The Tor Browser: comparison media/libjpeg/jquant2.c

--1:000000000000
+:402e9a988597
+/*
+* jquant2.c
+*
+* This file was part of the Independent JPEG Group's software:
+* Copyright (C) 1991-1996, Thomas G. Lane.
+* libjpeg-turbo Modifications:
+* Copyright (C) 2009, D. R. Commander.
+* For conditions of distribution and use, see the accompanying README file.
+*
+* This file contains 2-pass color quantization (color mapping) routines.
+* These routines provide selection of a custom color map for an image,
+* followed by mapping of the image to that color map, with optional
+* Floyd-Steinberg dithering.
+* It is also possible to use just the second pass to map to an arbitrary
+* externally-given color map.
+*
+* Note: ordered dithering is not supported, since there isn't any fast
+* way to compute intercolor distances; it's unclear that ordered dither's
+* fundamental assumptions even hold with an irregularly spaced color map.
+*/
+#define JPEG_INTERNALS
+#include "jinclude.h"
+#include "jpeglib.h"
+#ifdef QUANT_2PASS_SUPPORTED
+/*
+* This module implements the well-known Heckbert paradigm for color
+* quantization.  Most of the ideas used here can be traced back to
+* Heckbert's seminal paper
+*   Heckbert, Paul.  "Color Image Quantization for Frame Buffer Display",
+*   Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304.
+*
+* In the first pass over the image, we accumulate a histogram showing the
+* usage count of each possible color.  To keep the histogram to a reasonable
+* size, we reduce the precision of the input; typical practice is to retain
+* 5 or 6 bits per color, so that 8 or 4 different input values are counted
+* in the same histogram cell.
+*
+* Next, the color-selection step begins with a box representing the whole
+* color space, and repeatedly splits the "largest" remaining box until we
+* have as many boxes as desired colors.  Then the mean color in each
+* remaining box becomes one of the possible output colors.
+*
+* The second pass over the image maps each input pixel to the closest output
+* color (optionally after applying a Floyd-Steinberg dithering correction).
+* This mapping is logically trivial, but making it go fast enough requires
+* considerable care.
+*
+* Heckbert-style quantizers vary a good deal in their policies for choosing
+* the "largest" box and deciding where to cut it.  The particular policies
+* used here have proved out well in experimental comparisons, but better ones
+* may yet be found.
+*
+* In earlier versions of the IJG code, this module quantized in YCbCr color
+* space, processing the raw upsampled data without a color conversion step.
+* This allowed the color conversion math to be done only once per colormap
+* entry, not once per pixel.  However, that optimization precluded other
+* useful optimizations (such as merging color conversion with upsampling)
+* and it also interfered with desired capabilities such as quantizing to an
+* externally-supplied colormap.  We have therefore abandoned that approach.
+* The present code works in the post-conversion color space, typically RGB.
+*
+* To improve the visual quality of the results, we actually work in scaled
+* RGB space, giving G distances more weight than R, and R in turn more than
+* B.  To do everything in integer math, we must use integer scale factors.
+* The 2/3/1 scale factors used here correspond loosely to the relative
+* weights of the colors in the NTSC grayscale equation.
+* If you want to use this code to quantize a non-RGB color space, you'll
+* probably need to change these scale factors.
+*/
+#define R_SCALE 2		/* scale R distances by this much */
+#define G_SCALE 3		/* scale G distances by this much */
+#define B_SCALE 1		/* and B by this much */
+static const int c_scales[3]={R_SCALE, G_SCALE, B_SCALE};
+#define C0_SCALE c_scales[rgb_red[cinfo->out_color_space]]
+#define C1_SCALE c_scales[rgb_green[cinfo->out_color_space]]
+#define C2_SCALE c_scales[rgb_blue[cinfo->out_color_space]]
+/*
+* First we have the histogram data structure and routines for creating it.
+*
+* The number of bits of precision can be adjusted by changing these symbols.
+* We recommend keeping 6 bits for G and 5 each for R and B.
+* If you have plenty of memory and cycles, 6 bits all around gives marginally
+* better results; if you are short of memory, 5 bits all around will save
+* some space but degrade the results.
+* To maintain a fully accurate histogram, we'd need to allocate a "long"
+* (preferably unsigned long) for each cell.  In practice this is overkill;
+* we can get by with 16 bits per cell.  Few of the cell counts will overflow,
+* and clamping those that do overflow to the maximum value will give close-
+* enough results.  This reduces the recommended histogram size from 256Kb
+* to 128Kb, which is a useful savings on PC-class machines.
+* (In the second pass the histogram space is re-used for pixel mapping data;
+* in that capacity, each cell must be able to store zero to the number of
+* desired colors.  16 bits/cell is plenty for that too.)
+* Since the JPEG code is intended to run in small memory model on 80x86
+* machines, we can't just allocate the histogram in one chunk.  Instead
+* of a true 3-D array, we use a row of pointers to 2-D arrays.  Each
+* pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and
+* each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries.  Note that
+* on 80x86 machines, the pointer row is in near memory but the actual
+* arrays are in far memory (same arrangement as we use for image arrays).
+*/
+#define MAXNUMCOLORS  (MAXJSAMPLE+1) /* maximum size of colormap */
+/* These will do the right thing for either R,G,B or B,G,R color order,
+* but you may not like the results for other color orders.
+*/
+#define HIST_C0_BITS  5		/* bits of precision in R/B histogram */
+#define HIST_C1_BITS  6		/* bits of precision in G histogram */
+#define HIST_C2_BITS  5		/* bits of precision in B/R histogram */
+/* Number of elements along histogram axes. */
+#define HIST_C0_ELEMS  (1<<HIST_C0_BITS)
+#define HIST_C1_ELEMS  (1<<HIST_C1_BITS)
+#define HIST_C2_ELEMS  (1<<HIST_C2_BITS)
+/* These are the amounts to shift an input value to get a histogram index. */
+#define C0_SHIFT  (BITS_IN_JSAMPLE-HIST_C0_BITS)
+#define C1_SHIFT  (BITS_IN_JSAMPLE-HIST_C1_BITS)
+#define C2_SHIFT  (BITS_IN_JSAMPLE-HIST_C2_BITS)
+typedef UINT16 histcell;	/* histogram cell; prefer an unsigned type */
+typedef histcell FAR * histptr;	/* for pointers to histogram cells */
+typedef histcell hist1d[HIST_C2_ELEMS]; /* typedefs for the array */
+typedef hist1d FAR * hist2d;	/* type for the 2nd-level pointers */
+typedef hist2d * hist3d;	/* type for top-level pointer */
+/* Declarations for Floyd-Steinberg dithering.
+*
+* Errors are accumulated into the array fserrors[], at a resolution of
+* 1/16th of a pixel count.  The error at a given pixel is propagated
+* to its not-yet-processed neighbors using the standard F-S fractions,
+*		...	(here)	7/16
+*		3/16	5/16	1/16
+* We work left-to-right on even rows, right-to-left on odd rows.
+*
+* We can get away with a single array (holding one row's worth of errors)
+* by using it to store the current row's errors at pixel columns not yet
+* processed, but the next row's errors at columns already processed.  We
+* need only a few extra variables to hold the errors immediately around the
+* current column.  (If we are lucky, those variables are in registers, but
+* even if not, they're probably cheaper to access than array elements are.)
+*
+* The fserrors[] array has (#columns + 2) entries; the extra entry at
+* each end saves us from special-casing the first and last pixels.
+* Each entry is three values long, one value for each color component.
+*
+* Note: on a wide image, we might not have enough room in a PC's near data
+* segment to hold the error array; so it is allocated with alloc_large.
+*/
+#if BITS_IN_JSAMPLE == 8
+typedef INT16 FSERROR;		/* 16 bits should be enough */
+typedef int LOCFSERROR;		/* use 'int' for calculation temps */
+#else
+typedef INT32 FSERROR;		/* may need more than 16 bits */
+typedef INT32 LOCFSERROR;	/* be sure calculation temps are big enough */
+#endif
+typedef FSERROR FAR *FSERRPTR;	/* pointer to error array (in FAR storage!) */
+/* Private subobject */
+typedef struct {
+struct jpeg_color_quantizer pub; /* public fields */
+/* Space for the eventually created colormap is stashed here */
+JSAMPARRAY sv_colormap;	/* colormap allocated at init time */
+int desired;			/* desired # of colors = size of colormap */
+/* Variables for accumulating image statistics */
+hist3d histogram;		/* pointer to the histogram */
+boolean needs_zeroed;		/* TRUE if next pass must zero histogram */
+/* Variables for Floyd-Steinberg dithering */
+FSERRPTR fserrors;		/* accumulated errors */
+boolean on_odd_row;		/* flag to remember which row we are on */
+int * error_limiter;		/* table for clamping the applied error */
+} my_cquantizer;
+typedef my_cquantizer * my_cquantize_ptr;
+/*
+* Prescan some rows of pixels.
+* In this module the prescan simply updates the histogram, which has been
+* initialized to zeroes by start_pass.
+* An output_buf parameter is required by the method signature, but no data
+* is actually output (in fact the buffer controller is probably passing a
+* NULL pointer).
+*/
+METHODDEF(void)
+prescan_quantize (j_decompress_ptr cinfo, JSAMPARRAY input_buf,
+		  JSAMPARRAY output_buf, int num_rows)
+{
+my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
+register JSAMPROW ptr;
+register histptr histp;
+register hist3d histogram = cquantize->histogram;
+int row;
+JDIMENSION col;
+JDIMENSION width = cinfo->output_width;
+for (row = 0; row < num_rows; row++) {
+ptr = input_buf[row];
+for (col = width; col > 0; col--) {
+/* get pixel value and index into the histogram */
+histp = & histogram[GETJSAMPLE(ptr[0]) >> C0_SHIFT]
+			 [GETJSAMPLE(ptr[1]) >> C1_SHIFT]
+			 [GETJSAMPLE(ptr[2]) >> C2_SHIFT];
+/* increment, check for overflow and undo increment if so. */
+if (++(*histp) <= 0)
+	(*histp)--;
+ptr += 3;
+}
+}
+}
+/*
+* Next we have the really interesting routines: selection of a colormap
+* given the completed histogram.
+* These routines work with a list of "boxes", each representing a rectangular
+* subset of the input color space (to histogram precision).
+*/
+typedef struct {
+/* The bounds of the box (inclusive); expressed as histogram indexes */
+int c0min, c0max;
+int c1min, c1max;
+int c2min, c2max;
+/* The volume (actually 2-norm) of the box */
+INT32 volume;
+/* The number of nonzero histogram cells within this box */
+long colorcount;
+} box;
+typedef box * boxptr;
+LOCAL(boxptr)
+find_biggest_color_pop (boxptr boxlist, int numboxes)
+/* Find the splittable box with the largest color population */
+/* Returns NULL if no splittable boxes remain */
+{
+register boxptr boxp;
+register int i;
+register long maxc = 0;
+boxptr which = NULL;
+for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
+if (boxp->colorcount > maxc && boxp->volume > 0) {
+which = boxp;
+maxc = boxp->colorcount;
+}
+}
+return which;
+}
+LOCAL(boxptr)
+find_biggest_volume (boxptr boxlist, int numboxes)
+/* Find the splittable box with the largest (scaled) volume */
+/* Returns NULL if no splittable boxes remain */
+{
+register boxptr boxp;
+register int i;
+register INT32 maxv = 0;
+boxptr which = NULL;
+for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
+if (boxp->volume > maxv) {
+which = boxp;
+maxv = boxp->volume;
+}
+}
+return which;
+}
+LOCAL(void)
+update_box (j_decompress_ptr cinfo, boxptr boxp)
+/* Shrink the min/max bounds of a box to enclose only nonzero elements, */
+/* and recompute its volume and population */
+{
+my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
+hist3d histogram = cquantize->histogram;
+histptr histp;
+int c0,c1,c2;
+int c0min,c0max,c1min,c1max,c2min,c2max;
+INT32 dist0,dist1,dist2;
+long ccount;
+c0min = boxp->c0min;  c0max = boxp->c0max;
+c1min = boxp->c1min;  c1max = boxp->c1max;
+c2min = boxp->c2min;  c2max = boxp->c2max;
+if (c0max > c0min)
+for (c0 = c0min; c0 <= c0max; c0++)
+for (c1 = c1min; c1 <= c1max; c1++) {
+	histp = & histogram[c0][c1][c2min];
+	for (c2 = c2min; c2 <= c2max; c2++)
+	  if (*histp++ != 0) {
+	    boxp->c0min = c0min = c0;
+	    goto have_c0min;
+	  }
+}
+have_c0min:
+if (c0max > c0min)
+for (c0 = c0max; c0 >= c0min; c0--)
+for (c1 = c1min; c1 <= c1max; c1++) {
+	histp = & histogram[c0][c1][c2min];
+	for (c2 = c2min; c2 <= c2max; c2++)
+	  if (*histp++ != 0) {
+	    boxp->c0max = c0max = c0;
+	    goto have_c0max;
+	  }
+}
+have_c0max:
+if (c1max > c1min)
+for (c1 = c1min; c1 <= c1max; c1++)
+for (c0 = c0min; c0 <= c0max; c0++) {
+	histp = & histogram[c0][c1][c2min];
+	for (c2 = c2min; c2 <= c2max; c2++)
+	  if (*histp++ != 0) {
+	    boxp->c1min = c1min = c1;
+	    goto have_c1min;
+	  }
+}
+have_c1min:
+if (c1max > c1min)
+for (c1 = c1max; c1 >= c1min; c1--)
+for (c0 = c0min; c0 <= c0max; c0++) {
+	histp = & histogram[c0][c1][c2min];
+	for (c2 = c2min; c2 <= c2max; c2++)
+	  if (*histp++ != 0) {
+	    boxp->c1max = c1max = c1;
+	    goto have_c1max;
+	  }
+}
+have_c1max:
+if (c2max > c2min)
+for (c2 = c2min; c2 <= c2max; c2++)
+for (c0 = c0min; c0 <= c0max; c0++) {
+	histp = & histogram[c0][c1min][c2];
+	for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
+	  if (*histp != 0) {
+	    boxp->c2min = c2min = c2;
+	    goto have_c2min;
+	  }
+}
+have_c2min:
+if (c2max > c2min)
+for (c2 = c2max; c2 >= c2min; c2--)
+for (c0 = c0min; c0 <= c0max; c0++) {
+	histp = & histogram[c0][c1min][c2];
+	for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
+	  if (*histp != 0) {
+	    boxp->c2max = c2max = c2;
+	    goto have_c2max;
+	  }
+}
+have_c2max:
+/* Update box volume.
+* We use 2-norm rather than real volume here; this biases the method
+* against making long narrow boxes, and it has the side benefit that
+* a box is splittable iff norm > 0.
+* Since the differences are expressed in histogram-cell units,
+* we have to shift back to JSAMPLE units to get consistent distances;
+* after which, we scale according to the selected distance scale factors.
+*/
+dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE;
+dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE;
+dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE;
+boxp->volume = dist0*dist0 + dist1*dist1 + dist2*dist2;
+/* Now scan remaining volume of box and compute population */
+ccount = 0;
+for (c0 = c0min; c0 <= c0max; c0++)
+for (c1 = c1min; c1 <= c1max; c1++) {
+histp = & histogram[c0][c1][c2min];
+for (c2 = c2min; c2 <= c2max; c2++, histp++)
+	if (*histp != 0) {
+	  ccount++;
+	}
+}
+boxp->colorcount = ccount;
+}
+LOCAL(int)
+median_cut (j_decompress_ptr cinfo, boxptr boxlist, int numboxes,
+	    int desired_colors)
+/* Repeatedly select and split the largest box until we have enough boxes */
+{
+int n,lb;
+int c0,c1,c2,cmax;
+register boxptr b1,b2;
+while (numboxes < desired_colors) {
+/* Select box to split.
+* Current algorithm: by population for first half, then by volume.
+*/
+if (numboxes*2 <= desired_colors) {
+b1 = find_biggest_color_pop(boxlist, numboxes);
+} else {
+b1 = find_biggest_volume(boxlist, numboxes);
+}
+if (b1 == NULL)		/* no splittable boxes left! */
+break;
+b2 = &boxlist[numboxes];	/* where new box will go */
+/* Copy the color bounds to the new box. */
+b2->c0max = b1->c0max; b2->c1max = b1->c1max; b2->c2max = b1->c2max;
+b2->c0min = b1->c0min; b2->c1min = b1->c1min; b2->c2min = b1->c2min;
+/* Choose which axis to split the box on.
+* Current algorithm: longest scaled axis.
+* See notes in update_box about scaling distances.
+*/
+c0 = ((b1->c0max - b1->c0min) << C0_SHIFT) * C0_SCALE;
+c1 = ((b1->c1max - b1->c1min) << C1_SHIFT) * C1_SCALE;
+c2 = ((b1->c2max - b1->c2min) << C2_SHIFT) * C2_SCALE;
+/* We want to break any ties in favor of green, then red, blue last.
+* This code does the right thing for R,G,B or B,G,R color orders only.
+*/
+if (rgb_red[cinfo->out_color_space] == 0) {
+cmax = c1; n = 1;
+if (c0 > cmax) { cmax = c0; n = 0; }
+if (c2 > cmax) { n = 2; }
+}
+else {
+cmax = c1; n = 1;
+if (c2 > cmax) { cmax = c2; n = 2; }
+if (c0 > cmax) { n = 0; }
+}
+/* Choose split point along selected axis, and update box bounds.
+* Current algorithm: split at halfway point.
+* (Since the box has been shrunk to minimum volume,
+* any split will produce two nonempty subboxes.)
+* Note that lb value is max for lower box, so must be < old max.
+*/
+switch (n) {
+case 0:
+lb = (b1->c0max + b1->c0min) / 2;
+b1->c0max = lb;
+b2->c0min = lb+1;
+break;
+case 1:
+lb = (b1->c1max + b1->c1min) / 2;
+b1->c1max = lb;
+b2->c1min = lb+1;
+break;
+case 2:
+lb = (b1->c2max + b1->c2min) / 2;
+b1->c2max = lb;
+b2->c2min = lb+1;
+break;
+}
+/* Update stats for boxes */
+update_box(cinfo, b1);
+update_box(cinfo, b2);
+numboxes++;
+}
+return numboxes;
+}
+LOCAL(void)
+compute_color (j_decompress_ptr cinfo, boxptr boxp, int icolor)
+/* Compute representative color for a box, put it in colormap[icolor] */
+{
+/* Current algorithm: mean weighted by pixels (not colors) */
+/* Note it is important to get the rounding correct! */
+my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
+hist3d histogram = cquantize->histogram;
+histptr histp;
+int c0,c1,c2;
+int c0min,c0max,c1min,c1max,c2min,c2max;
+long count;
+long total = 0;
+long c0total = 0;
+long c1total = 0;
+long c2total = 0;
+c0min = boxp->c0min;  c0max = boxp->c0max;
+c1min = boxp->c1min;  c1max = boxp->c1max;
+c2min = boxp->c2min;  c2max = boxp->c2max;
+for (c0 = c0min; c0 <= c0max; c0++)
+for (c1 = c1min; c1 <= c1max; c1++) {
+histp = & histogram[c0][c1][c2min];
+for (c2 = c2min; c2 <= c2max; c2++) {
+	if ((count = *histp++) != 0) {
+	  total += count;
+	  c0total += ((c0 << C0_SHIFT) + ((1<<C0_SHIFT)>>1)) * count;
+	  c1total += ((c1 << C1_SHIFT) + ((1<<C1_SHIFT)>>1)) * count;
+	  c2total += ((c2 << C2_SHIFT) + ((1<<C2_SHIFT)>>1)) * count;
+	}
+}
+}
+cinfo->colormap[0][icolor] = (JSAMPLE) ((c0total + (total>>1)) / total);
+cinfo->colormap[1][icolor] = (JSAMPLE) ((c1total + (total>>1)) / total);
+cinfo->colormap[2][icolor] = (JSAMPLE) ((c2total + (total>>1)) / total);
+}
+LOCAL(void)
+select_colors (j_decompress_ptr cinfo, int desired_colors)
+/* Master routine for color selection */
+{
+boxptr boxlist;
+int numboxes;
+int i;
+/* Allocate workspace for box list */
+boxlist = (boxptr) (*cinfo->mem->alloc_small)
+((j_common_ptr) cinfo, JPOOL_IMAGE, desired_colors * SIZEOF(box));
+/* Initialize one box containing whole space */
+numboxes = 1;
+boxlist[0].c0min = 0;
+boxlist[0].c0max = MAXJSAMPLE >> C0_SHIFT;
+boxlist[0].c1min = 0;
+boxlist[0].c1max = MAXJSAMPLE >> C1_SHIFT;
+boxlist[0].c2min = 0;
+boxlist[0].c2max = MAXJSAMPLE >> C2_SHIFT;
+/* Shrink it to actually-used volume and set its statistics */
+update_box(cinfo, & boxlist[0]);
+/* Perform median-cut to produce final box list */
+numboxes = median_cut(cinfo, boxlist, numboxes, desired_colors);
+/* Compute the representative color for each box, fill colormap */
+for (i = 0; i < numboxes; i++)
+compute_color(cinfo, & boxlist[i], i);
+cinfo->actual_number_of_colors = numboxes;
+TRACEMS1(cinfo, 1, JTRC_QUANT_SELECTED, numboxes);
+}
+/*
+* These routines are concerned with the time-critical task of mapping input
+* colors to the nearest color in the selected colormap.
+*
+* We re-use the histogram space as an "inverse color map", essentially a
+* cache for the results of nearest-color searches.  All colors within a
+* histogram cell will be mapped to the same colormap entry, namely the one
+* closest to the cell's center.  This may not be quite the closest entry to
+* the actual input color, but it's almost as good.  A zero in the cache
+* indicates we haven't found the nearest color for that cell yet; the array
+* is cleared to zeroes before starting the mapping pass.  When we find the
+* nearest color for a cell, its colormap index plus one is recorded in the
+* cache for future use.  The pass2 scanning routines call fill_inverse_cmap
+* when they need to use an unfilled entry in the cache.
+*
+* Our method of efficiently finding nearest colors is based on the "locally
+* sorted search" idea described by Heckbert and on the incremental distance
+* calculation described by Spencer W. Thomas in chapter III.1 of Graphics
+* Gems II (James Arvo, ed.  Academic Press, 1991).  Thomas points out that
+* the distances from a given colormap entry to each cell of the histogram can
+* be computed quickly using an incremental method: the differences between
+* distances to adjacent cells themselves differ by a constant.  This allows a
+* fairly fast implementation of the "brute force" approach of computing the
+* distance from every colormap entry to every histogram cell.  Unfortunately,
+* it needs a work array to hold the best-distance-so-far for each histogram
+* cell (because the inner loop has to be over cells, not colormap entries).
+* The work array elements have to be INT32s, so the work array would need
+* 256Kb at our recommended precision.  This is not feasible in DOS machines.
+*
+* To get around these problems, we apply Thomas' method to compute the
+* nearest colors for only the cells within a small subbox of the histogram.
+* The work array need be only as big as the subbox, so the memory usage
+* problem is solved.  Furthermore, we need not fill subboxes that are never
+* referenced in pass2; many images use only part of the color gamut, so a
+* fair amount of work is saved.  An additional advantage of this
+* approach is that we can apply Heckbert's locality criterion to quickly
+* eliminate colormap entries that are far away from the subbox; typically
+* three-fourths of the colormap entries are rejected by Heckbert's criterion,
+* and we need not compute their distances to individual cells in the subbox.
+* The speed of this approach is heavily influenced by the subbox size: too
+* small means too much overhead, too big loses because Heckbert's criterion
+* can't eliminate as many colormap entries.  Empirically the best subbox
+* size seems to be about 1/512th of the histogram (1/8th in each direction).
+*
+* Thomas' article also describes a refined method which is asymptotically
+* faster than the brute-force method, but it is also far more complex and
+* cannot efficiently be applied to small subboxes.  It is therefore not
+* useful for programs intended to be portable to DOS machines.  On machines
+* with plenty of memory, filling the whole histogram in one shot with Thomas'
+* refined method might be faster than the present code --- but then again,
+* it might not be any faster, and it's certainly more complicated.
+*/
+/* log2(histogram cells in update box) for each axis; this can be adjusted */
+#define BOX_C0_LOG  (HIST_C0_BITS-3)
+#define BOX_C1_LOG  (HIST_C1_BITS-3)
+#define BOX_C2_LOG  (HIST_C2_BITS-3)
+#define BOX_C0_ELEMS  (1<<BOX_C0_LOG) /* # of hist cells in update box */
+#define BOX_C1_ELEMS  (1<<BOX_C1_LOG)
+#define BOX_C2_ELEMS  (1<<BOX_C2_LOG)
+#define BOX_C0_SHIFT  (C0_SHIFT + BOX_C0_LOG)
+#define BOX_C1_SHIFT  (C1_SHIFT + BOX_C1_LOG)
+#define BOX_C2_SHIFT  (C2_SHIFT + BOX_C2_LOG)
+/*
+* The next three routines implement inverse colormap filling.  They could
+* all be folded into one big routine, but splitting them up this way saves
+* some stack space (the mindist[] and bestdist[] arrays need not coexist)
+* and may allow some compilers to produce better code by registerizing more
+* inner-loop variables.
+*/
+LOCAL(int)
+find_nearby_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
+		    JSAMPLE colorlist[])
+/* Locate the colormap entries close enough to an update box to be candidates
+* for the nearest entry to some cell(s) in the update box.  The update box
+* is specified by the center coordinates of its first cell.  The number of
+* candidate colormap entries is returned, and their colormap indexes are
+* placed in colorlist[].
+* This routine uses Heckbert's "locally sorted search" criterion to select
+* the colors that need further consideration.
+*/
+{
+int numcolors = cinfo->actual_number_of_colors;
+int maxc0, maxc1, maxc2;
+int centerc0, centerc1, centerc2;
+int i, x, ncolors;
+INT32 minmaxdist, min_dist, max_dist, tdist;
+INT32 mindist[MAXNUMCOLORS];	/* min distance to colormap entry i */
+/* Compute true coordinates of update box's upper corner and center.
+* Actually we compute the coordinates of the center of the upper-corner
+* histogram cell, which are the upper bounds of the volume we care about.
+* Note that since ">>" rounds down, the "center" values may be closer to
+* min than to max; hence comparisons to them must be "<=", not "<".
+*/
+maxc0 = minc0 + ((1 << BOX_C0_SHIFT) - (1 << C0_SHIFT));
+centerc0 = (minc0 + maxc0) >> 1;
+maxc1 = minc1 + ((1 << BOX_C1_SHIFT) - (1 << C1_SHIFT));
+centerc1 = (minc1 + maxc1) >> 1;
+maxc2 = minc2 + ((1 << BOX_C2_SHIFT) - (1 << C2_SHIFT));
+centerc2 = (minc2 + maxc2) >> 1;
+/* For each color in colormap, find:
+*  1. its minimum squared-distance to any point in the update box
+*     (zero if color is within update box);
+*  2. its maximum squared-distance to any point in the update box.
+* Both of these can be found by considering only the corners of the box.
+* We save the minimum distance for each color in mindist[];
+* only the smallest maximum distance is of interest.
+*/
+minmaxdist = 0x7FFFFFFFL;
+for (i = 0; i < numcolors; i++) {
+/* We compute the squared-c0-distance term, then add in the other two. */
+x = GETJSAMPLE(cinfo->colormap[0][i]);
+if (x < minc0) {
+tdist = (x - minc0) * C0_SCALE;
+min_dist = tdist*tdist;
+tdist = (x - maxc0) * C0_SCALE;
+max_dist = tdist*tdist;
+} else if (x > maxc0) {
+tdist = (x - maxc0) * C0_SCALE;
+min_dist = tdist*tdist;
+tdist = (x - minc0) * C0_SCALE;
+max_dist = tdist*tdist;
+} else {
+/* within cell range so no contribution to min_dist */
+min_dist = 0;
+if (x <= centerc0) {
+	tdist = (x - maxc0) * C0_SCALE;
+	max_dist = tdist*tdist;
+} else {
+	tdist = (x - minc0) * C0_SCALE;
+	max_dist = tdist*tdist;
+}
+}
+x = GETJSAMPLE(cinfo->colormap[1][i]);
+if (x < minc1) {
+tdist = (x - minc1) * C1_SCALE;
+min_dist += tdist*tdist;
+tdist = (x - maxc1) * C1_SCALE;
+max_dist += tdist*tdist;
+} else if (x > maxc1) {
+tdist = (x - maxc1) * C1_SCALE;
+min_dist += tdist*tdist;
+tdist = (x - minc1) * C1_SCALE;
+max_dist += tdist*tdist;
+} else {
+/* within cell range so no contribution to min_dist */
+if (x <= centerc1) {
+	tdist = (x - maxc1) * C1_SCALE;
+	max_dist += tdist*tdist;
+} else {
+	tdist = (x - minc1) * C1_SCALE;
+	max_dist += tdist*tdist;
+}
+}
+x = GETJSAMPLE(cinfo->colormap[2][i]);
+if (x < minc2) {
+tdist = (x - minc2) * C2_SCALE;
+min_dist += tdist*tdist;
+tdist = (x - maxc2) * C2_SCALE;
+max_dist += tdist*tdist;
+} else if (x > maxc2) {
+tdist = (x - maxc2) * C2_SCALE;
+min_dist += tdist*tdist;
+tdist = (x - minc2) * C2_SCALE;
+max_dist += tdist*tdist;
+} else {
+/* within cell range so no contribution to min_dist */
+if (x <= centerc2) {
+	tdist = (x - maxc2) * C2_SCALE;
+	max_dist += tdist*tdist;
+} else {
+	tdist = (x - minc2) * C2_SCALE;
+	max_dist += tdist*tdist;
+}
+}
+mindist[i] = min_dist;	/* save away the results */
+if (max_dist < minmaxdist)
+minmaxdist = max_dist;
+}
+/* Now we know that no cell in the update box is more than minmaxdist
+* away from some colormap entry.  Therefore, only colors that are
+* within minmaxdist of some part of the box need be considered.
+*/
+ncolors = 0;
+for (i = 0; i < numcolors; i++) {
+if (mindist[i] <= minmaxdist)
+colorlist[ncolors++] = (JSAMPLE) i;
+}
+return ncolors;
+}
+LOCAL(void)
+find_best_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
+		  int numcolors, JSAMPLE colorlist[], JSAMPLE bestcolor[])
+/* Find the closest colormap entry for each cell in the update box,
+* given the list of candidate colors prepared by find_nearby_colors.
+* Return the indexes of the closest entries in the bestcolor[] array.
+* This routine uses Thomas' incremental distance calculation method to
+* find the distance from a colormap entry to successive cells in the box.
+*/
+{
+int ic0, ic1, ic2;
+int i, icolor;
+register INT32 * bptr;	/* pointer into bestdist[] array */
+JSAMPLE * cptr;		/* pointer into bestcolor[] array */
+INT32 dist0, dist1;		/* initial distance values */
+register INT32 dist2;		/* current distance in inner loop */
+INT32 xx0, xx1;		/* distance increments */
+register INT32 xx2;
+INT32 inc0, inc1, inc2;	/* initial values for increments */
+/* This array holds the distance to the nearest-so-far color for each cell */
+INT32 bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
+/* Initialize best-distance for each cell of the update box */
+bptr = bestdist;
+for (i = BOX_C0_ELEMS*BOX_C1_ELEMS*BOX_C2_ELEMS-1; i >= 0; i--)
+*bptr++ = 0x7FFFFFFFL;
+/* For each color selected by find_nearby_colors,
+* compute its distance to the center of each cell in the box.
+* If that's less than best-so-far, update best distance and color number.
+*/
+/* Nominal steps between cell centers ("x" in Thomas article) */
+#define STEP_C0  ((1 << C0_SHIFT) * C0_SCALE)
+#define STEP_C1  ((1 << C1_SHIFT) * C1_SCALE)
+#define STEP_C2  ((1 << C2_SHIFT) * C2_SCALE)
+for (i = 0; i < numcolors; i++) {
+icolor = GETJSAMPLE(colorlist[i]);
+/* Compute (square of) distance from minc0/c1/c2 to this color */
+inc0 = (minc0 - GETJSAMPLE(cinfo->colormap[0][icolor])) * C0_SCALE;
+dist0 = inc0*inc0;
+inc1 = (minc1 - GETJSAMPLE(cinfo->colormap[1][icolor])) * C1_SCALE;
+dist0 += inc1*inc1;
+inc2 = (minc2 - GETJSAMPLE(cinfo->colormap[2][icolor])) * C2_SCALE;
+dist0 += inc2*inc2;
+/* Form the initial difference increments */
+inc0 = inc0 * (2 * STEP_C0) + STEP_C0 * STEP_C0;
+inc1 = inc1 * (2 * STEP_C1) + STEP_C1 * STEP_C1;
+inc2 = inc2 * (2 * STEP_C2) + STEP_C2 * STEP_C2;
+/* Now loop over all cells in box, updating distance per Thomas method */
+bptr = bestdist;
+cptr = bestcolor;
+xx0 = inc0;
+for (ic0 = BOX_C0_ELEMS-1; ic0 >= 0; ic0--) {
+dist1 = dist0;
+xx1 = inc1;
+for (ic1 = BOX_C1_ELEMS-1; ic1 >= 0; ic1--) {
+	dist2 = dist1;
+	xx2 = inc2;
+	for (ic2 = BOX_C2_ELEMS-1; ic2 >= 0; ic2--) {
+	  if (dist2 < *bptr) {
+	    *bptr = dist2;
+	    *cptr = (JSAMPLE) icolor;
+	  }
+	  dist2 += xx2;
+	  xx2 += 2 * STEP_C2 * STEP_C2;
+	  bptr++;
+	  cptr++;
+	}
+	dist1 += xx1;
+	xx1 += 2 * STEP_C1 * STEP_C1;
+}
+dist0 += xx0;
+xx0 += 2 * STEP_C0 * STEP_C0;
+}
+}
+}
+LOCAL(void)
+fill_inverse_cmap (j_decompress_ptr cinfo, int c0, int c1, int c2)
+/* Fill the inverse-colormap entries in the update box that contains */
+/* histogram cell c0/c1/c2.  (Only that one cell MUST be filled, but */
+/* we can fill as many others as we wish.) */
+{
+my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
+hist3d histogram = cquantize->histogram;
+int minc0, minc1, minc2;	/* lower left corner of update box */
+int ic0, ic1, ic2;
+register JSAMPLE * cptr;	/* pointer into bestcolor[] array */
+register histptr cachep;	/* pointer into main cache array */
+/* This array lists the candidate colormap indexes. */
+JSAMPLE colorlist[MAXNUMCOLORS];
+int numcolors;		/* number of candidate colors */
+/* This array holds the actually closest colormap index for each cell. */
+JSAMPLE bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
+/* Convert cell coordinates to update box ID */
+c0 >>= BOX_C0_LOG;
+c1 >>= BOX_C1_LOG;
+c2 >>= BOX_C2_LOG;
+/* Compute true coordinates of update box's origin corner.
+* Actually we compute the coordinates of the center of the corner
+* histogram cell, which are the lower bounds of the volume we care about.
+*/
+minc0 = (c0 << BOX_C0_SHIFT) + ((1 << C0_SHIFT) >> 1);
+minc1 = (c1 << BOX_C1_SHIFT) + ((1 << C1_SHIFT) >> 1);
+minc2 = (c2 << BOX_C2_SHIFT) + ((1 << C2_SHIFT) >> 1);
+/* Determine which colormap entries are close enough to be candidates
+* for the nearest entry to some cell in the update box.
+*/
+numcolors = find_nearby_colors(cinfo, minc0, minc1, minc2, colorlist);
+/* Determine the actually nearest colors. */
+find_best_colors(cinfo, minc0, minc1, minc2, numcolors, colorlist,
+		   bestcolor);
+/* Save the best color numbers (plus 1) in the main cache array */
+c0 <<= BOX_C0_LOG;		/* convert ID back to base cell indexes */
+c1 <<= BOX_C1_LOG;
+c2 <<= BOX_C2_LOG;
+cptr = bestcolor;
+for (ic0 = 0; ic0 < BOX_C0_ELEMS; ic0++) {
+for (ic1 = 0; ic1 < BOX_C1_ELEMS; ic1++) {
+cachep = & histogram[c0+ic0][c1+ic1][c2];
+for (ic2 = 0; ic2 < BOX_C2_ELEMS; ic2++) {
+	*cachep++ = (histcell) (GETJSAMPLE(*cptr++) + 1);
+}
+}
+}
+}
+/*
+* Map some rows of pixels to the output colormapped representation.
+*/
+METHODDEF(void)
+pass2_no_dither (j_decompress_ptr cinfo,
+		 JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)
+/* This version performs no dithering */
+{
+my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
+hist3d histogram = cquantize->histogram;
+register JSAMPROW inptr, outptr;
+register histptr cachep;
+register int c0, c1, c2;
+int row;
+JDIMENSION col;
+JDIMENSION width = cinfo->output_width;
+for (row = 0; row < num_rows; row++) {
+inptr = input_buf[row];
+outptr = output_buf[row];
+for (col = width; col > 0; col--) {
+/* get pixel value and index into the cache */
+c0 = GETJSAMPLE(*inptr++) >> C0_SHIFT;
+c1 = GETJSAMPLE(*inptr++) >> C1_SHIFT;
+c2 = GETJSAMPLE(*inptr++) >> C2_SHIFT;
+cachep = & histogram[c0][c1][c2];
+/* If we have not seen this color before, find nearest colormap entry */
+/* and update the cache */
+if (*cachep == 0)
+	fill_inverse_cmap(cinfo, c0,c1,c2);
+/* Now emit the colormap index for this cell */
+*outptr++ = (JSAMPLE) (*cachep - 1);
+}
+}
+}
+METHODDEF(void)
+pass2_fs_dither (j_decompress_ptr cinfo,
+		 JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)
+/* This version performs Floyd-Steinberg dithering */
+{
+my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
+hist3d histogram = cquantize->histogram;
+register LOCFSERROR cur0, cur1, cur2;	/* current error or pixel value */
+LOCFSERROR belowerr0, belowerr1, belowerr2; /* error for pixel below cur */
+LOCFSERROR bpreverr0, bpreverr1, bpreverr2; /* error for below/prev col */
+register FSERRPTR errorptr;	/* => fserrors[] at column before current */
+JSAMPROW inptr;		/* => current input pixel */
+JSAMPROW outptr;		/* => current output pixel */
+histptr cachep;
+int dir;			/* +1 or -1 depending on direction */
+int dir3;			/* 3*dir, for advancing inptr & errorptr */
+int row;
+JDIMENSION col;
+JDIMENSION width = cinfo->output_width;
+JSAMPLE *range_limit = cinfo->sample_range_limit;
+int *error_limit = cquantize->error_limiter;
+JSAMPROW colormap0 = cinfo->colormap[0];
+JSAMPROW colormap1 = cinfo->colormap[1];
+JSAMPROW colormap2 = cinfo->colormap[2];
+SHIFT_TEMPS
+for (row = 0; row < num_rows; row++) {
+inptr = input_buf[row];
+outptr = output_buf[row];
+if (cquantize->on_odd_row) {
+/* work right to left in this row */
+inptr += (width-1) * 3;	/* so point to rightmost pixel */
+outptr += width-1;
+dir = -1;
+dir3 = -3;
+errorptr = cquantize->fserrors + (width+1)*3; /* => entry after last column */
+cquantize->on_odd_row = FALSE; /* flip for next time */
+} else {
+/* work left to right in this row */
+dir = 1;
+dir3 = 3;
+errorptr = cquantize->fserrors; /* => entry before first real column */
+cquantize->on_odd_row = TRUE; /* flip for next time */
+}
+/* Preset error values: no error propagated to first pixel from left */
+cur0 = cur1 = cur2 = 0;
+/* and no error propagated to row below yet */
+belowerr0 = belowerr1 = belowerr2 = 0;
+bpreverr0 = bpreverr1 = bpreverr2 = 0;
+for (col = width; col > 0; col--) {
+/* curN holds the error propagated from the previous pixel on the
+* current line.  Add the error propagated from the previous line
+* to form the complete error correction term for this pixel, and
+* round the error term (which is expressed * 16) to an integer.
+* RIGHT_SHIFT rounds towards minus infinity, so adding 8 is correct
+* for either sign of the error value.
+* Note: errorptr points to *previous* column's array entry.
+*/
+cur0 = RIGHT_SHIFT(cur0 + errorptr[dir3+0] + 8, 4);
+cur1 = RIGHT_SHIFT(cur1 + errorptr[dir3+1] + 8, 4);
+cur2 = RIGHT_SHIFT(cur2 + errorptr[dir3+2] + 8, 4);
+/* Limit the error using transfer function set by init_error_limit.
+* See comments with init_error_limit for rationale.
+*/
+cur0 = error_limit[cur0];
+cur1 = error_limit[cur1];
+cur2 = error_limit[cur2];
+/* Form pixel value + error, and range-limit to 0..MAXJSAMPLE.
+* The maximum error is +- MAXJSAMPLE (or less with error limiting);
+* this sets the required size of the range_limit array.
+*/
+cur0 += GETJSAMPLE(inptr[0]);
+cur1 += GETJSAMPLE(inptr[1]);
+cur2 += GETJSAMPLE(inptr[2]);
+cur0 = GETJSAMPLE(range_limit[cur0]);
+cur1 = GETJSAMPLE(range_limit[cur1]);
+cur2 = GETJSAMPLE(range_limit[cur2]);
+/* Index into the cache with adjusted pixel value */
+cachep = & histogram[cur0>>C0_SHIFT][cur1>>C1_SHIFT][cur2>>C2_SHIFT];
+/* If we have not seen this color before, find nearest colormap */
+/* entry and update the cache */
+if (*cachep == 0)
+	fill_inverse_cmap(cinfo, cur0>>C0_SHIFT,cur1>>C1_SHIFT,cur2>>C2_SHIFT);
+/* Now emit the colormap index for this cell */
+{ register int pixcode = *cachep - 1;
+	*outptr = (JSAMPLE) pixcode;
+	/* Compute representation error for this pixel */
+	cur0 -= GETJSAMPLE(colormap0[pixcode]);
+	cur1 -= GETJSAMPLE(colormap1[pixcode]);
+	cur2 -= GETJSAMPLE(colormap2[pixcode]);
+}
+/* Compute error fractions to be propagated to adjacent pixels.
+* Add these into the running sums, and simultaneously shift the
+* next-line error sums left by 1 column.
+*/
+{ register LOCFSERROR bnexterr, delta;
+	bnexterr = cur0;	/* Process component 0 */
+	delta = cur0 * 2;
+	cur0 += delta;		/* form error * 3 */
+	errorptr[0] = (FSERROR) (bpreverr0 + cur0);
+	cur0 += delta;		/* form error * 5 */
+	bpreverr0 = belowerr0 + cur0;
+	belowerr0 = bnexterr;
+	cur0 += delta;		/* form error * 7 */
+	bnexterr = cur1;	/* Process component 1 */
+	delta = cur1 * 2;
+	cur1 += delta;		/* form error * 3 */
+	errorptr[1] = (FSERROR) (bpreverr1 + cur1);
+	cur1 += delta;		/* form error * 5 */
+	bpreverr1 = belowerr1 + cur1;
+	belowerr1 = bnexterr;
+	cur1 += delta;		/* form error * 7 */
+	bnexterr = cur2;	/* Process component 2 */
+	delta = cur2 * 2;
+	cur2 += delta;		/* form error * 3 */
+	errorptr[2] = (FSERROR) (bpreverr2 + cur2);
+	cur2 += delta;		/* form error * 5 */
+	bpreverr2 = belowerr2 + cur2;
+	belowerr2 = bnexterr;
+	cur2 += delta;		/* form error * 7 */
+}
+/* At this point curN contains the 7/16 error value to be propagated
+* to the next pixel on the current line, and all the errors for the
+* next line have been shifted over.  We are therefore ready to move on.
+*/
+inptr += dir3;		/* Advance pixel pointers to next column */
+outptr += dir;
+errorptr += dir3;		/* advance errorptr to current column */
+}
+/* Post-loop cleanup: we must unload the final error values into the
+* final fserrors[] entry.  Note we need not unload belowerrN because
+* it is for the dummy column before or after the actual array.
+*/
+errorptr[0] = (FSERROR) bpreverr0; /* unload prev errs into array */
+errorptr[1] = (FSERROR) bpreverr1;
+errorptr[2] = (FSERROR) bpreverr2;
+}
+}
+/*
+* Initialize the error-limiting transfer function (lookup table).
+* The raw F-S error computation can potentially compute error values of up to
+* +- MAXJSAMPLE.  But we want the maximum correction applied to a pixel to be
+* much less, otherwise obviously wrong pixels will be created.  (Typical
+* effects include weird fringes at color-area boundaries, isolated bright
+* pixels in a dark area, etc.)  The standard advice for avoiding this problem
+* is to ensure that the "corners" of the color cube are allocated as output
+* colors; then repeated errors in the same direction cannot cause cascading
+* error buildup.  However, that only prevents the error from getting
+* completely out of hand; Aaron Giles reports that error limiting improves
+* the results even with corner colors allocated.
+* A simple clamping of the error values to about +- MAXJSAMPLE/8 works pretty
+* well, but the smoother transfer function used below is even better.  Thanks
+* to Aaron Giles for this idea.
+*/
+LOCAL(void)
+init_error_limit (j_decompress_ptr cinfo)
+/* Allocate and fill in the error_limiter table */
+{
+my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
+int * table;
+int in, out;
+table = (int *) (*cinfo->mem->alloc_small)
+((j_common_ptr) cinfo, JPOOL_IMAGE, (MAXJSAMPLE*2+1) * SIZEOF(int));
+table += MAXJSAMPLE;		/* so can index -MAXJSAMPLE .. +MAXJSAMPLE */
+cquantize->error_limiter = table;
+#define STEPSIZE ((MAXJSAMPLE+1)/16)
+/* Map errors 1:1 up to +- MAXJSAMPLE/16 */
+out = 0;
+for (in = 0; in < STEPSIZE; in++, out++) {
+table[in] = out; table[-in] = -out;
+}
+/* Map errors 1:2 up to +- 3*MAXJSAMPLE/16 */
+for (; in < STEPSIZE*3; in++, out += (in&1) ? 0 : 1) {
+table[in] = out; table[-in] = -out;
+}
+/* Clamp the rest to final out value (which is (MAXJSAMPLE+1)/8) */
+for (; in <= MAXJSAMPLE; in++) {
+table[in] = out; table[-in] = -out;
+}
+#undef STEPSIZE
+}
+/*
+* Finish up at the end of each pass.
+*/
+METHODDEF(void)
+finish_pass1 (j_decompress_ptr cinfo)
+{
+my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
+/* Select the representative colors and fill in cinfo->colormap */
+cinfo->colormap = cquantize->sv_colormap;
+select_colors(cinfo, cquantize->desired);
+/* Force next pass to zero the color index table */
+cquantize->needs_zeroed = TRUE;
+}
+METHODDEF(void)
+finish_pass2 (j_decompress_ptr cinfo)
+{
+/* no work */
+}
+/*
+* Initialize for each processing pass.
+*/
+METHODDEF(void)
+start_pass_2_quant (j_decompress_ptr cinfo, boolean is_pre_scan)
+{
+my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
+hist3d histogram = cquantize->histogram;
+int i;
+/* Only F-S dithering or no dithering is supported. */
+/* If user asks for ordered dither, give him F-S. */
+if (cinfo->dither_mode != JDITHER_NONE)
+cinfo->dither_mode = JDITHER_FS;
+if (is_pre_scan) {
+/* Set up method pointers */
+cquantize->pub.color_quantize = prescan_quantize;
+cquantize->pub.finish_pass = finish_pass1;
+cquantize->needs_zeroed = TRUE; /* Always zero histogram */
+} else {
+/* Set up method pointers */
+if (cinfo->dither_mode == JDITHER_FS)
+cquantize->pub.color_quantize = pass2_fs_dither;
+else
+cquantize->pub.color_quantize = pass2_no_dither;
+cquantize->pub.finish_pass = finish_pass2;
+/* Make sure color count is acceptable */
+i = cinfo->actual_number_of_colors;
+if (i < 1)
+ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 1);
+if (i > MAXNUMCOLORS)
+ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
+if (cinfo->dither_mode == JDITHER_FS) {
+size_t arraysize = (size_t) ((cinfo->output_width + 2) *
+				   (3 * SIZEOF(FSERROR)));
+/* Allocate Floyd-Steinberg workspace if we didn't already. */
+if (cquantize->fserrors == NULL)
+	cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)
+	  ((j_common_ptr) cinfo, JPOOL_IMAGE, arraysize);
+/* Initialize the propagated errors to zero. */
+jzero_far((void FAR *) cquantize->fserrors, arraysize);
+/* Make the error-limit table if we didn't already. */
+if (cquantize->error_limiter == NULL)
+	init_error_limit(cinfo);
+cquantize->on_odd_row = FALSE;
+}
+}
+/* Zero the histogram or inverse color map, if necessary */
+if (cquantize->needs_zeroed) {
+for (i = 0; i < HIST_C0_ELEMS; i++) {
+jzero_far((void FAR *) histogram[i],
+		HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));
+}
+cquantize->needs_zeroed = FALSE;
+}
+}
+/*
+* Switch to a new external colormap between output passes.
+*/
+METHODDEF(void)
+new_color_map_2_quant (j_decompress_ptr cinfo)
+{
+my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
+/* Reset the inverse color map */
+cquantize->needs_zeroed = TRUE;
+}
+/*
+* Module initialization routine for 2-pass color quantization.
+*/
+GLOBAL(void)
+jinit_2pass_quantizer (j_decompress_ptr cinfo)
+{
+my_cquantize_ptr cquantize;
+int i;
+cquantize = (my_cquantize_ptr)
+(*cinfo->mem->alloc_small) ((j_common_ptr) cinfo, JPOOL_IMAGE,
+				SIZEOF(my_cquantizer));
+cinfo->cquantize = (struct jpeg_color_quantizer *) cquantize;
+cquantize->pub.start_pass = start_pass_2_quant;
+cquantize->pub.new_color_map = new_color_map_2_quant;
+cquantize->fserrors = NULL;	/* flag optional arrays not allocated */
+cquantize->error_limiter = NULL;
+/* Make sure jdmaster didn't give me a case I can't handle */
+if (cinfo->out_color_components != 3)
+ERREXIT(cinfo, JERR_NOTIMPL);
+/* Allocate the histogram/inverse colormap storage */
+cquantize->histogram = (hist3d) (*cinfo->mem->alloc_small)
+((j_common_ptr) cinfo, JPOOL_IMAGE, HIST_C0_ELEMS * SIZEOF(hist2d));
+for (i = 0; i < HIST_C0_ELEMS; i++) {
+cquantize->histogram[i] = (hist2d) (*cinfo->mem->alloc_large)
+((j_common_ptr) cinfo, JPOOL_IMAGE,
+HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));
+}
+cquantize->needs_zeroed = TRUE; /* histogram is garbage now */
+/* Allocate storage for the completed colormap, if required.
+* We do this now since it is FAR storage and may affect
+* the memory manager's space calculations.
+*/
+if (cinfo->enable_2pass_quant) {
+/* Make sure color count is acceptable */
+int desired = cinfo->desired_number_of_colors;
+/* Lower bound on # of colors ... somewhat arbitrary as long as > 0 */
+if (desired < 8)
+ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 8);
+/* Make sure colormap indexes can be represented by JSAMPLEs */
+if (desired > MAXNUMCOLORS)
+ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
+cquantize->sv_colormap = (*cinfo->mem->alloc_sarray)
+((j_common_ptr) cinfo,JPOOL_IMAGE, (JDIMENSION) desired, (JDIMENSION) 3);
+cquantize->desired = desired;
+} else
+cquantize->sv_colormap = NULL;
+/* Only F-S dithering or no dithering is supported. */
+/* If user asks for ordered dither, give him F-S. */
+if (cinfo->dither_mode != JDITHER_NONE)
+cinfo->dither_mode = JDITHER_FS;
+/* Allocate Floyd-Steinberg workspace if necessary.
+* This isn't really needed until pass 2, but again it is FAR storage.
+* Although we will cope with a later change in dither_mode,
+* we do not promise to honor max_memory_to_use if dither_mode changes.
+*/
+if (cinfo->dither_mode == JDITHER_FS) {
+cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)
+((j_common_ptr) cinfo, JPOOL_IMAGE,
+(size_t) ((cinfo->output_width + 2) * (3 * SIZEOF(FSERROR))));
+/* Might as well create the error-limiting table too. */
+init_error_limit(cinfo);
+}
+}
+#endif /* QUANT_2PASS_SUPPORTED */

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media/libjpeg/jquant2.c