in YCbCr form.  (We could transform to an even
// more perceptually linear space such as Lab or Luv, but that is very slow
// and doesn't yield much better results than scaled YCbCr.)
//

#define Y_SCALE 2		// scale Y distances up by this much

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


//
// First we have the histogram data structure and routines for creating it.
//
// For work in YCbCr space, it is useful to keep more precision for Y than
// for Cb or Cr.  We recommend keeping 6 bits for Y and 5 bits each for Cb/Cr.
// 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 Y value (typically 2^6 = 64 pointers) and
// each 2-D array has 2^5^2 = 1024 or 2^6^2 = 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).
//

#ifndef HIST_Y_BITS		// so you can override from Makefile
#define HIST_Y_BITS  6		// bits of precision in Y histogram
#endif

#ifndef HIST_C_BITS		// so you can override from Makefile
#define HIST_C_BITS  5		// bits of precision in Cb/Cr histogram
#endif

#define HIST_Y_ELEMS  (1<<HIST_Y_BITS) // # of elements along histogram axes
#define HIST_C_ELEMS  (1<<HIST_C_BITS)

// These are the amounts to shift an input value to get a histogram index.
// For a combination 8/12 bit implementation, would need variables here...

#define Y_SHIFT  (BITS_IN_JSAMPLE-HIST_Y_BITS)
#define C_SHIFT  (BITS_IN_JSAMPLE-HIST_C_BITS)


typedef UINT16 histcell;	// histogram cell; MUST be an unsigned type

typedef histcell MLPTR histptr;	// for pointers to histogram cells

typedef histcell hist1d[HIST_C_ELEMS]; // typedefs for the array
typedef hist1d MLPTR hist2d;	// type for the Y-level pointers
typedef hist2d MLPTR hist3d;	// type for top-level pointer

static hist3d histogram;	// pointer to the histogram

//
// Prescan some rows of pixels.
// In this module the prescan simply updates the histogram, which has been
// initialized to zeroes by color_quant_init.
// Note: workspace is probably not useful for this routine, but it is passed
// anyway to allow some code sharing within the pipeline controller.
//

METHODDEF void
color_quant_prescan (decompress_info_ptr cinfo, int num_rows,
		     JSAMPIMAGE image_data, JSAMPARRAY workspace)
{
	JSAMPROW ptr0, ptr1, ptr2;
	histptr histp;
	int c0, c1, c2;
	int row;
	long col;
	long width = cinfo->image_width;

	for( row = 0; row < num_rows; row++ )
	{
		ptr0 = image_data[0][row];
		ptr1 = image_data[1][row];
		ptr2 = image_data[2][row];
		for( col = width; col > 0; col-- )
		{
			// get pixel value and index into the histogram
			c0 = GETJSAMPLE(*ptr0++) >> Y_SHIFT;
			c1 = GETJSAMPLE(*ptr1++) >> C_SHIFT;
			c2 = GETJSAMPLE(*ptr2++) >> C_SHIFT;
			histp = & histogram[c0][c1][c2];
			// increment,
			// check for overflow and undo increment if so.
			// We assume unsigned representation here!
			if( ++(*histp) == 0 )
				(*histp)--;
		}
	}
}


//
// Now 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 number of nonzero histogram cells within this box
	long colorcount;
}box;

typedef box MLPTR boxptr;

static boxptr boxlist;	// array with room for desired # of boxes
static int numboxes;	// number of boxes currently in boxlist

static JSAMPARRAY my_colormap;	// the finished colormap (in YCbCr space)


// Find the splittable box with the largest color population
// Returns NULL if no splittable boxes remain
LOCAL boxptr
find_biggest_color_pop (void)
{
	boxptr boxp;
	int i;
	long max = 0;
	boxptr which = NULL;

	for( i = 0, boxp = boxlist; i < numboxes; i++, boxp++ )
	{
		if( boxp->colorcount > max )
		{
			if( (boxp->c0max > boxp->c0min) ||
				(boxp->c1max > boxp->c1min) ||
				(boxp->c2max > boxp->c2min) )
			{
				which = boxp;
				max = boxp->colorcount;
			}
		}
	}
	return which;
}


// Find the splittable box with the largest (scaled) volume
// Returns NULL if no splittable boxes remain
LOCAL boxptr
find_biggest_volume (void)
{
	boxptr boxp;
	int i;
	INT32 max = 0;
	INT32 norm, c0,c1,c2;
	boxptr which = NULL;

	// We use 2-norm rather than real volume here.
	// Some care is needed since the differences are expressed in
	// histogram-cell units; if HIST_Y_BITS != HIST_C_BITS, we have to
	// adjust the scaling to get the proper scaled-YCbCr-space distance.
	// This code won't work right if HIST_Y_BITS < HIST_C_BITS,
	// but that shouldn't ever be true.
	// Note norm > 0 iff box is splittable,
	// so need not check separately.
	
	for( i = 0, boxp = boxlist; i < numboxes; i++, boxp++ )
	{
		c0 = (boxp->c0max - boxp->c0min) * Y_SCALE;
		c1 = (boxp->c1max - boxp->c1min) << (HIST_Y_BITS-HIST_C_BITS);
		c2 = (boxp->c2max - boxp->c2min) << (HIST_Y_BITS-HIST_C_BITS);
		norm = c0*c0 + c1*c1 + c2*c2;
		if( norm > max )
		{
			which = boxp;
			max = norm;
		}
	}
	return which;
}


// Shrink the min/max bounds of a box
// to enclose only nonzero elements,
// and recompute its population
LOCAL void
update_box (boxptr boxp)
{
	histptr histp;
	int c0,c1,c2;
	int c0min,c0max,c1min,c1max,c2min,c2max;
	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_C_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_C_ELEMS)
				{
					if( *histp != 0 )
					{
						boxp->c2max = c2max = c2;
						goto have_c2max;
					}
				}
			}
		}
	}
 have_c2max:

	// 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;
}


// Repeatedly select and split the largest box until we have enough boxes
LOCAL void
median_cut (int desired_colors)
{
	int n,lb;
	int c0,c1,c2,cmax;
	boxptr b1,b2;

	while( numboxes < desired_colors )
	{
		// Select box to split
		// Current algorithm: by population for
		// first half, then by volume
		if( numboxes*2 <= desired_colors )
		{
			b1 = find_biggest_color_pop();
		}
		else
		{
			b1 = find_biggest_volume();
		}
		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 find_biggest_volume about scaling...
		// 
		c0 = (b1->c0max - b1->c0min) * Y_SCALE;
		c1 = (b1->c1max - b1->c1min) << (HIST_Y_BITS-HIST_C_BITS);
		c2 = (b1->c2max - b1->c2min) << (HIST_Y_BITS-HIST_C_BITS);
		cmax = c0;
		n = 0;
		if( c1 > cmax )
		{
			cmax = c1;
			n = 1;
		}
		if( c2 > cmax )
		{
			n = 2;
		}
		// 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(b1);
		update_box(b2);
		numboxes++;
	}
}


// Compute representative color for a box,
// put it in my_colormap[icolor]
LOCAL void
compute_color (boxptr boxp, int icolor)
{
	// Current algorithm: mean weighted by pixels (not colors)
	// Note it is important to get the rounding correct!
	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 << Y_SHIFT) + ((1<<Y_SHIFT)>>1)) * count;
	  c1total += ((c1 << C_SHIFT) + ((1<<C_SHIFT)>>1)) * count;
	  c2total += ((c2 << C_SHIFT) + ((1<<C_SHIFT)>>1)) * count;
	}
      }
    }
  
  my_colormap[0][icolor] = (JSAMPLE) ((c0total + (total>>1)) / total);
  my_colormap[1][icolor] = (JSAMPLE) ((c1total + (total>>1)) / total);
  my_colormap[2][icolor] = (JSAMPLE) ((c2total + (total>>1)) / total);
}

JSAMPARRAY rc_input_hack[3];
JSAMPARRAY rc_output_hack[10];	/* assume no more than 10 output components */

LOCAL void
remap_colormap (decompress_info_ptr cinfo)
/* Remap the internal colormap to the output colorspace */
{
  /* This requires a little trickery since color_convert expects to
   * deal with 3-D arrays (a 2-D sample array for each component).
   * We must promote the colormaps into one-row 3-D arrays.
   */
  short ci;

  for (ci = 0; ci < 3; ci++)
    rc_input_hack[ci] = &(my_colormap[ci]);
  for (ci = 0; ci < cinfo->color_out_comps; ci++)
    rc_output_hack[ci] = &(cinfo->colormap[ci]);

  (*cinfo->methods->color_convert) (cinfo, 1,
				    (long) cinfo->actual_number_of_colors,
				    rc_input_hack, rc_output_hack);
}

#if	(defined( _INC_WINDOWS ) && defined( USELOCBUF ))
BYTE	DSBox[256 * SIZEOF(box)];
#endif	/* defined( _INC_WINDOWS ) && defined( USELOCBUF ) */

LOCAL void
select_colors (decompress_info_ptr cinfo)
/* Master routine for color selection */
{
  int desired = cinfo->desired_number_of_colors;
  int i;

  /* Allocate workspace for box list */
#if	(defined( _INC_WINDOWS ) && defined( USELOCBUF ))
	boxlist = (boxptr) &DSBox[0];
#else
  boxlist = (boxptr) (*cinfo->emethods->alloc_small) (desired * SIZEOF(box));
#endif
  /* Initialize one box containing whole space */
  numboxes = 1;
  boxlist[0].c0min = 0;
  boxlist[0].c0max = MAXJSAMPLE >> Y_SHIFT;
  boxlist[0].c1min = 0;
  boxlist[0].c1max = MAXJSAMPLE >> C_SHIFT;
  boxlist[0].c2min = 0;
  boxlist[0].c2max = MAXJSAMPLE >> C_SHIFT;
  /* Shrink it to actually-used volume and set its statistics */
  update_box(& boxlist[0]);
  /* Perform median-cut to produce final box list */
  median_cut(desired);
  /* Compute the representative color for each box, fill my_colormap[] */
  for (i = 0; i < numboxes; i++)
    compute_color(& boxlist[i], i);
  cinfo->actual_number_of_colors = numboxes;
  /* Produce an output colormap in the desired output colorspace */
  remap_colormap(cinfo);
#ifndef	_INC_WINDOWS
  TRACEMS1(cinfo->emethods, 1, "Selected %d colors for quantization",
	   numboxes);
#endif
  /* Done with the box list */
#if	(defined( _INC_WINDOWS ) && defined( USELOCBUF ))
	boxlist = 0;
#else
  (*cinfo->emethods->free_small) ((void MLPTR) boxlist);
#endif
}


/*
 * 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.
 * Another disadvantage of the brute force approach is that it computes
 * distances to every cell of the cubical histogram.  When working with YCbCr
 * input, only about a quarter of the cube represents realizable colors, so
 * many of the cells will never be used and filling them is wasted effort.
 *
 * 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.  A subbox is processed only when some cell in it is
 * referenced by the pass2 routines, so we will never bother with cells far
 * outside the realizable color volume.  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.
 */


#ifndef BOX_Y_LOG		/* so you can override from Makefile */
#define BOX_Y_LOG  (HIST_Y_BITS-3) /* log2(hist cells in update box, Y axis) */
#endif
#ifndef BOX_C_LOG		/* so you can override from Makefile */
#define BOX_C_LOG  (HIST_C_BITS-3) /* log2(hist cells in update box, C axes) */
#endif

#define BOX_Y_ELEMS  (1<<BOX_Y_LOG) /* # of hist cells in update box */
#define BOX_C_ELEMS  (1<<BOX_C_LOG)

#define BOX_Y_SHIFT  (Y_SHIFT + BOX_Y_LOG)
#define BOX_C_SHIFT  (C_SHIFT + BOX_C_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 (decompress_info_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_Y_SHIFT) - (1 << Y_SHIFT));
  centerc0 = (minc0 + maxc0) >> 1;
  maxc1 = minc1 + ((1 << BOX_C_SHIFT) - (1 << C_SHIFT));
  centerc1 = (minc1 + maxc1) >> 1;
  maxc2 = minc2 + ((1 << BOX_C_SHIFT) - (1 << C_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.
   * Note we have to scale Y to get correct distance in scaled space.
   */
  minmaxdist = 0x7FFFFFFFL;

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

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

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

INT32 bestdist[BOX_Y_ELEMS * BOX_C_ELEMS * BOX_C_ELEMS];

LOCAL void
find_best_colors (decompress_info_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 MLPTR bptr;	/* pointer into bestdist[] array */
  JSAMPLE MLPTR 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 */

  /* Initialize best-distance for each cell of the update box */
  bptr = bestdist;
  for (i = BOX_Y_ELEMS*BOX_C_ELEMS*BOX_C_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.
   * Note we have to scale Y to get correct distance in scaled space.
   */
  
  /* Nominal steps between cell centers ("x" in Thomas article) */
#define STEP_Y  ((1 << Y_SHIFT) * Y_SCALE)
#define STEP_C  (1 << C_SHIFT)
  
  for (i = 0; i < numcolors; i++) {
    icolor = GETJSAMPLE(colorlist[i]);
    /* Compute (square of) distance from minc0/c1/c2 to this color */
    inc0 = (minc0 - (int) GETJSAMPLE(my_colormap[0][icolor])) * Y_SCALE;
    dist0 = inc0*inc0;
    inc1 = minc1 - (int) GETJSAMPLE(my_colormap[1][icolor]);
    dist0 += inc1*inc1;
    inc2 = minc2 - (int) GETJSAMPLE(my_colormap[2][icolor]);
    dist0 += inc2*inc2;
    /* Form the initial difference increments */
    inc0 = inc0 * (2 * STEP_Y) + STEP_Y * STEP_Y;
    inc1 = inc1 * (2 * STEP_C) + STEP_C * STEP_C;
    inc2 = inc2 * (2 * STEP_C) + STEP_C * STEP_C;
    /* Now loop over all cells in box, updating distance per Thomas method */
    bptr = bestdist;
    cptr = bestcolor;
    xx0 = inc0;
    for (ic0 = BOX_Y_ELEMS-1; ic0 >= 0; ic0--) {
      dist1 = dist0;
      xx1 = inc1;
      for (ic1 = BOX_C_ELEMS-1; ic1 >= 0; ic1--) {
	dist2 = dist1;
	xx2 = inc2;
	for (ic2 = BOX_C_ELEMS-1; ic2 >= 0; ic2--) {
	  if (dist2 < *bptr) {
	    *bptr = dist2;
	    *cptr = (JSAMPLE) icolor;
	  }
	  dist2 += xx2;
	  xx2 += 2 * STEP_C * STEP_C;
	  bptr++;
	  cptr++;
	}
	dist1 += xx1;
	xx1 += 2 * STEP_C * STEP_C;
      }
      dist0 += xx0;
      xx0 += 2 * STEP_Y * STEP_Y;
    }
  }
}

JSAMPLE ficolorlist[MAXNUMCOLORS];
// This array holds the actually closest colormap index for each cell.
JSAMPLE fibestcolor[BOX_Y_ELEMS * BOX_C_ELEMS * BOX_C_ELEMS];

// 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.)
LOCAL void
fill_inverse_cmap( decompress_info_ptr cinfo, int c0, int c1, int c2 )
{
	int minc0, minc1, minc2;	// lower left corner of update box
	int ic0, ic1, ic2;
	JSAMPLE MLPTR cptr;	// pointer into bestcolor[] array
	histptr cachep;		// pointer into main cache array
	// This array lists the candidate colormap indexes.
	int numcolors;		// number of candidate colors

	// Convert cell coordinates to update box ID
	c0 >>= BOX_Y_LOG;
	c1 >>= BOX_C_LOG;
	c2 >>= BOX_C_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_Y_SHIFT) + ((1 << Y_SHIFT) >> 1);
	minc1 = (c1 << BOX_C_SHIFT) + ((1 << C_SHIFT) >> 1);
	minc2 = (c2 << BOX_C_SHIFT) + ((1 << C_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, ficolorlist);

	// Determine the actually nearest colors.
	find_best_colors(cinfo, minc0, minc1, minc2,
		numcolors, ficolorlist,
		fibestcolor );

	// Save the best color numbers (plus 1) in the main cache array
	c0 <<= BOX_Y_LOG;	// convert ID back to base cell indexes
	c1 <<= BOX_C_LOG;
	c2 <<= BOX_C_LOG;
	cptr = fibestcolor;

	for( ic0 = 0; ic0 < BOX_Y_ELEMS; ic0++ )
	{
		for( ic1 = 0; ic1 < BOX_C_ELEMS; ic1++ )
		{
			cachep = & histogram[c0+ic0][c1+ic1][c2];
			for( ic2 = 0; ic2 < BOX_C_ELEMS; ic2++ )
			{
				*cachep++ = (histcell) (GETJSAMPLE(*cptr++) + 1);
			}
		}
	}
}


#ifdef	_INC_WINDOWS	// NOTE: When in a DLL the SS != DS!!!

JSAMPARRAY	DSoutput_workspace;	// So we MUST place the pointer to the
// array pointers in the DATA SEGMENT ... This is taken OFF the stack
// It is a FAR pointer to an array of FAR pointers ...
#endif

//
// These routines perform second-pass scanning of the image: map each pixel to
// the proper colormap index, and output the indexes to the output file.
//
// output_workspace is a one-component array of pixel dimensions at least
// as large as the input image strip; it can be used to hold the converted
// pixels' colormap indexes.
//

// This version performs no dithering
METHODDEF void
pass2_nodither (decompress_info_ptr cinfo, int num_rows,
		JSAMPIMAGE image_data, JSAMPARRAY output_workspace)
{
	JSAMPROW ptr0, ptr1, ptr2, outptr;
	histptr cachep;
	int c0, c1, c2;
	int row;
	long col;
	long width = cinfo->image_width;

#ifdef	_INC_WINDOWS
	DSoutput_workspace = output_workspace;	// pointer to array of pointers
	// moved to DATA SEGMENT, so we can point to this later ...
#endif

	// Convert data to colormap indexes, which we save in output_workspace
	for( row = 0; row < num_rows; row++ )
	{
		ptr0 = image_data[0][row];
		ptr1 = image_data[1][row];
		ptr2 = image_data[2][row];
		outptr = output_workspace[row];
		for( col = width; col > 0; col-- )
		{
			// get pixel value and index into the cache
			c0 = GETJSAMPLE(*ptr0++) >> Y_SHIFT;
			c1 = GETJSAMPLE(*ptr1++) >> C_SHIFT;
			c2 = GETJSAMPLE(*ptr2++) >> C_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);

		}

	}

	// Emit converted rows to the output file
#ifdef	_INC_WINDOWS	// We can ONLY pass a DS pointer!!!
	(*cinfo->methods->put_pixel_rows) (cinfo, num_rows, &DSoutput_workspace);
#else
	(*cinfo->methods->put_pixel_rows) (cinfo, num_rows, &output_workspace);
#endif

}


// 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_medium.
//

#ifdef EIGHT_BIT_SAMPLES
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 MLPTR FSERRPTR;	// pointer to error array (in FAR storage!)

static FSERRPTR fserrors;	// accumulated errors
static boolean on_odd_row;	// flag to remember which row we are on

// This version performs Floyd-Steinberg dithering
METHODDEF void
pass2_dither (decompress_info_ptr cinfo, int num_rows,
	      JSAMPIMAGE image_data, JSAMPARRAY output_workspace)
{
	LOCFSERROR cur0, cur1, cur2;	// current error or pixel value
	LOCFSERROR belowerr0, belowerr1, belowerr2; // error for pixel below cur
	LOCFSERROR bpreverr0, bpreverr1, bpreverr2; // error for below/prev col
	FSERRPTR errorptr;	// => fserrors[] at column before current
	JSAMPROW ptr0, ptr1, ptr2;	// => current input pixel
	JSAMPROW outptr;	// => current output pixel
	histptr cachep;
	int dir;		// +1 or -1 depending on direction
	int dir3;		// 3*dir, for advancing errorptr
	int row;
	long col;
	long width = cinfo->image_width;
	JSAMPLE MLPTR range_limit = cinfo->sample_range_limit;
	JSAMPROW colormap0 = my_colormap[0];
	JSAMPROW colormap1 = my_colormap[1];
	JSAMPROW colormap2 = my_colormap[2];
	SHIFT_TEMPS

#ifdef	_INC_WINDOWS
	DSoutput_workspace = output_workspace;	// Move from STACK to DATA SEG!!!
#endif
	// Convert data to colormap indexes, which we save in output_workspace
	for( row = 0; row < num_rows; row++ )
	{
		ptr0 = image_data[0][row];
		ptr1 = image_data[1][row];
		ptr2 = image_data[2][row];
		outptr = output_workspace[row];
		if( on_odd_row )
		{
			// work right to left in this row
			ptr0 += width - 1;	// so point to rightmost pixel
			ptr1 += width - 1;
			ptr2 += width - 1;
			outptr += width - 1;
			dir = -1;
			dir3 = -3;
			errorptr = fserrors + (width+1)*3; // point to entry after last column
			on_odd_row = FALSE;	// flip for next time
		}
		else
		{
			// work left to right in this row
			dir = 1;
			dir3 = 3;
			errorptr = fserrors;	// point to entry before first real column
			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);

			// Form pixel value + error, and range-limit to
			// 0..MAXJSAMPLE.
			// The maximum error is +- MAXJSAMPLE; this
			// sets the required size of the range_limit array.
			
			cur0 += GETJSAMPLE(*ptr0);
			cur1 += GETJSAMPLE(*ptr1);
			cur2 += GETJSAMPLE(*ptr2);
			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 >> Y_SHIFT][cur1 >> C_SHIFT][cur2 >> C_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 >> Y_SHIFT,
					cur1 >> C_SHIFT,
					cur2 >> C_SHIFT );

			// Now emit the colormap index for this cell
			{
				int pixcode = *cachep - 1;
				*outptr = (JSAMPLE) pixcode;
				// Compute representation error for this pixel
				cur0 -= GETJSAMPLE(colormap0[pixcode]);
				cur1 -= GETJSAMPLE(colormap1[pixcode]);
				cur2 -= GETJSAMPLE(colormap2[pixcode]);
			}

			// Compute error fractions to be propagated to adjacent pixels.
			// Add these into the running sums, and simultaneously shift the
			// next-line error sums left by 1 column.
			// 
			{
				LOCFSERROR bnexterr, delta;

				bnexterr = cur0;	// Process component 0
				delta = cur0 * 2;
				cur0 += delta;		// form error * 3
				errorptr[0] = (FSERROR) (bpreverr0 + cur0);
				cur0 += delta;		// form error * 5
				bpreverr0 = belowerr0 + cur0;
				belowerr0 = bnexterr;
				cur0 += delta;		// form error * 7
				bnexterr = cur1;	// Process component 1
				delta = cur1 * 2;
				cur1 += delta;		// form error * 3
				errorptr[1] = (FSERROR) (bpreverr1 + cur1);
				cur1 += delta;		// form error * 5
				bpreverr1 = belowerr1 + cur1;
				belowerr1 = bnexterr;
				cur1 += delta;		// form error * 7
				bnexterr = cur2;	// Process component 2
				delta = cur2 * 2;
				cur2 += delta;		// form error * 3
				errorptr[2] = (FSERROR) (bpreverr2 + cur2);
				cur2 += delta;		// form error * 5
				bpreverr2 = belowerr2 + cur2;
				belowerr2 = bnexterr;
				cur2 += delta;		// form error * 7
			}

			// At this point curN contains the 7/16 error
			// value to be propagated
			// to the next pixel on the current line,
			// and all the errors for the
			// next line have been shifted over.
			// We are therefore ready to move on.

			ptr0 += dir;	// Advance pixel pointers to next column
			ptr1 += dir;
			ptr2 += dir;
			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;
	}	// 	for( row = 0; row < num_rows; row++ )

	// Emit converted rows to the output file
#ifdef	_INC_WINDOWS
	(*cinfo->methods->put_pixel_rows) (cinfo, num_rows, &DSoutput_workspace);
#else
	(*cinfo->methods->put_pixel_rows) (cinfo, num_rows, &output_workspace);
#endif
}


//
// Initialize for two-pass color quantization.
//

METHODDEF void
jcolor_quant_init (decompress_info_ptr cinfo)
{
	int i;

	// Lower bound on # of colors ...
	// somewhat arbitrary as long as > 0
	if( cinfo->desired_number_of_colors < 8 )
	{
#ifdef	_INC_WINDOWS
		ERRFLAG( BAD_JPGCRL );	/* "Cannot request less than 8 quantized colors" */
		return;
#else
    	ERREXIT(cinfo->emethods, "Cannot request less than 8 quantized colors");
#endif
	}

	// Make sure colormap indexes can be represented by JSAMPLEs
	if( cinfo->desired_number_of_colors > MAXNUMCOLORS )
	{
#ifdef	_INC_WINDOWS
		ERRFLAG( BAD_JPGCRM );
		return;
#else
		ERREXIT1(cinfo->emethods, "Cannot request more than %d quantized colors",
			MAXNUMCOLORS);
#endif
	}

	// Allocate and zero the histogram
	histogram = (hist3d) (*cinfo->emethods->alloc_small)
		(HIST_Y_ELEMS * SIZEOF(hist2d));

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

		histogram[i] = (hist2d) (*cinfo->emethods->alloc_medium)
			(HIST_C_ELEMS*HIST_C_ELEMS * SIZEOF(histcell));
		jzero_far((void MLPTR ) histogram[i],
			HIST_C_ELEMS*HIST_C_ELEMS * SIZEOF(histcell));
	}

	// Allocate storage for the internal and external colormaps.
	// We do this now since it is FAR storage and may affect the memory
	// manager's space calculations.
	my_colormap = (*cinfo->emethods->alloc_small_sarray)
		((long) cinfo->desired_number_of_colors,
		(long) 3 );

	cinfo->colormap = (*cinfo->emethods->alloc_small_sarray)
		((long) cinfo->desired_number_of_colors,
		(long) cinfo->color_out_comps );

	// Allocate Floyd-Steinberg workspace if necessary
	// This isn't needed until pass 2, but again it is FAR storage.
	if( cinfo->use_dithering )
	{
		size_t arraysize = (size_t) ((cinfo->image_width + 2L) *
			(3 * SIZEOF(FSERROR)) );

		fserrors = (FSERRPTR) (*cinfo->emethods->alloc_medium) (arraysize);

		// Initialize the propagated errors to zero.
		jzero_far((void MLPTR) fserrors, arraysize);
		on_odd_row = FALSE;
	}

	// Indicate number of passes needed, excluding the prescan pass.
	cinfo->total_passes++;	// I always use one pass

}


//
// Perform two-pass quantization: rescan the image data and output the
// converted data via put_color_map and put_pixel_rows.
// The source_method is a routine that can scan the image data; it can
// be called as many times as desired.  The processing routine called by
// source_method has the same interface as color_quantize does in the
// one-pass case, except it must call put_pixel_rows itself.  (This allows
// me to use multiple passes in which earlier passes don't output anything.)
//

METHODDEF void
color_quant_doit (decompress_info_ptr cinfo, quantize_caller_ptr source_method)
{
	int i;

	// Select the representative colors
	select_colors(cinfo);

	// Pass the external colormap to the output module.
	// NB: the output module may continue to use
	// the colormap until shutdown.
	(*cinfo->methods->put_color_map) ( cinfo,
		cinfo->actual_number_of_colors,
		cinfo->colormap);

	// Re-zero the histogram so pass 2 can
	// use it as nearest-color cache
	for( i = 0; i < HIST_Y_ELEMS; i++ )
	{
		jzero_far(( void MLPTR ) histogram[i],
			HIST_C_ELEMS*HIST_C_ELEMS * SIZEOF(histcell));
	}

	// Perform pass 2
	if( cinfo->use_dithering )
		(*source_method) (cinfo, pass2_dither);
	else
		(*source_method) (cinfo, pass2_nodither);
}


//
// Finish up at the end of the file.
//

// no work (we let free_all release the histogram/cache and colormaps)
// Note that we *mustn't* free the external colormap before free_all,
// since output module may use it!
METHODDEF void
color_quant_term (decompress_info_ptr cinfo)
{
	// nothing
}


//
// Map some rows of pixels to the output colormapped representation.
// Not used in two-pass case.
//

METHODDEF void
color_quantize (decompress_info_ptr cinfo, int num_rows,
		JSAMPIMAGE input_data, JSAMPARRAY output_data)
{
#ifdef	_INC_WINDOWS
	ERRFLAG( BAD_NOIMP2 );
#else
	ERREXIT(cinfo->emethods, "Should not get here!");
#endif
}


//
// The method selection routine for 2-pass color quantization.
//

GLOBAL void
jsel2quantize (decompress_info_ptr cinfo)
{
	if( cinfo->two_pass_quantize )
	{
		// Make sure jdmaster didn't give me a
		// case I can't handle
		if( (cinfo->num_components != 3) ||
			(cinfo->jpeg_color_space != CS_YCbCr) )
		{
#ifdef	_INC_WINDOWS
			ERRFLAG( BAD_JPG2PQ );	// "2-pass quantization only handles YCbCr input"
			return;
#else
			ERREXIT(cinfo->emethods, "2-pass quantization only handles YCbCr input");
#endif
		}

		cinfo->methods->color_quant_init = jcolor_quant_init;
		cinfo->methods->color_quant_prescan = color_quant_prescan;
		cinfo->methods->color_quant_doit = color_quant_doit;
		cinfo->methods->color_quant_term = color_quant_term;
		cinfo->methods->color_quantize = color_quantize;

		// Quantized grayscale output is normally done by
		// jquant1.c (which will do
		// a much better job of it).  But if the
		// program is configured with only
		// 2-pass quantization, then I have to do the job.
		// In this situation,
		// jseldcolor's clearing of the Cb/Cr
		// component_needed flags is incorrect,
		// because I will look at those components
		// before conversion.

		cinfo->cur_comp_info[1]->component_needed = TRUE;
		cinfo->cur_comp_info[2]->component_needed = TRUE;
	}
}

#endif // QUANT_2PASS_SUPPORTED

// eof - wquant2.c

