This commit was manufactured by cvs2svn to create branch 'PHP_5'.PHP-5

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diff --git a/ext/gd/libgd/gd_topal.c b/ext/gd/libgd/gd_topal.c
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--- a/ext/gd/libgd/gd_topal.c
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@@ -1,1658 +0,0 @@
-
-
-/*
- * gd_topal.c 
- * 
- * This code is adapted pretty much entirely from jquant2.c,
- * Copyright (C) 1991-1996, Thomas G. Lane. That file is
- * part of the Independent JPEG Group's software. Conditions of
- * use are compatible with the gd license. See the gd license 
- * statement and README-JPEG.TXT for additional information.
- *
- * 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.
- *
- * SUPPORT FOR ALPHA CHANNELS WAS HACKED IN BY THOMAS BOUTELL, who also
- * adapted the code to work within gd rather than within libjpeg, and
- * may not have done a great job of either. It's not Thomas G. Lane's fault.
- */
-
-#include "gd.h"
-#include "gdhelpers.h"
-#include <string.h>
-#include <stdlib.h>
-
-/*
- * 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
- * RGBA space, giving G distances more weight than R, and R in turn more than
- * B.  Alpha is weighted least. 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. 
- */
-
-#ifndef TRUE
-#define TRUE 1
-#endif /* TRUE */
-
-#ifndef FALSE
-#define FALSE 0
-#endif /* FALSE */
-
-#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 */
-#define A_SCALE 4		/* and alpha by this much. This really
-				   only scales by 1 because alpha
-				   values are 7-bit to begin with. */
-
-/* Channel ordering (fixed in gd) */
-#define C0_SCALE R_SCALE
-#define C1_SCALE G_SCALE
-#define C2_SCALE B_SCALE
-#define C3_SCALE A_SCALE
-
-/*
- * 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  (gdMaxColors)	/* maximum size of colormap */
-
-#define HIST_C0_BITS  5		/* bits of precision in R histogram */
-#define HIST_C1_BITS  6		/* bits of precision in G histogram */
-#define HIST_C2_BITS  5		/* bits of precision in B histogram */
-#define HIST_C3_BITS  3		/* bits of precision in A 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)
-#define HIST_C3_ELEMS  (1<<HIST_C3_BITS)
-
-/* These are the amounts to shift an input value to get a histogram index. */
-#define C0_SHIFT  (8-HIST_C0_BITS)
-#define C1_SHIFT  (8-HIST_C1_BITS)
-#define C2_SHIFT  (8-HIST_C2_BITS)
-/* Beware! Alpha is 7 bit to begin with */
-#define C3_SHIFT  (7-HIST_C3_BITS)
-
-
-typedef unsigned short histcell;	/* histogram cell; prefer an unsigned type */
-
-typedef histcell *histptr;	/* for pointers to histogram cells */
-
-typedef histcell hist1d[HIST_C3_ELEMS];		/* typedefs for the array */
-typedef hist1d *hist2d;		/* type for the 2nd-level pointers */
-typedef hist2d *hist3d;		/* type for third-level pointer */
-typedef hist3d *hist4d;		/* 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.
- *
- */
-
-typedef signed short FSERROR;	/* 16 bits should be enough */
-typedef int LOCFSERROR;		/* use 'int' for calculation temps */
-
-typedef FSERROR *FSERRPTR;	/* pointer to error array */
-
-/* Private object */
-
-typedef struct
-  {
-    hist4d histogram;		/* pointer to the histogram */
-    int needs_zeroed;		/* TRUE if next pass must zero histogram */
-
-    /* Variables for Floyd-Steinberg dithering */
-    FSERRPTR fserrors;		/* accumulated errors */
-    int on_odd_row;		/* flag to remember which row we are on */
-    int *error_limiter;		/* table for clamping the applied error */
-    int *error_limiter_storage;	/* gdMalloc'd storage for the above */
-    int transparentIsPresent;	/* TBB: for rescaling to ensure that */
-    int opaqueIsPresent;	/* 100% opacity & transparency are preserved */
-  }
-my_cquantizer;
-
-typedef my_cquantizer *my_cquantize_ptr;
-
-/*
- * Prescan the pixel array. 
- * 
- * The prescan simply updates the histogram, which has been
- * initialized to zeroes by start_pass.
- *
- */
-
-static void
-prescan_quantize (gdImagePtr im, my_cquantize_ptr cquantize)
-{
-  register histptr histp;
-  register hist4d histogram = cquantize->histogram;
-  int row;
-  int col;
-  int *ptr;
-  int width = im->sx;
-
-  for (row = 0; row < im->sy; row++)
-    {
-      ptr = im->tpixels[row];
-      for (col = width; col > 0; col--)
-	{
-	  /* get pixel value and index into the histogram */
-	  int r, g, b, a;
-	  r = gdTrueColorGetRed (*ptr) >> C0_SHIFT;
-	  g = gdTrueColorGetGreen (*ptr) >> C1_SHIFT;
-	  b = gdTrueColorGetBlue (*ptr) >> C2_SHIFT;
-	  a = gdTrueColorGetAlpha (*ptr);
-	  /* We must have 100% opacity and transparency available
-	     in the color map to do an acceptable job with alpha 
-	     channel, if opacity and transparency are present in the
-	     original, because of the visual properties of large
-	     flat-color border areas (requiring 100% transparency) 
-	     and the behavior of poorly implemented browsers 
-	     (requiring 100% opacity). Test for the presence of
-	     these here, and rescale the most opaque and transparent
-	     palette entries at the end if so. This avoids the need
-	     to develop a fuller understanding I have not been able
-	     to reach so far in my study of this subject. TBB */
-	  if (a == gdAlphaTransparent)
-	    {
-	      cquantize->transparentIsPresent = 1;
-	    }
-	  if (a == gdAlphaOpaque)
-	    {
-	      cquantize->opaqueIsPresent = 1;
-	    }
-	  a >>= C3_SHIFT;
-	  histp = &histogram[r][g][b][a];
-	  /* increment, check for overflow and undo increment if so. */
-	  if (++(*histp) <= 0)
-	    (*histp)--;
-	  ptr++;
-	}
-    }
-}
-
-
-/*
- * 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;
-  int c3min, c3max;
-  /* The volume (actually 2-norm) of the box */
-  int volume;
-  /* The number of nonzero histogram cells within this box */
-  long colorcount;
-}
-box;
-
-typedef box *boxptr;
-
-static 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;
-}
-
-
-static 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 int 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;
-}
-
-
-static void
-update_box (gdImagePtr im, my_cquantize_ptr cquantize, boxptr boxp)
-/* Shrink the min/max bounds of a box to enclose only nonzero elements, */
-/* and recompute its volume and population */
-{
-  hist4d histogram = cquantize->histogram;
-  histptr histp;
-  int c0, c1, c2, c3;
-  int c0min, c0max, c1min, c1max, c2min, c2max, c3min, c3max;
-  int dist0, dist1, dist2, dist3;
-  long ccount;
-
-  c0min = boxp->c0min;
-  c0max = boxp->c0max;
-  c1min = boxp->c1min;
-  c1max = boxp->c1max;
-  c2min = boxp->c2min;
-  c2max = boxp->c2max;
-  c3min = boxp->c3min;
-  c3max = boxp->c3max;
-
-  if (c0max > c0min)
-    {
-      for (c0 = c0min; c0 <= c0max; c0++)
-	{
-	  for (c1 = c1min; c1 <= c1max; c1++)
-	    {
-	      for (c2 = c2min; c2 <= c2max; c2++)
-		{
-		  histp = &histogram[c0][c1][c2][c3min];
-		  for (c3 = c3min; c3 <= c3max; c3++)
-		    {
-		      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++)
-	    {
-	      for (c2 = c2min; c2 <= c2max; c2++)
-		{
-		  histp = &histogram[c0][c1][c2][c3min];
-		  for (c3 = c3min; c3 <= c3max; c3++)
-		    {
-		      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++)
-	{
-	  for (c2 = c2min; c2 <= c2max; c2++)
-	    {
-	      histp = &histogram[c0][c1][c2][c3min];
-	      for (c3 = c3min; c3 <= c3max; c3++)
-		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++)
-	{
-	  for (c2 = c2min; c2 <= c2max; c2++)
-	    {
-	      histp = &histogram[c0][c1][c2][c3min];
-	      for (c3 = c3min; c3 <= c3max; c3++)
-		if (*histp++ != 0)
-		  {
-		    boxp->c1max = c1max = c1;
-		    goto have_c1max;
-		  }
-	    }
-	}
-have_c1max:
-  /* The original version hand-rolled the array lookup a little, but
-     with four dimensions, I don't even want to think about it. TBB */
-  if (c2max > c2min)
-    for (c2 = c2min; c2 <= c2max; c2++)
-      for (c0 = c0min; c0 <= c0max; c0++)
-	for (c1 = c1min; c1 <= c1max; c1++)
-	  for (c3 = c3min; c3 <= c3max; c3++)
-	    if (histogram[c0][c1][c2][c3] != 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++)
-	for (c1 = c1min; c1 <= c1max; c1++)
-	  for (c3 = c3min; c3 <= c3max; c3++)
-	    if (histogram[c0][c1][c2][c3] != 0)
-	      {
-		boxp->c2max = c2max = c2;
-		goto have_c2max;
-	      }
-have_c2max:
-  if (c3max > c3min)
-    for (c3 = c3min; c3 <= c3max; c3++)
-      for (c0 = c0min; c0 <= c0max; c0++)
-	for (c1 = c1min; c1 <= c1max; c1++)
-	  for (c2 = c2min; c2 <= c2max; c2++)
-	    if (histogram[c0][c1][c2][c3] != 0)
-	      {
-		boxp->c3min = c3min = c3;
-		goto have_c3min;
-	      }
-have_c3min:
-  if (c3max > c3min)
-    for (c3 = c3max; c3 >= c3min; c3--)
-      for (c0 = c0min; c0 <= c0max; c0++)
-	for (c1 = c1min; c1 <= c1max; c1++)
-	  for (c2 = c2min; c2 <= c2max; c2++)
-	    if (histogram[c0][c1][c2][c3] != 0)
-	      {
-		boxp->c3max = c3max = c3;
-		goto have_c3max;
-	      }
-have_c3max:
-  /* 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 8-bit units to get consistent distances; 
-   * after which, we scale according to the selected distance scale factors.
-   * TBB: alpha shifts back to 7 bit units. That was accounted for in the
-   * alpha scale factor. 
-   */
-  dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE;
-  dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE;
-  dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE;
-  dist3 = ((c3max - c3min) << C3_SHIFT) * C3_SCALE;
-  boxp->volume = dist0 * dist0 + dist1 * dist1 + dist2 * dist2 + dist3 * dist3;
-
-  /* Now scan remaining volume of box and compute population */
-  ccount = 0;
-  for (c0 = c0min; c0 <= c0max; c0++)
-    for (c1 = c1min; c1 <= c1max; c1++)
-      for (c2 = c2min; c2 <= c2max; c2++)
-	{
-	  histp = &histogram[c0][c1][c2][c3min];
-	  for (c3 = c3min; c3 <= c3max; c3++, histp++)
-	    if (*histp != 0)
-	      {
-		ccount++;
-	      }
-	}
-  boxp->colorcount = ccount;
-}
-
-
-static int
-median_cut (gdImagePtr im, my_cquantize_ptr cquantize,
-	    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, c3, 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->c3max = b1->c3max;
-      b2->c0min = b1->c0min;
-      b2->c1min = b1->c1min;
-      b2->c2min = b1->c2min;
-      b2->c3min = b1->c3min;
-      /* 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;
-      c3 = ((b1->c3max - b1->c3min) << C3_SHIFT) * C3_SCALE;
-      /* We want to break any ties in favor of green, then red, then blue, 
-         with alpha last. */
-      cmax = c1;
-      n = 1;
-      if (c0 > cmax)
-	{
-	  cmax = c0;
-	  n = 0;
-	}
-      if (c2 > cmax)
-	{
-	  cmax = c2;
-	  n = 2;
-	}
-      if (c3 > cmax)
-	{
-	  n = 3;
-	}
-      /* 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;
-	case 3:
-	  lb = (b1->c3max + b1->c3min) / 2;
-	  b1->c3max = lb;
-	  b2->c3min = lb + 1;
-	  break;
-	}
-      /* Update stats for boxes */
-      update_box (im, cquantize, b1);
-      update_box (im, cquantize, b2);
-      numboxes++;
-    }
-  return numboxes;
-}
-
-
-static void
-compute_color (gdImagePtr im, my_cquantize_ptr cquantize,
-	       boxptr boxp, int icolor)
-/* 
-   Compute representative color for a box, put it in 
-   palette index icolor */
-{
-  /* Current algorithm: mean weighted by pixels (not colors) */
-  /* Note it is important to get the rounding correct! */
-  hist4d histogram = cquantize->histogram;
-  histptr histp;
-  int c0, c1, c2, c3;
-  int c0min, c0max, c1min, c1max, c2min, c2max, c3min, c3max;
-  long count;
-  long total = 0;
-  long c0total = 0;
-  long c1total = 0;
-  long c2total = 0;
-  long c3total = 0;
-
-  c0min = boxp->c0min;
-  c0max = boxp->c0max;
-  c1min = boxp->c1min;
-  c1max = boxp->c1max;
-  c2min = boxp->c2min;
-  c2max = boxp->c2max;
-  c3min = boxp->c3min;
-  c3max = boxp->c3max;
-
-  for (c0 = c0min; c0 <= c0max; c0++)
-    {
-      for (c1 = c1min; c1 <= c1max; c1++)
-	{
-	  for (c2 = c2min; c2 <= c2max; c2++)
-	    {
-	      histp = &histogram[c0][c1][c2][c3min];
-	      for (c3 = c3min; c3 <= c3max; c3++)
-		{
-		  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;
-		      c3total += ((c3 << C3_SHIFT) + ((1 << C3_SHIFT) >> 1)) * count;
-		    }
-		}
-	    }
-	}
-    }
-  im->red[icolor] = (int) ((c0total + (total >> 1)) / total);
-  im->green[icolor] = (int) ((c1total + (total >> 1)) / total);
-  im->blue[icolor] = (int) ((c2total + (total >> 1)) / total);
-  im->alpha[icolor] = (int) ((c3total + (total >> 1)) / total);
-  im->open[icolor] = 0;
-  if (im->colorsTotal <= icolor)
-    {
-      im->colorsTotal = icolor + 1;
-    }
-}
-
-static void
-select_colors (gdImagePtr im, my_cquantize_ptr cquantize, int desired_colors)
-/* Master routine for color selection */
-{
-  boxptr boxlist;
-  int numboxes;
-  int i;
-
-  /* Allocate workspace for box list */
-  boxlist = (boxptr) gdMalloc (desired_colors * sizeof (box));
-  /* Initialize one box containing whole space */
-  numboxes = 1;
-  /* Note maxval for alpha is different */
-  boxlist[0].c0min = 0;
-  boxlist[0].c0max = 255 >> C0_SHIFT;
-  boxlist[0].c1min = 0;
-  boxlist[0].c1max = 255 >> C1_SHIFT;
-  boxlist[0].c2min = 0;
-  boxlist[0].c2max = 255 >> C2_SHIFT;
-  boxlist[0].c3min = 0;
-  boxlist[0].c3max = gdAlphaMax >> C3_SHIFT;
-  /* Shrink it to actually-used volume and set its statistics */
-  update_box (im, cquantize, &boxlist[0]);
-  /* Perform median-cut to produce final box list */
-  numboxes = median_cut (im, cquantize, boxlist, numboxes, desired_colors);
-  /* Compute the representative color for each box, fill colormap */
-  for (i = 0; i < numboxes; i++)
-    compute_color (im, cquantize, &boxlist[i], i);
-  /* TBB: if the image contains colors at both scaled ends
-     of the alpha range, rescale slightly to make sure alpha 
-     covers the full spectrum from 100% transparent to 100% 
-     opaque. Even a faint distinct background color is 
-     generally considered failure with regard to alpha. */
-
-  im->colorsTotal = numboxes;
-  gdFree (boxlist);
-}
-
-
-/*
- * 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_C3_LOG  (HIST_C3_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_C3_ELEMS  (1<<BOX_C3_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)
-#define BOX_C3_SHIFT  (C3_SHIFT + BOX_C3_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.
- */
-
-static int
-find_nearby_colors (gdImagePtr im, my_cquantize_ptr cquantize,
-		int minc0, int minc1, int minc2, int minc3, int 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 = im->colorsTotal;
-  int maxc0, maxc1, maxc2, maxc3;
-  int centerc0, centerc1, centerc2, centerc3;
-  int i, x, ncolors;
-  int minmaxdist, min_dist, max_dist, tdist;
-  int 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;
-  maxc3 = minc3 + ((1 << BOX_C3_SHIFT) - (1 << C3_SHIFT));
-  centerc3 = (minc3 + maxc3) >> 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 three. */
-      x = im->red[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 = im->green[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 = im->blue[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;
-	    }
-	}
-
-      x = im->alpha[i];
-      if (x < minc3)
-	{
-	  tdist = (x - minc3) * C3_SCALE;
-	  min_dist += tdist * tdist;
-	  tdist = (x - maxc3) * C3_SCALE;
-	  max_dist += tdist * tdist;
-	}
-      else if (x > maxc3)
-	{
-	  tdist = (x - maxc3) * C3_SCALE;
-	  min_dist += tdist * tdist;
-	  tdist = (x - minc3) * C3_SCALE;
-	  max_dist += tdist * tdist;
-	}
-      else
-	{
-	  /* within cell range so no contribution to min_dist */
-	  if (x <= centerc3)
-	    {
-	      tdist = (x - maxc3) * C3_SCALE;
-	      max_dist += tdist * tdist;
-	    }
-	  else
-	    {
-	      tdist = (x - minc3) * C3_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++] = i;
-    }
-  return ncolors;
-}
-
-
-static void
-find_best_colors (gdImagePtr im, my_cquantize_ptr cquantize,
-		  int minc0, int minc1, int minc2, int minc3,
-		  int numcolors, int colorlist[], int 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 int *bptr;		/* pointer into bestdist[] array */
-  int *cptr;			/* pointer into bestcolor[] array */
-  int dist0, dist1, dist2;	/* initial distance values */
-  int xx0, xx1, xx2;		/* distance increments */
-  int inc0, inc1, inc2, inc3;	/* initial values for increments */
-  /* This array holds the distance to the nearest-so-far color for each cell */
-  int bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS * BOX_C3_ELEMS];
-
-  /* Initialize best-distance for each cell of the update box */
-  bptr = bestdist;
-  for (i = BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS * BOX_C3_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)
-#define STEP_C3  ((1 << C3_SHIFT) * C3_SCALE)
-
-	for (i = 0; i < numcolors; i++)	{
-		icolor = colorlist[i];
-		/* Compute (square of) distance from minc0/c1/c2 to this color */
-		inc0 = (minc0 - (im->red[icolor])) * C0_SCALE;
-		dist0 = inc0 * inc0;
-		inc1 = (minc1 - (im->green[icolor])) * C1_SCALE;
-		dist0 += inc1 * inc1;
-		inc2 = (minc2 - (im->blue[icolor])) * C2_SCALE;
-		dist0 += inc2 * inc2;
-		inc3 = (minc3 - (im->alpha[icolor])) * C3_SCALE;
-		dist0 += inc3 * inc3;
-		/* 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;
-		inc3 = inc3 * (2 * STEP_C3) + STEP_C3 * STEP_C3;
-		/* 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--) {
-					register int dist3 = dist2;		/* current distance in inner loop */
-					register int xx3 = inc3;
-					register int ic3;
-					for (ic3 = BOX_C3_ELEMS - 1; ic3 >= 0; ic3--) {
-						if (dist3 < *bptr) {
-							*bptr = dist3;
-							*cptr = icolor;
-						}
-						dist3 += xx3;
-						xx3 += 2 * STEP_C3 * STEP_C3;
-						bptr++;
-						cptr++;
-					}
-					dist2 += xx2;
-					xx2 += 2 * STEP_C2 * STEP_C2;
-				}
-				dist1 += xx1;
-				xx1 += 2 * STEP_C1 * STEP_C1;
-			}
-			dist0 += xx0;
-			xx0 += 2 * STEP_C0 * STEP_C0;
-		}
-	}
-}
-
-
-static void
-fill_inverse_cmap (gdImagePtr im, my_cquantize_ptr cquantize,
-		   int c0, int c1, int c2, int c3)
-/* Fill the inverse-colormap entries in the update box that contains */
-/* histogram cell c0/c1/c2/c3.  (Only that one cell MUST be filled, but */
-/* we can fill as many others as we wish.) */
-{
-  hist4d histogram = cquantize->histogram;
-  int minc0, minc1, minc2, minc3;	/* lower left corner of update box */
-  int ic0, ic1, ic2, ic3;
-  register int *cptr;		/* pointer into bestcolor[] array */
-  register histptr cachep;	/* pointer into main cache array */
-  /* This array lists the candidate colormap indexes. */
-  int colorlist[MAXNUMCOLORS];
-  int numcolors;		/* number of candidate colors */
-  /* This array holds the actually closest colormap index for each cell. */
-  int bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS * BOX_C3_ELEMS];
-
-  /* Convert cell coordinates to update box ID */
-  c0 >>= BOX_C0_LOG;
-  c1 >>= BOX_C1_LOG;
-  c2 >>= BOX_C2_LOG;
-  c3 >>= BOX_C3_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);
-  minc3 = (c3 << BOX_C3_SHIFT) + ((1 << C3_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 (im, cquantize, minc0, minc1, minc2, minc3, colorlist);
-
-  /* Determine the actually nearest colors. */
-  find_best_colors (im, cquantize, minc0, minc1, minc2, minc3, 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;
-  c3 <<= BOX_C3_LOG;
-  cptr = bestcolor;
-  for (ic0 = 0; ic0 < BOX_C0_ELEMS; ic0++)
-    {
-      for (ic1 = 0; ic1 < BOX_C1_ELEMS; ic1++)
-	{
-	  for (ic2 = 0; ic2 < BOX_C2_ELEMS; ic2++)
-	    {
-	      cachep = &histogram[c0 + ic0][c1 + ic1][c2 + ic2][c3];
-	      for (ic3 = 0; ic3 < BOX_C3_ELEMS; ic3++)
-		{
-		  *cachep++ = (histcell) ((*cptr++) + 1);
-		}
-	    }
-	}
-    }
-}
-
-
-/*
- * Map some rows of pixels to the output colormapped representation.
- */
-
-void
-pass2_no_dither (gdImagePtr im, my_cquantize_ptr cquantize)
-/* This version performs no dithering */
-{
-  hist4d histogram = cquantize->histogram;
-  register int *inptr;
-  register unsigned char *outptr;
-  register histptr cachep;
-  register int c0, c1, c2, c3;
-  int row;
-  int col;
-  int width = im->sx;
-  int num_rows = im->sy;
-  for (row = 0; row < num_rows; row++)
-    {
-      inptr = im->tpixels[row];
-      outptr = im->pixels[row];
-      for (col = 0; col < width; col++)
-	{
-	  int r, g, b, a;
-	  /* get pixel value and index into the cache */
-	  r = gdTrueColorGetRed (*inptr);
-	  g = gdTrueColorGetGreen (*inptr);
-	  b = gdTrueColorGetBlue (*inptr);
-	  a = gdTrueColorGetAlpha (*inptr++);
-	  c0 = r >> C0_SHIFT;
-	  c1 = g >> C1_SHIFT;
-	  c2 = b >> C2_SHIFT;
-	  c3 = a >> C3_SHIFT;
-	  cachep = &histogram[c0][c1][c2][c3];
-	  /* If we have not seen this color before, find nearest colormap entry */
-	  /* and update the cache */
-	  if (*cachep == 0)
-	    {
-#if 0
-	      /* TBB: quick and dirty approach for use when testing
-	         fill_inverse_cmap for errors */
-	      int i;
-	      int best = -1;
-	      int mindist = 0x7FFFFFFF;
-	      for (i = 0; (i < im->colorsTotal); i++)
-		{
-		  int rdist = (im->red[i] >> C0_SHIFT) - c0;
-		  int gdist = (im->green[i] >> C1_SHIFT) - c1;
-		  int bdist = (im->blue[i] >> C2_SHIFT) - c2;
-		  int adist = (im->alpha[i] >> C3_SHIFT) - c3;
-		  int dist = (rdist * rdist) * R_SCALE +
-		  (gdist * gdist) * G_SCALE +
-		  (bdist * bdist) * B_SCALE +
-		  (adist * adist) * A_SCALE;
-		  if (dist < mindist)
-		    {
-		      best = i;
-		      mindist = dist;
-		    }
-		}
-	      *cachep = best + 1;
-#endif
-	      fill_inverse_cmap (im, cquantize, c0, c1, c2, c3);
-	    }
-	  /* Now emit the colormap index for this cell */
-	  *outptr++ = (*cachep - 1);
-	}
-    }
-}
-
-/* We assume that right shift corresponds to signed division by 2 with
- * rounding towards minus infinity.  This is correct for typical "arithmetic
- * shift" instructions that shift in copies of the sign bit.  But some
- * C compilers implement >> with an unsigned shift.  For these machines you
- * must define RIGHT_SHIFT_IS_UNSIGNED.
- * RIGHT_SHIFT provides a proper signed right shift of an INT32 quantity.
- * It is only applied with constant shift counts.  SHIFT_TEMPS must be
- * included in the variables of any routine using RIGHT_SHIFT.
- */
-
-#ifdef RIGHT_SHIFT_IS_UNSIGNED
-#define SHIFT_TEMPS	INT32 shift_temp;
-#define RIGHT_SHIFT(x,shft)  \
-	((shift_temp = (x)) < 0 ? \
-	 (shift_temp >> (shft)) | ((~((INT32) 0)) << (32-(shft))) : \
-	 (shift_temp >> (shft)))
-#else
-#define SHIFT_TEMPS
-#define RIGHT_SHIFT(x,shft)	((x) >> (shft))
-#endif
-
-
-void
-pass2_fs_dither (gdImagePtr im, my_cquantize_ptr cquantize)
-
-/* This version performs Floyd-Steinberg dithering */
-{
-  hist4d histogram = cquantize->histogram;
-  register LOCFSERROR cur0, cur1, cur2, cur3;	/* current error or pixel value */
-  LOCFSERROR belowerr0, belowerr1, belowerr2, belowerr3;	/* error for pixel below cur */
-  LOCFSERROR bpreverr0, bpreverr1, bpreverr2, bpreverr3;	/* error for below/prev col */
-  register FSERRPTR errorptr;	/* => fserrors[] at column before current */
-  int *inptr;			/* => current input pixel */
-  unsigned char *outptr;	/* => current output pixel */
-  histptr cachep;
-  int dir;			/* +1 or -1 depending on direction */
-  int dir4;			/* 4*dir, for advancing errorptr */
-  int row;
-  int col;
-  int width = im->sx;
-  int num_rows = im->sy;
-  int *error_limit = cquantize->error_limiter;
-  int *colormap0 = im->red;
-  int *colormap1 = im->green;
-  int *colormap2 = im->blue;
-  int *colormap3 = im->alpha;
-  SHIFT_TEMPS
-
-    for (row = 0; row < num_rows; row++)
-    {
-      inptr = im->tpixels[row];
-      outptr = im->pixels[row];
-      if (cquantize->on_odd_row)
-	{
-	  /* work right to left in this row */
-	  inptr += (width - 1);	/* so point to rightmost pixel */
-	  outptr += width - 1;
-	  dir = -1;
-	  dir4 = -4;
-	  errorptr = cquantize->fserrors + (width + 1) * 4;	/* => entry after last column */
-	  cquantize->on_odd_row = FALSE;	/* flip for next time */
-	}
-      else
-	{
-	  /* work left to right in this row */
-	  dir = 1;
-	  dir4 = 4;
-	  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 = cur3 = 0;
-      /* and no error propagated to row below yet */
-      belowerr0 = belowerr1 = belowerr2 = belowerr3 = 0;
-      bpreverr0 = bpreverr1 = bpreverr2 = bpreverr3 = 0;
-
-      for (col = width; col > 0; col--)
-	{
-	  int a;
-	  /* 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[dir4 + 0] + 8, 4);
-	  cur1 = RIGHT_SHIFT (cur1 + errorptr[dir4 + 1] + 8, 4);
-	  cur2 = RIGHT_SHIFT (cur2 + errorptr[dir4 + 2] + 8, 4);
-	  cur3 = RIGHT_SHIFT (cur3 + errorptr[dir4 + 3] + 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];
-	  cur3 = error_limit[cur3];
-	  /* Form pixel value + error, and range-limit to 0..MAXJSAMPLE.
-	   * The maximum error is +- MAXJSAMPLE (or less with error limiting);
-	   * but we'll be lazy and just clamp this with an if test (TBB).
-	   */
-	  cur0 += gdTrueColorGetRed (*inptr);
-	  cur1 += gdTrueColorGetGreen (*inptr);
-	  cur2 += gdTrueColorGetBlue (*inptr);
-	  /* Expand to 8 bits for consistency with dithering algorithm -- TBB */
-	  a = gdTrueColorGetAlpha (*inptr);
-	  cur3 += (a << 1) + (a >> 6);
-	  if (cur0 < 0)
-	    {
-	      cur0 = 0;
-	    }
-	  if (cur0 > 255)
-	    {
-	      cur0 = 255;
-	    }
-	  if (cur1 < 0)
-	    {
-	      cur1 = 0;
-	    }
-	  if (cur1 > 255)
-	    {
-	      cur1 = 255;
-	    }
-	  if (cur2 < 0)
-	    {
-	      cur2 = 0;
-	    }
-	  if (cur2 > 255)
-	    {
-	      cur2 = 255;
-	    }
-	  if (cur3 < 0)
-	    {
-	      cur3 = 0;
-	    }
-	  if (cur3 > 255)
-	    {
-	      cur3 = 255;
-	    }
-	  /* Index into the cache with adjusted pixel value */
-	  cachep = &histogram
-	    [cur0 >> C0_SHIFT]
-	    [cur1 >> C1_SHIFT]
-	    [cur2 >> C2_SHIFT]
-	    [cur3 >> (C3_SHIFT + 1)];
-	  /* If we have not seen this color before, find nearest colormap */
-	  /* entry and update the cache */
-	  if (*cachep == 0)
-	    fill_inverse_cmap (im, cquantize,
-		       cur0 >> C0_SHIFT, cur1 >> C1_SHIFT, cur2 >> C2_SHIFT,
-			       cur3 >> (C3_SHIFT + 1));
-	  /* Now emit the colormap index for this cell */
-	  {
-	    register int pixcode = *cachep - 1;
-	    *outptr = pixcode;
-	    /* Compute representation error for this pixel */
-	    cur0 -= colormap0[pixcode];
-	    cur1 -= colormap1[pixcode];
-	    cur2 -= colormap2[pixcode];
-	    cur3 -= ((colormap3[pixcode] << 1) + (colormap3[pixcode] >> 6));
-	  }
-	  /* 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 */
-	    bnexterr = cur3;	/* Process component 3 */
-	    delta = cur3 * 2;
-	    cur3 += delta;	/* form error * 3 */
-	    errorptr[3] = (FSERROR) (bpreverr3 + cur3);
-	    cur3 += delta;	/* form error * 5 */
-	    bpreverr3 = belowerr3 + cur3;
-	    belowerr3 = bnexterr;
-	    cur3 += 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 += dir;		/* Advance pixel pointers to next column */
-	  outptr += dir;
-	  errorptr += dir4;	/* 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;
-      errorptr[3] = (FSERROR) bpreverr3;
-    }
-}
-
-
-/*
- * 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.
- */
-
-static int
-init_error_limit (gdImagePtr im, my_cquantize_ptr cquantize)
-/* Allocate and fill in the error_limiter table */
-{
-  int *table;
-  int in, out;
-
-  cquantize->error_limiter_storage = (int *) gdMalloc ((255 * 2 + 1) * sizeof (int));
-  if (!cquantize->error_limiter_storage)
-    {
-      return 0;
-    }
-  /* so can index -MAXJSAMPLE .. +MAXJSAMPLE */
-  cquantize->error_limiter = cquantize->error_limiter_storage + 255;
-  table = cquantize->error_limiter;
-#define STEPSIZE ((255+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 <= 255; in++)
-    {
-      table[in] = out;
-      table[-in] = -out;
-    }
-#undef STEPSIZE
-  return 1;
-}
-
-static void
-zeroHistogram (hist4d histogram)
-{
-  int i;
-  int j;
-  /* Zero the histogram or inverse color map */
-  for (i = 0; i < HIST_C0_ELEMS; i++)
-    {
-      for (j = 0; j < HIST_C1_ELEMS; j++)
-	{
-	  memset (histogram[i][j],
-		  0,
-		  HIST_C2_ELEMS * HIST_C3_ELEMS * sizeof (histcell));
-	}
-    }
-}
-
-/* Here we go at last. */
-void
-gdImageTrueColorToPalette (gdImagePtr im, int dither, int colorsWanted)
-{
-	my_cquantize_ptr cquantize = 0;
-	int i;
-	size_t arraysize;
-	if (!im->trueColor || colorsWanted <= 0) {
-		/* Nothing to do! */
-		return;
-	}
-	
-	if (colorsWanted > gdMaxColors) {
-		colorsWanted = gdMaxColors;
-	}
-
-	im->pixels = gdCalloc (sizeof (unsigned char *), im->sy);
-
-	for (i = 0; i < im->sy; i++) {
-		im->pixels[i] = gdCalloc (sizeof (unsigned char *), im->sx);
-	}
-	cquantize = (my_cquantize_ptr) gdCalloc (sizeof (my_cquantizer), 1);
-
-	/* Allocate the histogram/inverse colormap storage */
-	cquantize->histogram = (hist4d) gdMalloc (HIST_C0_ELEMS * sizeof (hist3d));
-	for (i = 0; i < HIST_C0_ELEMS; i++) {
-		int j;
-		cquantize->histogram[i] = (hist3d) gdCalloc (HIST_C1_ELEMS, sizeof (hist2d));
-		for (j = 0; j < HIST_C1_ELEMS; j++) {
-			cquantize->histogram[i][j] = (hist2d) gdCalloc (HIST_C2_ELEMS * HIST_C3_ELEMS, sizeof (histcell));
-		}
-	}
-
-	cquantize->fserrors = (FSERRPTR) gdMalloc (4 * sizeof (FSERROR));
-	init_error_limit (im, cquantize);
-	arraysize = (size_t) ((im->sx + 2) * (4 * sizeof (FSERROR)));
-	gdFree(cquantize->fserrors);
-	/* Allocate Floyd-Steinberg workspace. */
-	cquantize->fserrors = gdCalloc (arraysize, 1);
-	cquantize->on_odd_row = FALSE;
-
-	/* Do the work! */
-	zeroHistogram (cquantize->histogram);
-	prescan_quantize (im, cquantize);
-	select_colors (im, cquantize, colorsWanted);
-
-	zeroHistogram (cquantize->histogram);
-	if (dither) {
-		pass2_fs_dither (im, cquantize);
-	} else {
-		pass2_no_dither (im, cquantize);
-	}
-	if (cquantize->transparentIsPresent) {
-		int mt = -1;
-		int mtIndex = -1;
-		for (i = 0; i < im->colorsTotal; i++) {
-			if (im->alpha[i] > mt) {
-				mtIndex = i;
-				mt = im->alpha[i];
-			}
-		}
-		for (i = 0; i < im->colorsTotal; i++) {
-			if (im->alpha[i] == mt) {
-				im->alpha[i] = gdAlphaTransparent;
-			}
-		}
-	}
-	if (cquantize->opaqueIsPresent) {
-		int mo = 128;
-		int moIndex = -1;
-		for (i = 0; i < im->colorsTotal; i++) {
-			if (im->alpha[i] < mo) {
-				moIndex = i;
-				mo = im->alpha[i];
-			}
-		}
-		for (i = 0; i < im->colorsTotal; i++) {
-			if (im->alpha[i] == mo) {
-				im->alpha[i] = gdAlphaOpaque;
-			}
-		}
-	}
-	
-	/* Success! Get rid of the truecolor image data. */
-	im->trueColor = 0;
-	/* Junk the truecolor pixels */
-	for (i = 0; i < im->sy; i++) {
-		gdFree(im->tpixels[i]);
-	}
-	gdFree (im->tpixels);
-	im->tpixels = 0;
-	/* Tediously free stuff. */
-
-	for (i = 0; i < HIST_C0_ELEMS; i++) {
-		if (cquantize->histogram[i]) {
-			int j;
-			for (j = 0; j < HIST_C1_ELEMS; j++) {
-				if (cquantize->histogram[i][j]) {
-					gdFree(cquantize->histogram[i][j]);
-				}
-			}
-			gdFree(cquantize->histogram[i]);
-		}
-	}
-	if (cquantize->histogram) {
-		gdFree(cquantize->histogram);
-	}
-	if (cquantize->fserrors) {
-		gdFree(cquantize->fserrors);
-	}
-	if (cquantize->error_limiter_storage) {
-		gdFree(cquantize->error_limiter_storage);
-	}
-	if (cquantize) {
-		gdFree(cquantize);
-	}
-}
-
-/* bring the palette colors in im2 to be closer to im1
- *
- */
-int
-gdImageColorMatch (gdImagePtr im1, gdImagePtr im2)
-{
-	unsigned long *buf; /* stores our calculations */
-	unsigned long *bp; /* buf ptr */
-	int color, rgb;
-	int x,y;
-	int count;
-
-	if( !im1->trueColor ) {
-		return -1; /* im1 must be True Color */
-	}
-	if( im2->trueColor ) {
-		return -2; /* im2 must be indexed */
-	}
-	if( (im1->sx != im2->sx) || (im1->sy != im2->sy) ) {
-		return -3; /* the images are meant to be the same dimensions */
-	}
-
-	buf = (unsigned long *)gdMalloc( sizeof(unsigned long) * 5 * im2->colorsTotal );
-	memset( buf, 0, sizeof(unsigned long) * 5 * im2->colorsTotal );
-
-	for( x=0; x<im1->sx; x++ ) {
-		for( y=0; y<im1->sy; y++ ) {
-			color = im2->pixels[y][x];
-			rgb = im1->tpixels[y][x];
-			bp = buf + (color * 5);
-			(*(bp++))++;
-			*(bp++) += gdTrueColorGetRed(rgb);
-			*(bp++) += gdTrueColorGetGreen(rgb);
-			*(bp++) += gdTrueColorGetBlue(rgb);
-			*(bp++) += gdTrueColorGetAlpha(rgb);
-		}
-	}
-	bp = buf;
-	for( color=0; color<im2->colorsTotal; color++ ) {
-		count = *(bp++);
-		if( count > 0 ) {
-			im2->red[color]		= *(bp++) / count;
-			im2->green[color]	= *(bp++) / count;
-			im2->blue[color]	= *(bp++) / count;
-			im2->alpha[color]	= *(bp++) / count;
-		} else {
-			bp += 4;
-		}
-	}
-	gdFree(buf);
-	return 0;
-}