jdk

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/*
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 * reserved comment block
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 * DO NOT REMOVE OR ALTER!
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 */
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/*
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 * jquant2.c
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 *
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 * Copyright (C) 1991-1996, Thomas G. Lane.
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 * This file is part of the Independent JPEG Group's software.
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 * For conditions of distribution and use, see the accompanying README file.
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 *
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 * This file contains 2-pass color quantization (color mapping) routines.
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 * These routines provide selection of a custom color map for an image,
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 * followed by mapping of the image to that color map, with optional
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 * Floyd-Steinberg dithering.
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 * It is also possible to use just the second pass to map to an arbitrary
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 * externally-given color map.
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 *
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 * Note: ordered dithering is not supported, since there isn't any fast
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 * way to compute intercolor distances; it's unclear that ordered dither's
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 * fundamental assumptions even hold with an irregularly spaced color map.
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 */
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#define JPEG_INTERNALS
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#include "jinclude.h"
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#include "jpeglib.h"
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#ifdef QUANT_2PASS_SUPPORTED
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30

31
/*
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 * This module implements the well-known Heckbert paradigm for color
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 * quantization.  Most of the ideas used here can be traced back to
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 * Heckbert's seminal paper
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 *   Heckbert, Paul.  "Color Image Quantization for Frame Buffer Display",
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 *   Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304.
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 *
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 * In the first pass over the image, we accumulate a histogram showing the
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 * usage count of each possible color.  To keep the histogram to a reasonable
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 * size, we reduce the precision of the input; typical practice is to retain
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 * 5 or 6 bits per color, so that 8 or 4 different input values are counted
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 * in the same histogram cell.
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 *
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 * Next, the color-selection step begins with a box representing the whole
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 * color space, and repeatedly splits the "largest" remaining box until we
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 * have as many boxes as desired colors.  Then the mean color in each
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 * remaining box becomes one of the possible output colors.
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 *
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 * The second pass over the image maps each input pixel to the closest output
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 * color (optionally after applying a Floyd-Steinberg dithering correction).
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 * This mapping is logically trivial, but making it go fast enough requires
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 * considerable care.
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 *
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 * Heckbert-style quantizers vary a good deal in their policies for choosing
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 * the "largest" box and deciding where to cut it.  The particular policies
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 * used here have proved out well in experimental comparisons, but better ones
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 * may yet be found.
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 *
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 * In earlier versions of the IJG code, this module quantized in YCbCr color
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 * space, processing the raw upsampled data without a color conversion step.
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 * This allowed the color conversion math to be done only once per colormap
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 * entry, not once per pixel.  However, that optimization precluded other
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 * useful optimizations (such as merging color conversion with upsampling)
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 * and it also interfered with desired capabilities such as quantizing to an
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 * externally-supplied colormap.  We have therefore abandoned that approach.
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 * The present code works in the post-conversion color space, typically RGB.
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 *
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 * To improve the visual quality of the results, we actually work in scaled
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 * RGB space, giving G distances more weight than R, and R in turn more than
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 * B.  To do everything in integer math, we must use integer scale factors.
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 * The 2/3/1 scale factors used here correspond loosely to the relative
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 * weights of the colors in the NTSC grayscale equation.
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 * If you want to use this code to quantize a non-RGB color space, you'll
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 * probably need to change these scale factors.
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 */
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#define R_SCALE 2               /* scale R distances by this much */
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#define G_SCALE 3               /* scale G distances by this much */
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#define B_SCALE 1               /* and B by this much */
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/* Relabel R/G/B as components 0/1/2, respecting the RGB ordering defined
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 * in jmorecfg.h.  As the code stands, it will do the right thing for R,G,B
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 * and B,G,R orders.  If you define some other weird order in jmorecfg.h,
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 * you'll get compile errors until you extend this logic.  In that case
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 * you'll probably want to tweak the histogram sizes too.
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 */
87

88
#if RGB_RED == 0
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#define C0_SCALE R_SCALE
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#endif
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#if RGB_BLUE == 0
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#define C0_SCALE B_SCALE
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#endif
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#if RGB_GREEN == 1
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#define C1_SCALE G_SCALE
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#endif
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#if RGB_RED == 2
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#define C2_SCALE R_SCALE
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#endif
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#if RGB_BLUE == 2
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#define C2_SCALE B_SCALE
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#endif
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104

105
/*
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 * First we have the histogram data structure and routines for creating it.
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 *
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 * The number of bits of precision can be adjusted by changing these symbols.
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 * We recommend keeping 6 bits for G and 5 each for R and B.
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 * If you have plenty of memory and cycles, 6 bits all around gives marginally
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 * better results; if you are short of memory, 5 bits all around will save
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 * some space but degrade the results.
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 * To maintain a fully accurate histogram, we'd need to allocate a "long"
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 * (preferably unsigned long) for each cell.  In practice this is overkill;
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 * we can get by with 16 bits per cell.  Few of the cell counts will overflow,
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 * and clamping those that do overflow to the maximum value will give close-
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 * enough results.  This reduces the recommended histogram size from 256Kb
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 * to 128Kb, which is a useful savings on PC-class machines.
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 * (In the second pass the histogram space is re-used for pixel mapping data;
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 * in that capacity, each cell must be able to store zero to the number of
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 * desired colors.  16 bits/cell is plenty for that too.)
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 * Since the JPEG code is intended to run in small memory model on 80x86
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 * machines, we can't just allocate the histogram in one chunk.  Instead
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 * of a true 3-D array, we use a row of pointers to 2-D arrays.  Each
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 * pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and
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 * each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries.  Note that
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 * on 80x86 machines, the pointer row is in near memory but the actual
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 * arrays are in far memory (same arrangement as we use for image arrays).
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 */
130

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#define MAXNUMCOLORS  (MAXJSAMPLE+1) /* maximum size of colormap */
132

133
/* These will do the right thing for either R,G,B or B,G,R color order,
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 * but you may not like the results for other color orders.
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 */
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#define HIST_C0_BITS  5         /* bits of precision in R/B histogram */
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#define HIST_C1_BITS  6         /* bits of precision in G histogram */
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#define HIST_C2_BITS  5         /* bits of precision in B/R histogram */
139

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/* Number of elements along histogram axes. */
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#define HIST_C0_ELEMS  (1<<HIST_C0_BITS)
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#define HIST_C1_ELEMS  (1<<HIST_C1_BITS)
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#define HIST_C2_ELEMS  (1<<HIST_C2_BITS)
144

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/* These are the amounts to shift an input value to get a histogram index. */
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#define C0_SHIFT  (BITS_IN_JSAMPLE-HIST_C0_BITS)
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#define C1_SHIFT  (BITS_IN_JSAMPLE-HIST_C1_BITS)
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#define C2_SHIFT  (BITS_IN_JSAMPLE-HIST_C2_BITS)
149

150

151
typedef UINT16 histcell;        /* histogram cell; prefer an unsigned type */
152

153
typedef histcell FAR * histptr; /* for pointers to histogram cells */
154

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typedef histcell hist1d[HIST_C2_ELEMS]; /* typedefs for the array */
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typedef hist1d FAR * hist2d;    /* type for the 2nd-level pointers */
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typedef hist2d * hist3d;        /* type for top-level pointer */
158

159

160
/* Declarations for Floyd-Steinberg dithering.
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 *
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 * Errors are accumulated into the array fserrors[], at a resolution of
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 * 1/16th of a pixel count.  The error at a given pixel is propagated
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 * to its not-yet-processed neighbors using the standard F-S fractions,
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 *              ...     (here)  7/16
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 *              3/16    5/16    1/16
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 * We work left-to-right on even rows, right-to-left on odd rows.
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 *
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 * We can get away with a single array (holding one row's worth of errors)
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 * by using it to store the current row's errors at pixel columns not yet
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 * processed, but the next row's errors at columns already processed.  We
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 * need only a few extra variables to hold the errors immediately around the
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 * current column.  (If we are lucky, those variables are in registers, but
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 * even if not, they're probably cheaper to access than array elements are.)
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 *
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 * The fserrors[] array has (#columns + 2) entries; the extra entry at
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 * each end saves us from special-casing the first and last pixels.
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 * Each entry is three values long, one value for each color component.
179
 *
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 * Note: on a wide image, we might not have enough room in a PC's near data
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 * segment to hold the error array; so it is allocated with alloc_large.
182
 */
183

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#if BITS_IN_JSAMPLE == 8
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typedef INT16 FSERROR;          /* 16 bits should be enough */
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typedef int LOCFSERROR;         /* use 'int' for calculation temps */
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#else
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typedef INT32 FSERROR;          /* may need more than 16 bits */
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typedef INT32 LOCFSERROR;       /* be sure calculation temps are big enough */
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#endif
191

192
typedef FSERROR FAR *FSERRPTR;  /* pointer to error array (in FAR storage!) */
193

194

195
/* Private subobject */
196

197
typedef struct {
198
  struct jpeg_color_quantizer pub; /* public fields */
199

200
  /* Space for the eventually created colormap is stashed here */
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  JSAMPARRAY sv_colormap;       /* colormap allocated at init time */
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  int desired;                  /* desired # of colors = size of colormap */
203

204
  /* Variables for accumulating image statistics */
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  hist3d histogram;             /* pointer to the histogram */
206

207
  boolean needs_zeroed;         /* TRUE if next pass must zero histogram */
208

209
  /* Variables for Floyd-Steinberg dithering */
210
  FSERRPTR fserrors;            /* accumulated errors */
211
  boolean on_odd_row;           /* flag to remember which row we are on */
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  int * error_limiter;          /* table for clamping the applied error */
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} my_cquantizer;
214

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typedef my_cquantizer * my_cquantize_ptr;
216

217

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/*
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 * Prescan some rows of pixels.
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 * In this module the prescan simply updates the histogram, which has been
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 * initialized to zeroes by start_pass.
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 * An output_buf parameter is required by the method signature, but no data
223
 * is actually output (in fact the buffer controller is probably passing a
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 * NULL pointer).
225
 */
226

227
METHODDEF(void)
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prescan_quantize (j_decompress_ptr cinfo, JSAMPARRAY input_buf,
229
                  JSAMPARRAY output_buf, int num_rows)
230
{
231
  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
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  register JSAMPROW ptr;
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  register histptr histp;
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  register hist3d histogram = cquantize->histogram;
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  int row;
236
  JDIMENSION col;
237
  JDIMENSION width = cinfo->output_width;
238

239
  for (row = 0; row < num_rows; row++) {
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    ptr = input_buf[row];
241
    for (col = width; col > 0; col--) {
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      /* get pixel value and index into the histogram */
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      histp = & histogram[GETJSAMPLE(ptr[0]) >> C0_SHIFT]
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                         [GETJSAMPLE(ptr[1]) >> C1_SHIFT]
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                         [GETJSAMPLE(ptr[2]) >> C2_SHIFT];
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      /* increment, check for overflow and undo increment if so. */
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      if (++(*histp) <= 0)
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        (*histp)--;
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      ptr += 3;
250
    }
251
  }
252
}
253

254

255
/*
256
 * Next we have the really interesting routines: selection of a colormap
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 * given the completed histogram.
258
 * These routines work with a list of "boxes", each representing a rectangular
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 * subset of the input color space (to histogram precision).
260
 */
261

262
typedef struct {
263
  /* The bounds of the box (inclusive); expressed as histogram indexes */
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  int c0min, c0max;
265
  int c1min, c1max;
266
  int c2min, c2max;
267
  /* The volume (actually 2-norm) of the box */
268
  INT32 volume;
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  /* The number of nonzero histogram cells within this box */
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  long colorcount;
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} box;
272

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typedef box * boxptr;
274

275

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LOCAL(boxptr)
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find_biggest_color_pop (boxptr boxlist, int numboxes)
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/* Find the splittable box with the largest color population */
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/* Returns NULL if no splittable boxes remain */
280
{
281
  register boxptr boxp;
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  register int i;
283
  register long maxc = 0;
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  boxptr which = NULL;
285

286
  for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
287
    if (boxp->colorcount > maxc && boxp->volume > 0) {
288
      which = boxp;
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      maxc = boxp->colorcount;
290
    }
291
  }
292
  return which;
293
}
294

295

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LOCAL(boxptr)
297
find_biggest_volume (boxptr boxlist, int numboxes)
298
/* Find the splittable box with the largest (scaled) volume */
299
/* Returns NULL if no splittable boxes remain */
300
{
301
  register boxptr boxp;
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  register int i;
303
  register INT32 maxv = 0;
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  boxptr which = NULL;
305

306
  for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
307
    if (boxp->volume > maxv) {
308
      which = boxp;
309
      maxv = boxp->volume;
310
    }
311
  }
312
  return which;
313
}
314

315

316
LOCAL(void)
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update_box (j_decompress_ptr cinfo, boxptr boxp)
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/* Shrink the min/max bounds of a box to enclose only nonzero elements, */
319
/* and recompute its volume and population */
320
{
321
  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
322
  hist3d histogram = cquantize->histogram;
323
  histptr histp;
324
  int c0,c1,c2;
325
  int c0min,c0max,c1min,c1max,c2min,c2max;
326
  INT32 dist0,dist1,dist2;
327
  long ccount;
328

329
  c0min = boxp->c0min;  c0max = boxp->c0max;
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  c1min = boxp->c1min;  c1max = boxp->c1max;
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  c2min = boxp->c2min;  c2max = boxp->c2max;
332

333
  if (c0max > c0min)
334
    for (c0 = c0min; c0 <= c0max; c0++)
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      for (c1 = c1min; c1 <= c1max; c1++) {
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        histp = & histogram[c0][c1][c2min];
337
        for (c2 = c2min; c2 <= c2max; c2++)
338
          if (*histp++ != 0) {
339
            boxp->c0min = c0min = c0;
340
            goto have_c0min;
341
          }
342
      }
343
 have_c0min:
344
  if (c0max > c0min)
345
    for (c0 = c0max; c0 >= c0min; c0--)
346
      for (c1 = c1min; c1 <= c1max; c1++) {
347
        histp = & histogram[c0][c1][c2min];
348
        for (c2 = c2min; c2 <= c2max; c2++)
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          if (*histp++ != 0) {
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            boxp->c0max = c0max = c0;
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            goto have_c0max;
352
          }
353
      }
354
 have_c0max:
355
  if (c1max > c1min)
356
    for (c1 = c1min; c1 <= c1max; c1++)
357
      for (c0 = c0min; c0 <= c0max; c0++) {
358
        histp = & histogram[c0][c1][c2min];
359
        for (c2 = c2min; c2 <= c2max; c2++)
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          if (*histp++ != 0) {
361
            boxp->c1min = c1min = c1;
362
            goto have_c1min;
363
          }
364
      }
365
 have_c1min:
366
  if (c1max > c1min)
367
    for (c1 = c1max; c1 >= c1min; c1--)
368
      for (c0 = c0min; c0 <= c0max; c0++) {
369
        histp = & histogram[c0][c1][c2min];
370
        for (c2 = c2min; c2 <= c2max; c2++)
371
          if (*histp++ != 0) {
372
            boxp->c1max = c1max = c1;
373
            goto have_c1max;
374
          }
375
      }
376
 have_c1max:
377
  if (c2max > c2min)
378
    for (c2 = c2min; c2 <= c2max; c2++)
379
      for (c0 = c0min; c0 <= c0max; c0++) {
380
        histp = & histogram[c0][c1min][c2];
381
        for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
382
          if (*histp != 0) {
383
            boxp->c2min = c2min = c2;
384
            goto have_c2min;
385
          }
386
      }
387
 have_c2min:
388
  if (c2max > c2min)
389
    for (c2 = c2max; c2 >= c2min; c2--)
390
      for (c0 = c0min; c0 <= c0max; c0++) {
391
        histp = & histogram[c0][c1min][c2];
392
        for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
393
          if (*histp != 0) {
394
            boxp->c2max = c2max = c2;
395
            goto have_c2max;
396
          }
397
      }
398
 have_c2max:
399

400
  /* Update box volume.
401
   * We use 2-norm rather than real volume here; this biases the method
402
   * against making long narrow boxes, and it has the side benefit that
403
   * a box is splittable iff norm > 0.
404
   * Since the differences are expressed in histogram-cell units,
405
   * we have to shift back to JSAMPLE units to get consistent distances;
406
   * after which, we scale according to the selected distance scale factors.
407
   */
408
  dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE;
409
  dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE;
410
  dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE;
411
  boxp->volume = dist0*dist0 + dist1*dist1 + dist2*dist2;
412

413
  /* Now scan remaining volume of box and compute population */
414
  ccount = 0;
415
  for (c0 = c0min; c0 <= c0max; c0++)
416
    for (c1 = c1min; c1 <= c1max; c1++) {
417
      histp = & histogram[c0][c1][c2min];
418
      for (c2 = c2min; c2 <= c2max; c2++, histp++)
419
        if (*histp != 0) {
420
          ccount++;
421
        }
422
    }
423
  boxp->colorcount = ccount;
424
}
425

426

427
LOCAL(int)
428
median_cut (j_decompress_ptr cinfo, boxptr boxlist, int numboxes,
429
            int desired_colors)
430
/* Repeatedly select and split the largest box until we have enough boxes */
431
{
432
  int n,lb;
433
  int c0,c1,c2,cmax;
434
  register boxptr b1,b2;
435

436
  while (numboxes < desired_colors) {
437
    /* Select box to split.
438
     * Current algorithm: by population for first half, then by volume.
439
     */
440
    if (numboxes*2 <= desired_colors) {
441
      b1 = find_biggest_color_pop(boxlist, numboxes);
442
    } else {
443
      b1 = find_biggest_volume(boxlist, numboxes);
444
    }
445
    if (b1 == NULL)             /* no splittable boxes left! */
446
      break;
447
    b2 = &boxlist[numboxes];    /* where new box will go */
448
    /* Copy the color bounds to the new box. */
449
    b2->c0max = b1->c0max; b2->c1max = b1->c1max; b2->c2max = b1->c2max;
450
    b2->c0min = b1->c0min; b2->c1min = b1->c1min; b2->c2min = b1->c2min;
451
    /* Choose which axis to split the box on.
452
     * Current algorithm: longest scaled axis.
453
     * See notes in update_box about scaling distances.
454
     */
455
    c0 = ((b1->c0max - b1->c0min) << C0_SHIFT) * C0_SCALE;
456
    c1 = ((b1->c1max - b1->c1min) << C1_SHIFT) * C1_SCALE;
457
    c2 = ((b1->c2max - b1->c2min) << C2_SHIFT) * C2_SCALE;
458
    /* We want to break any ties in favor of green, then red, blue last.
459
     * This code does the right thing for R,G,B or B,G,R color orders only.
460
     */
461
#if RGB_RED == 0
462
    cmax = c1; n = 1;
463
    if (c0 > cmax) { cmax = c0; n = 0; }
464
    if (c2 > cmax) { n = 2; }
465
#else
466
    cmax = c1; n = 1;
467
    if (c2 > cmax) { cmax = c2; n = 2; }
468
    if (c0 > cmax) { n = 0; }
469
#endif
470
    /* Choose split point along selected axis, and update box bounds.
471
     * Current algorithm: split at halfway point.
472
     * (Since the box has been shrunk to minimum volume,
473
     * any split will produce two nonempty subboxes.)
474
     * Note that lb value is max for lower box, so must be < old max.
475
     */
476
    switch (n) {
477
    case 0:
478
      lb = (b1->c0max + b1->c0min) / 2;
479
      b1->c0max = lb;
480
      b2->c0min = lb+1;
481
      break;
482
    case 1:
483
      lb = (b1->c1max + b1->c1min) / 2;
484
      b1->c1max = lb;
485
      b2->c1min = lb+1;
486
      break;
487
    case 2:
488
      lb = (b1->c2max + b1->c2min) / 2;
489
      b1->c2max = lb;
490
      b2->c2min = lb+1;
491
      break;
492
    }
493
    /* Update stats for boxes */
494
    update_box(cinfo, b1);
495
    update_box(cinfo, b2);
496
    numboxes++;
497
  }
498
  return numboxes;
499
}
500

501

502
LOCAL(void)
503
compute_color (j_decompress_ptr cinfo, boxptr boxp, int icolor)
504
/* Compute representative color for a box, put it in colormap[icolor] */
505
{
506
  /* Current algorithm: mean weighted by pixels (not colors) */
507
  /* Note it is important to get the rounding correct! */
508
  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
509
  hist3d histogram = cquantize->histogram;
510
  histptr histp;
511
  int c0,c1,c2;
512
  int c0min,c0max,c1min,c1max,c2min,c2max;
513
  long count;
514
  long total = 0;
515
  long c0total = 0;
516
  long c1total = 0;
517
  long c2total = 0;
518

519
  c0min = boxp->c0min;  c0max = boxp->c0max;
520
  c1min = boxp->c1min;  c1max = boxp->c1max;
521
  c2min = boxp->c2min;  c2max = boxp->c2max;
522

523
  for (c0 = c0min; c0 <= c0max; c0++)
524
    for (c1 = c1min; c1 <= c1max; c1++) {
525
      histp = & histogram[c0][c1][c2min];
526
      for (c2 = c2min; c2 <= c2max; c2++) {
527
        if ((count = *histp++) != 0) {
528
          total += count;
529
          c0total += ((c0 << C0_SHIFT) + ((1<<C0_SHIFT)>>1)) * count;
530
          c1total += ((c1 << C1_SHIFT) + ((1<<C1_SHIFT)>>1)) * count;
531
          c2total += ((c2 << C2_SHIFT) + ((1<<C2_SHIFT)>>1)) * count;
532
        }
533
      }
534
    }
535

536
  cinfo->colormap[0][icolor] = (JSAMPLE) ((c0total + (total>>1)) / total);
537
  cinfo->colormap[1][icolor] = (JSAMPLE) ((c1total + (total>>1)) / total);
538
  cinfo->colormap[2][icolor] = (JSAMPLE) ((c2total + (total>>1)) / total);
539
}
540

541

542
LOCAL(void)
543
select_colors (j_decompress_ptr cinfo, int desired_colors)
544
/* Master routine for color selection */
545
{
546
  boxptr boxlist;
547
  int numboxes;
548
  int i;
549

550
  /* Allocate workspace for box list */
551
  boxlist = (boxptr) (*cinfo->mem->alloc_small)
552
    ((j_common_ptr) cinfo, JPOOL_IMAGE, desired_colors * SIZEOF(box));
553
  /* Initialize one box containing whole space */
554
  numboxes = 1;
555
  boxlist[0].c0min = 0;
556
  boxlist[0].c0max = MAXJSAMPLE >> C0_SHIFT;
557
  boxlist[0].c1min = 0;
558
  boxlist[0].c1max = MAXJSAMPLE >> C1_SHIFT;
559
  boxlist[0].c2min = 0;
560
  boxlist[0].c2max = MAXJSAMPLE >> C2_SHIFT;
561
  /* Shrink it to actually-used volume and set its statistics */
562
  update_box(cinfo, & boxlist[0]);
563
  /* Perform median-cut to produce final box list */
564
  numboxes = median_cut(cinfo, boxlist, numboxes, desired_colors);
565
  /* Compute the representative color for each box, fill colormap */
566
  for (i = 0; i < numboxes; i++)
567
    compute_color(cinfo, & boxlist[i], i);
568
  cinfo->actual_number_of_colors = numboxes;
569
  TRACEMS1(cinfo, 1, JTRC_QUANT_SELECTED, numboxes);
570
}
571

572

573
/*
574
 * These routines are concerned with the time-critical task of mapping input
575
 * colors to the nearest color in the selected colormap.
576
 *
577
 * We re-use the histogram space as an "inverse color map", essentially a
578
 * cache for the results of nearest-color searches.  All colors within a
579
 * histogram cell will be mapped to the same colormap entry, namely the one
580
 * closest to the cell's center.  This may not be quite the closest entry to
581
 * the actual input color, but it's almost as good.  A zero in the cache
582
 * indicates we haven't found the nearest color for that cell yet; the array
583
 * is cleared to zeroes before starting the mapping pass.  When we find the
584
 * nearest color for a cell, its colormap index plus one is recorded in the
585
 * cache for future use.  The pass2 scanning routines call fill_inverse_cmap
586
 * when they need to use an unfilled entry in the cache.
587
 *
588
 * Our method of efficiently finding nearest colors is based on the "locally
589
 * sorted search" idea described by Heckbert and on the incremental distance
590
 * calculation described by Spencer W. Thomas in chapter III.1 of Graphics
591
 * Gems II (James Arvo, ed.  Academic Press, 1991).  Thomas points out that
592
 * the distances from a given colormap entry to each cell of the histogram can
593
 * be computed quickly using an incremental method: the differences between
594
 * distances to adjacent cells themselves differ by a constant.  This allows a
595
 * fairly fast implementation of the "brute force" approach of computing the
596
 * distance from every colormap entry to every histogram cell.  Unfortunately,
597
 * it needs a work array to hold the best-distance-so-far for each histogram
598
 * cell (because the inner loop has to be over cells, not colormap entries).
599
 * The work array elements have to be INT32s, so the work array would need
600
 * 256Kb at our recommended precision.  This is not feasible in DOS machines.
601
 *
602
 * To get around these problems, we apply Thomas' method to compute the
603
 * nearest colors for only the cells within a small subbox of the histogram.
604
 * The work array need be only as big as the subbox, so the memory usage
605
 * problem is solved.  Furthermore, we need not fill subboxes that are never
606
 * referenced in pass2; many images use only part of the color gamut, so a
607
 * fair amount of work is saved.  An additional advantage of this
608
 * approach is that we can apply Heckbert's locality criterion to quickly
609
 * eliminate colormap entries that are far away from the subbox; typically
610
 * three-fourths of the colormap entries are rejected by Heckbert's criterion,
611
 * and we need not compute their distances to individual cells in the subbox.
612
 * The speed of this approach is heavily influenced by the subbox size: too
613
 * small means too much overhead, too big loses because Heckbert's criterion
614
 * can't eliminate as many colormap entries.  Empirically the best subbox
615
 * size seems to be about 1/512th of the histogram (1/8th in each direction).
616
 *
617
 * Thomas' article also describes a refined method which is asymptotically
618
 * faster than the brute-force method, but it is also far more complex and
619
 * cannot efficiently be applied to small subboxes.  It is therefore not
620
 * useful for programs intended to be portable to DOS machines.  On machines
621
 * with plenty of memory, filling the whole histogram in one shot with Thomas'
622
 * refined method might be faster than the present code --- but then again,
623
 * it might not be any faster, and it's certainly more complicated.
624
 */
625

626

627
/* log2(histogram cells in update box) for each axis; this can be adjusted */
628
#define BOX_C0_LOG  (HIST_C0_BITS-3)
629
#define BOX_C1_LOG  (HIST_C1_BITS-3)
630
#define BOX_C2_LOG  (HIST_C2_BITS-3)
631

632
#define BOX_C0_ELEMS  (1<<BOX_C0_LOG) /* # of hist cells in update box */
633
#define BOX_C1_ELEMS  (1<<BOX_C1_LOG)
634
#define BOX_C2_ELEMS  (1<<BOX_C2_LOG)
635

636
#define BOX_C0_SHIFT  (C0_SHIFT + BOX_C0_LOG)
637
#define BOX_C1_SHIFT  (C1_SHIFT + BOX_C1_LOG)
638
#define BOX_C2_SHIFT  (C2_SHIFT + BOX_C2_LOG)
639

640

641
/*
642
 * The next three routines implement inverse colormap filling.  They could
643
 * all be folded into one big routine, but splitting them up this way saves
644
 * some stack space (the mindist[] and bestdist[] arrays need not coexist)
645
 * and may allow some compilers to produce better code by registerizing more
646
 * inner-loop variables.
647
 */
648

649
LOCAL(int)
650
find_nearby_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
651
                    JSAMPLE colorlist[])
652
/* Locate the colormap entries close enough to an update box to be candidates
653
 * for the nearest entry to some cell(s) in the update box.  The update box
654
 * is specified by the center coordinates of its first cell.  The number of
655
 * candidate colormap entries is returned, and their colormap indexes are
656
 * placed in colorlist[].
657
 * This routine uses Heckbert's "locally sorted search" criterion to select
658
 * the colors that need further consideration.
659
 */
660
{
661
  int numcolors = cinfo->actual_number_of_colors;
662
  int maxc0, maxc1, maxc2;
663
  int centerc0, centerc1, centerc2;
664
  int i, x, ncolors;
665
  INT32 minmaxdist, min_dist, max_dist, tdist;
666
  INT32 mindist[MAXNUMCOLORS];  /* min distance to colormap entry i */
667

668
  /* Compute true coordinates of update box's upper corner and center.
669
   * Actually we compute the coordinates of the center of the upper-corner
670
   * histogram cell, which are the upper bounds of the volume we care about.
671
   * Note that since ">>" rounds down, the "center" values may be closer to
672
   * min than to max; hence comparisons to them must be "<=", not "<".
673
   */
674
  maxc0 = minc0 + ((1 << BOX_C0_SHIFT) - (1 << C0_SHIFT));
675
  centerc0 = (minc0 + maxc0) >> 1;
676
  maxc1 = minc1 + ((1 << BOX_C1_SHIFT) - (1 << C1_SHIFT));
677
  centerc1 = (minc1 + maxc1) >> 1;
678
  maxc2 = minc2 + ((1 << BOX_C2_SHIFT) - (1 << C2_SHIFT));
679
  centerc2 = (minc2 + maxc2) >> 1;
680

681
  /* For each color in colormap, find:
682
   *  1. its minimum squared-distance to any point in the update box
683
   *     (zero if color is within update box);
684
   *  2. its maximum squared-distance to any point in the update box.
685
   * Both of these can be found by considering only the corners of the box.
686
   * We save the minimum distance for each color in mindist[];
687
   * only the smallest maximum distance is of interest.
688
   */
689
  minmaxdist = 0x7FFFFFFFL;
690

691
  for (i = 0; i < numcolors; i++) {
692
    /* We compute the squared-c0-distance term, then add in the other two. */
693
    x = GETJSAMPLE(cinfo->colormap[0][i]);
694
    if (x < minc0) {
695
      tdist = (x - minc0) * C0_SCALE;
696
      min_dist = tdist*tdist;
697
      tdist = (x - maxc0) * C0_SCALE;
698
      max_dist = tdist*tdist;
699
    } else if (x > maxc0) {
700
      tdist = (x - maxc0) * C0_SCALE;
701
      min_dist = tdist*tdist;
702
      tdist = (x - minc0) * C0_SCALE;
703
      max_dist = tdist*tdist;
704
    } else {
705
      /* within cell range so no contribution to min_dist */
706
      min_dist = 0;
707
      if (x <= centerc0) {
708
        tdist = (x - maxc0) * C0_SCALE;
709
        max_dist = tdist*tdist;
710
      } else {
711
        tdist = (x - minc0) * C0_SCALE;
712
        max_dist = tdist*tdist;
713
      }
714
    }
715

716
    x = GETJSAMPLE(cinfo->colormap[1][i]);
717
    if (x < minc1) {
718
      tdist = (x - minc1) * C1_SCALE;
719
      min_dist += tdist*tdist;
720
      tdist = (x - maxc1) * C1_SCALE;
721
      max_dist += tdist*tdist;
722
    } else if (x > maxc1) {
723
      tdist = (x - maxc1) * C1_SCALE;
724
      min_dist += tdist*tdist;
725
      tdist = (x - minc1) * C1_SCALE;
726
      max_dist += tdist*tdist;
727
    } else {
728
      /* within cell range so no contribution to min_dist */
729
      if (x <= centerc1) {
730
        tdist = (x - maxc1) * C1_SCALE;
731
        max_dist += tdist*tdist;
732
      } else {
733
        tdist = (x - minc1) * C1_SCALE;
734
        max_dist += tdist*tdist;
735
      }
736
    }
737

738
    x = GETJSAMPLE(cinfo->colormap[2][i]);
739
    if (x < minc2) {
740
      tdist = (x - minc2) * C2_SCALE;
741
      min_dist += tdist*tdist;
742
      tdist = (x - maxc2) * C2_SCALE;
743
      max_dist += tdist*tdist;
744
    } else if (x > maxc2) {
745
      tdist = (x - maxc2) * C2_SCALE;
746
      min_dist += tdist*tdist;
747
      tdist = (x - minc2) * C2_SCALE;
748
      max_dist += tdist*tdist;
749
    } else {
750
      /* within cell range so no contribution to min_dist */
751
      if (x <= centerc2) {
752
        tdist = (x - maxc2) * C2_SCALE;
753
        max_dist += tdist*tdist;
754
      } else {
755
        tdist = (x - minc2) * C2_SCALE;
756
        max_dist += tdist*tdist;
757
      }
758
    }
759

760
    mindist[i] = min_dist;      /* save away the results */
761
    if (max_dist < minmaxdist)
762
      minmaxdist = max_dist;
763
  }
764

765
  /* Now we know that no cell in the update box is more than minmaxdist
766
   * away from some colormap entry.  Therefore, only colors that are
767
   * within minmaxdist of some part of the box need be considered.
768
   */
769
  ncolors = 0;
770
  for (i = 0; i < numcolors; i++) {
771
    if (mindist[i] <= minmaxdist)
772
      colorlist[ncolors++] = (JSAMPLE) i;
773
  }
774
  return ncolors;
775
}
776

777

778
LOCAL(void)
779
find_best_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
780
                  int numcolors, JSAMPLE colorlist[], JSAMPLE bestcolor[])
781
/* Find the closest colormap entry for each cell in the update box,
782
 * given the list of candidate colors prepared by find_nearby_colors.
783
 * Return the indexes of the closest entries in the bestcolor[] array.
784
 * This routine uses Thomas' incremental distance calculation method to
785
 * find the distance from a colormap entry to successive cells in the box.
786
 */
787
{
788
  int ic0, ic1, ic2;
789
  int i, icolor;
790
  register INT32 * bptr;        /* pointer into bestdist[] array */
791
  JSAMPLE * cptr;               /* pointer into bestcolor[] array */
792
  INT32 dist0, dist1;           /* initial distance values */
793
  register INT32 dist2;         /* current distance in inner loop */
794
  INT32 xx0, xx1;               /* distance increments */
795
  register INT32 xx2;
796
  INT32 inc0, inc1, inc2;       /* initial values for increments */
797
  /* This array holds the distance to the nearest-so-far color for each cell */
798
  INT32 bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
799

800
  /* Initialize best-distance for each cell of the update box */
801
  bptr = bestdist;
802
  for (i = BOX_C0_ELEMS*BOX_C1_ELEMS*BOX_C2_ELEMS-1; i >= 0; i--)
803
    *bptr++ = 0x7FFFFFFFL;
804

805
  /* For each color selected by find_nearby_colors,
806
   * compute its distance to the center of each cell in the box.
807
   * If that's less than best-so-far, update best distance and color number.
808
   */
809

810
  /* Nominal steps between cell centers ("x" in Thomas article) */
811
#define STEP_C0  ((1 << C0_SHIFT) * C0_SCALE)
812
#define STEP_C1  ((1 << C1_SHIFT) * C1_SCALE)
813
#define STEP_C2  ((1 << C2_SHIFT) * C2_SCALE)
814

815
  for (i = 0; i < numcolors; i++) {
816
    icolor = GETJSAMPLE(colorlist[i]);
817
    /* Compute (square of) distance from minc0/c1/c2 to this color */
818
    inc0 = (minc0 - GETJSAMPLE(cinfo->colormap[0][icolor])) * C0_SCALE;
819
    dist0 = inc0*inc0;
820
    inc1 = (minc1 - GETJSAMPLE(cinfo->colormap[1][icolor])) * C1_SCALE;
821
    dist0 += inc1*inc1;
822
    inc2 = (minc2 - GETJSAMPLE(cinfo->colormap[2][icolor])) * C2_SCALE;
823
    dist0 += inc2*inc2;
824
    /* Form the initial difference increments */
825
    inc0 = inc0 * (2 * STEP_C0) + STEP_C0 * STEP_C0;
826
    inc1 = inc1 * (2 * STEP_C1) + STEP_C1 * STEP_C1;
827
    inc2 = inc2 * (2 * STEP_C2) + STEP_C2 * STEP_C2;
828
    /* Now loop over all cells in box, updating distance per Thomas method */
829
    bptr = bestdist;
830
    cptr = bestcolor;
831
    xx0 = inc0;
832
    for (ic0 = BOX_C0_ELEMS-1; ic0 >= 0; ic0--) {
833
      dist1 = dist0;
834
      xx1 = inc1;
835
      for (ic1 = BOX_C1_ELEMS-1; ic1 >= 0; ic1--) {
836
        dist2 = dist1;
837
        xx2 = inc2;
838
        for (ic2 = BOX_C2_ELEMS-1; ic2 >= 0; ic2--) {
839
          if (dist2 < *bptr) {
840
            *bptr = dist2;
841
            *cptr = (JSAMPLE) icolor;
842
          }
843
          dist2 += xx2;
844
          xx2 += 2 * STEP_C2 * STEP_C2;
845
          bptr++;
846
          cptr++;
847
        }
848
        dist1 += xx1;
849
        xx1 += 2 * STEP_C1 * STEP_C1;
850
      }
851
      dist0 += xx0;
852
      xx0 += 2 * STEP_C0 * STEP_C0;
853
    }
854
  }
855
}
856

857

858
LOCAL(void)
859
fill_inverse_cmap (j_decompress_ptr cinfo, int c0, int c1, int c2)
860
/* Fill the inverse-colormap entries in the update box that contains */
861
/* histogram cell c0/c1/c2.  (Only that one cell MUST be filled, but */
862
/* we can fill as many others as we wish.) */
863
{
864
  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
865
  hist3d histogram = cquantize->histogram;
866
  int minc0, minc1, minc2;      /* lower left corner of update box */
867
  int ic0, ic1, ic2;
868
  register JSAMPLE * cptr;      /* pointer into bestcolor[] array */
869
  register histptr cachep;      /* pointer into main cache array */
870
  /* This array lists the candidate colormap indexes. */
871
  JSAMPLE colorlist[MAXNUMCOLORS];
872
  int numcolors;                /* number of candidate colors */
873
  /* This array holds the actually closest colormap index for each cell. */
874
  JSAMPLE bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
875

876
  /* Convert cell coordinates to update box ID */
877
  c0 >>= BOX_C0_LOG;
878
  c1 >>= BOX_C1_LOG;
879
  c2 >>= BOX_C2_LOG;
880

881
  /* Compute true coordinates of update box's origin corner.
882
   * Actually we compute the coordinates of the center of the corner
883
   * histogram cell, which are the lower bounds of the volume we care about.
884
   */
885
  minc0 = (c0 << BOX_C0_SHIFT) + ((1 << C0_SHIFT) >> 1);
886
  minc1 = (c1 << BOX_C1_SHIFT) + ((1 << C1_SHIFT) >> 1);
887
  minc2 = (c2 << BOX_C2_SHIFT) + ((1 << C2_SHIFT) >> 1);
888

889
  /* Determine which colormap entries are close enough to be candidates
890
   * for the nearest entry to some cell in the update box.
891
   */
892
  numcolors = find_nearby_colors(cinfo, minc0, minc1, minc2, colorlist);
893

894
  /* Determine the actually nearest colors. */
895
  find_best_colors(cinfo, minc0, minc1, minc2, numcolors, colorlist,
896
                   bestcolor);
897

898
  /* Save the best color numbers (plus 1) in the main cache array */
899
  c0 <<= BOX_C0_LOG;            /* convert ID back to base cell indexes */
900
  c1 <<= BOX_C1_LOG;
901
  c2 <<= BOX_C2_LOG;
902
  cptr = bestcolor;
903
  for (ic0 = 0; ic0 < BOX_C0_ELEMS; ic0++) {
904
    for (ic1 = 0; ic1 < BOX_C1_ELEMS; ic1++) {
905
      cachep = & histogram[c0+ic0][c1+ic1][c2];
906
      for (ic2 = 0; ic2 < BOX_C2_ELEMS; ic2++) {
907
        *cachep++ = (histcell) (GETJSAMPLE(*cptr++) + 1);
908
      }
909
    }
910
  }
911
}
912

913

914
/*
915
 * Map some rows of pixels to the output colormapped representation.
916
 */
917

918
METHODDEF(void)
919
pass2_no_dither (j_decompress_ptr cinfo,
920
                 JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)
921
/* This version performs no dithering */
922
{
923
  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
924
  hist3d histogram = cquantize->histogram;
925
  register JSAMPROW inptr, outptr;
926
  register histptr cachep;
927
  register int c0, c1, c2;
928
  int row;
929
  JDIMENSION col;
930
  JDIMENSION width = cinfo->output_width;
931

932
  for (row = 0; row < num_rows; row++) {
933
    inptr = input_buf[row];
934
    outptr = output_buf[row];
935
    for (col = width; col > 0; col--) {
936
      /* get pixel value and index into the cache */
937
      c0 = GETJSAMPLE(*inptr++) >> C0_SHIFT;
938
      c1 = GETJSAMPLE(*inptr++) >> C1_SHIFT;
939
      c2 = GETJSAMPLE(*inptr++) >> C2_SHIFT;
940
      cachep = & histogram[c0][c1][c2];
941
      /* If we have not seen this color before, find nearest colormap entry */
942
      /* and update the cache */
943
      if (*cachep == 0)
944
        fill_inverse_cmap(cinfo, c0,c1,c2);
945
      /* Now emit the colormap index for this cell */
946
      *outptr++ = (JSAMPLE) (*cachep - 1);
947
    }
948
  }
949
}
950

951

952
METHODDEF(void)
953
pass2_fs_dither (j_decompress_ptr cinfo,
954
                 JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)
955
/* This version performs Floyd-Steinberg dithering */
956
{
957
  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
958
  hist3d histogram = cquantize->histogram;
959
  register LOCFSERROR cur0, cur1, cur2; /* current error or pixel value */
960
  LOCFSERROR belowerr0, belowerr1, belowerr2; /* error for pixel below cur */
961
  LOCFSERROR bpreverr0, bpreverr1, bpreverr2; /* error for below/prev col */
962
  register FSERRPTR errorptr;   /* => fserrors[] at column before current */
963
  JSAMPROW inptr;               /* => current input pixel */
964
  JSAMPROW outptr;              /* => current output pixel */
965
  histptr cachep;
966
  int dir;                      /* +1 or -1 depending on direction */
967
  int dir3;                     /* 3*dir, for advancing inptr & errorptr */
968
  int row;
969
  JDIMENSION col;
970
  JDIMENSION width = cinfo->output_width;
971
  JSAMPLE *range_limit = cinfo->sample_range_limit;
972
  int *error_limit = cquantize->error_limiter;
973
  JSAMPROW colormap0 = cinfo->colormap[0];
974
  JSAMPROW colormap1 = cinfo->colormap[1];
975
  JSAMPROW colormap2 = cinfo->colormap[2];
976
  SHIFT_TEMPS
977

978
  for (row = 0; row < num_rows; row++) {
979
    inptr = input_buf[row];
980
    outptr = output_buf[row];
981
    if (cquantize->on_odd_row) {
982
      /* work right to left in this row */
983
      inptr += (width-1) * 3;   /* so point to rightmost pixel */
984
      outptr += width-1;
985
      dir = -1;
986
      dir3 = -3;
987
      errorptr = cquantize->fserrors + (width+1)*3; /* => entry after last column */
988
      cquantize->on_odd_row = FALSE; /* flip for next time */
989
    } else {
990
      /* work left to right in this row */
991
      dir = 1;
992
      dir3 = 3;
993
      errorptr = cquantize->fserrors; /* => entry before first real column */
994
      cquantize->on_odd_row = TRUE; /* flip for next time */
995
    }
996
    /* Preset error values: no error propagated to first pixel from left */
997
    cur0 = cur1 = cur2 = 0;
998
    /* and no error propagated to row below yet */
999
    belowerr0 = belowerr1 = belowerr2 = 0;
1000
    bpreverr0 = bpreverr1 = bpreverr2 = 0;
1001

1002
    for (col = width; col > 0; col--) {
1003
      /* curN holds the error propagated from the previous pixel on the
1004
       * current line.  Add the error propagated from the previous line
1005
       * to form the complete error correction term for this pixel, and
1006
       * round the error term (which is expressed * 16) to an integer.
1007
       * RIGHT_SHIFT rounds towards minus infinity, so adding 8 is correct
1008
       * for either sign of the error value.
1009
       * Note: errorptr points to *previous* column's array entry.
1010
       */
1011
      cur0 = RIGHT_SHIFT(cur0 + errorptr[dir3+0] + 8, 4);
1012
      cur1 = RIGHT_SHIFT(cur1 + errorptr[dir3+1] + 8, 4);
1013
      cur2 = RIGHT_SHIFT(cur2 + errorptr[dir3+2] + 8, 4);
1014
      /* Limit the error using transfer function set by init_error_limit.
1015
       * See comments with init_error_limit for rationale.
1016
       */
1017
      cur0 = error_limit[cur0];
1018
      cur1 = error_limit[cur1];
1019
      cur2 = error_limit[cur2];
1020
      /* Form pixel value + error, and range-limit to 0..MAXJSAMPLE.
1021
       * The maximum error is +- MAXJSAMPLE (or less with error limiting);
1022
       * this sets the required size of the range_limit array.
1023
       */
1024
      cur0 += GETJSAMPLE(inptr[0]);
1025
      cur1 += GETJSAMPLE(inptr[1]);
1026
      cur2 += GETJSAMPLE(inptr[2]);
1027
      cur0 = GETJSAMPLE(range_limit[cur0]);
1028
      cur1 = GETJSAMPLE(range_limit[cur1]);
1029
      cur2 = GETJSAMPLE(range_limit[cur2]);
1030
      /* Index into the cache with adjusted pixel value */
1031
      cachep = & histogram[cur0>>C0_SHIFT][cur1>>C1_SHIFT][cur2>>C2_SHIFT];
1032
      /* If we have not seen this color before, find nearest colormap */
1033
      /* entry and update the cache */
1034
      if (*cachep == 0)
1035
        fill_inverse_cmap(cinfo, cur0>>C0_SHIFT,cur1>>C1_SHIFT,cur2>>C2_SHIFT);
1036
      /* Now emit the colormap index for this cell */
1037
      { register int pixcode = *cachep - 1;
1038
        *outptr = (JSAMPLE) pixcode;
1039
        /* Compute representation error for this pixel */
1040
        cur0 -= GETJSAMPLE(colormap0[pixcode]);
1041
        cur1 -= GETJSAMPLE(colormap1[pixcode]);
1042
        cur2 -= GETJSAMPLE(colormap2[pixcode]);
1043
      }
1044
      /* Compute error fractions to be propagated to adjacent pixels.
1045
       * Add these into the running sums, and simultaneously shift the
1046
       * next-line error sums left by 1 column.
1047
       */
1048
      { register LOCFSERROR bnexterr, delta;
1049

1050
        bnexterr = cur0;        /* Process component 0 */
1051
        delta = cur0 * 2;
1052
        cur0 += delta;          /* form error * 3 */
1053
        errorptr[0] = (FSERROR) (bpreverr0 + cur0);
1054
        cur0 += delta;          /* form error * 5 */
1055
        bpreverr0 = belowerr0 + cur0;
1056
        belowerr0 = bnexterr;
1057
        cur0 += delta;          /* form error * 7 */
1058
        bnexterr = cur1;        /* Process component 1 */
1059
        delta = cur1 * 2;
1060
        cur1 += delta;          /* form error * 3 */
1061
        errorptr[1] = (FSERROR) (bpreverr1 + cur1);
1062
        cur1 += delta;          /* form error * 5 */
1063
        bpreverr1 = belowerr1 + cur1;
1064
        belowerr1 = bnexterr;
1065
        cur1 += delta;          /* form error * 7 */
1066
        bnexterr = cur2;        /* Process component 2 */
1067
        delta = cur2 * 2;
1068
        cur2 += delta;          /* form error * 3 */
1069
        errorptr[2] = (FSERROR) (bpreverr2 + cur2);
1070
        cur2 += delta;          /* form error * 5 */
1071
        bpreverr2 = belowerr2 + cur2;
1072
        belowerr2 = bnexterr;
1073
        cur2 += delta;          /* form error * 7 */
1074
      }
1075
      /* At this point curN contains the 7/16 error value to be propagated
1076
       * to the next pixel on the current line, and all the errors for the
1077
       * next line have been shifted over.  We are therefore ready to move on.
1078
       */
1079
      inptr += dir3;            /* Advance pixel pointers to next column */
1080
      outptr += dir;
1081
      errorptr += dir3;         /* advance errorptr to current column */
1082
    }
1083
    /* Post-loop cleanup: we must unload the final error values into the
1084
     * final fserrors[] entry.  Note we need not unload belowerrN because
1085
     * it is for the dummy column before or after the actual array.
1086
     */
1087
    errorptr[0] = (FSERROR) bpreverr0; /* unload prev errs into array */
1088
    errorptr[1] = (FSERROR) bpreverr1;
1089
    errorptr[2] = (FSERROR) bpreverr2;
1090
  }
1091
}
1092

1093

1094
/*
1095
 * Initialize the error-limiting transfer function (lookup table).
1096
 * The raw F-S error computation can potentially compute error values of up to
1097
 * +- MAXJSAMPLE.  But we want the maximum correction applied to a pixel to be
1098
 * much less, otherwise obviously wrong pixels will be created.  (Typical
1099
 * effects include weird fringes at color-area boundaries, isolated bright
1100
 * pixels in a dark area, etc.)  The standard advice for avoiding this problem
1101
 * is to ensure that the "corners" of the color cube are allocated as output
1102
 * colors; then repeated errors in the same direction cannot cause cascading
1103
 * error buildup.  However, that only prevents the error from getting
1104
 * completely out of hand; Aaron Giles reports that error limiting improves
1105
 * the results even with corner colors allocated.
1106
 * A simple clamping of the error values to about +- MAXJSAMPLE/8 works pretty
1107
 * well, but the smoother transfer function used below is even better.  Thanks
1108
 * to Aaron Giles for this idea.
1109
 */
1110

1111
LOCAL(void)
1112
init_error_limit (j_decompress_ptr cinfo)
1113
/* Allocate and fill in the error_limiter table */
1114
{
1115
  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1116
  int * table;
1117
  int in, out;
1118

1119
  table = (int *) (*cinfo->mem->alloc_small)
1120
    ((j_common_ptr) cinfo, JPOOL_IMAGE, (MAXJSAMPLE*2+1) * SIZEOF(int));
1121
  table += MAXJSAMPLE;          /* so can index -MAXJSAMPLE .. +MAXJSAMPLE */
1122
  cquantize->error_limiter = table;
1123

1124
#define STEPSIZE ((MAXJSAMPLE+1)/16)
1125
  /* Map errors 1:1 up to +- MAXJSAMPLE/16 */
1126
  out = 0;
1127
  for (in = 0; in < STEPSIZE; in++, out++) {
1128
    table[in] = out; table[-in] = -out;
1129
  }
1130
  /* Map errors 1:2 up to +- 3*MAXJSAMPLE/16 */
1131
  for (; in < STEPSIZE*3; in++, out += (in&1) ? 0 : 1) {
1132
    table[in] = out; table[-in] = -out;
1133
  }
1134
  /* Clamp the rest to final out value (which is (MAXJSAMPLE+1)/8) */
1135
  for (; in <= MAXJSAMPLE; in++) {
1136
    table[in] = out; table[-in] = -out;
1137
  }
1138
#undef STEPSIZE
1139
}
1140

1141

1142
/*
1143
 * Finish up at the end of each pass.
1144
 */
1145

1146
METHODDEF(void)
1147
finish_pass1 (j_decompress_ptr cinfo)
1148
{
1149
  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1150

1151
  /* Select the representative colors and fill in cinfo->colormap */
1152
  cinfo->colormap = cquantize->sv_colormap;
1153
  select_colors(cinfo, cquantize->desired);
1154
  /* Force next pass to zero the color index table */
1155
  cquantize->needs_zeroed = TRUE;
1156
}
1157

1158

1159
METHODDEF(void)
1160
finish_pass2 (j_decompress_ptr cinfo)
1161
{
1162
  /* no work */
1163
}
1164

1165

1166
/*
1167
 * Initialize for each processing pass.
1168
 */
1169

1170
METHODDEF(void)
1171
start_pass_2_quant (j_decompress_ptr cinfo, boolean is_pre_scan)
1172
{
1173
  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1174
  hist3d histogram = cquantize->histogram;
1175
  int i;
1176

1177
  /* Only F-S dithering or no dithering is supported. */
1178
  /* If user asks for ordered dither, give him F-S. */
1179
  if (cinfo->dither_mode != JDITHER_NONE)
1180
    cinfo->dither_mode = JDITHER_FS;
1181

1182
  if (is_pre_scan) {
1183
    /* Set up method pointers */
1184
    cquantize->pub.color_quantize = prescan_quantize;
1185
    cquantize->pub.finish_pass = finish_pass1;
1186
    cquantize->needs_zeroed = TRUE; /* Always zero histogram */
1187
  } else {
1188
    /* Set up method pointers */
1189
    if (cinfo->dither_mode == JDITHER_FS)
1190
      cquantize->pub.color_quantize = pass2_fs_dither;
1191
    else
1192
      cquantize->pub.color_quantize = pass2_no_dither;
1193
    cquantize->pub.finish_pass = finish_pass2;
1194

1195
    /* Make sure color count is acceptable */
1196
    i = cinfo->actual_number_of_colors;
1197
    if (i < 1)
1198
      ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 1);
1199
    if (i > MAXNUMCOLORS)
1200
      ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
1201

1202
    if (cinfo->dither_mode == JDITHER_FS) {
1203
      size_t arraysize = (size_t) ((cinfo->output_width + 2) *
1204
                                   (3 * SIZEOF(FSERROR)));
1205
      /* Allocate Floyd-Steinberg workspace if we didn't already. */
1206
      if (cquantize->fserrors == NULL)
1207
        cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)
1208
          ((j_common_ptr) cinfo, JPOOL_IMAGE, arraysize);
1209
      /* Initialize the propagated errors to zero. */
1210
      jzero_far((void FAR *) cquantize->fserrors, arraysize);
1211
      /* Make the error-limit table if we didn't already. */
1212
      if (cquantize->error_limiter == NULL)
1213
        init_error_limit(cinfo);
1214
      cquantize->on_odd_row = FALSE;
1215
    }
1216

1217
  }
1218
  /* Zero the histogram or inverse color map, if necessary */
1219
  if (cquantize->needs_zeroed) {
1220
    for (i = 0; i < HIST_C0_ELEMS; i++) {
1221
      jzero_far((void FAR *) histogram[i],
1222
                HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));
1223
    }
1224
    cquantize->needs_zeroed = FALSE;
1225
  }
1226
}
1227

1228

1229
/*
1230
 * Switch to a new external colormap between output passes.
1231
 */
1232

1233
METHODDEF(void)
1234
new_color_map_2_quant (j_decompress_ptr cinfo)
1235
{
1236
  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1237

1238
  /* Reset the inverse color map */
1239
  cquantize->needs_zeroed = TRUE;
1240
}
1241

1242

1243
/*
1244
 * Module initialization routine for 2-pass color quantization.
1245
 */
1246

1247
GLOBAL(void)
1248
jinit_2pass_quantizer (j_decompress_ptr cinfo)
1249
{
1250
  my_cquantize_ptr cquantize;
1251
  int i;
1252

1253
  cquantize = (my_cquantize_ptr)
1254
    (*cinfo->mem->alloc_small) ((j_common_ptr) cinfo, JPOOL_IMAGE,
1255
                                SIZEOF(my_cquantizer));
1256
  cinfo->cquantize = (struct jpeg_color_quantizer *) cquantize;
1257
  cquantize->pub.start_pass = start_pass_2_quant;
1258
  cquantize->pub.new_color_map = new_color_map_2_quant;
1259
  cquantize->fserrors = NULL;   /* flag optional arrays not allocated */
1260
  cquantize->error_limiter = NULL;
1261

1262
  /* Make sure jdmaster didn't give me a case I can't handle */
1263
  if (cinfo->out_color_components != 3)
1264
    ERREXIT(cinfo, JERR_NOTIMPL);
1265

1266
  /* Allocate the histogram/inverse colormap storage */
1267
  cquantize->histogram = (hist3d) (*cinfo->mem->alloc_small)
1268
    ((j_common_ptr) cinfo, JPOOL_IMAGE, HIST_C0_ELEMS * SIZEOF(hist2d));
1269
  for (i = 0; i < HIST_C0_ELEMS; i++) {
1270
    cquantize->histogram[i] = (hist2d) (*cinfo->mem->alloc_large)
1271
      ((j_common_ptr) cinfo, JPOOL_IMAGE,
1272
       HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));
1273
  }
1274
  cquantize->needs_zeroed = TRUE; /* histogram is garbage now */
1275

1276
  /* Allocate storage for the completed colormap, if required.
1277
   * We do this now since it is FAR storage and may affect
1278
   * the memory manager's space calculations.
1279
   */
1280
  if (cinfo->enable_2pass_quant) {
1281
    /* Make sure color count is acceptable */
1282
    int desired = cinfo->desired_number_of_colors;
1283
    /* Lower bound on # of colors ... somewhat arbitrary as long as > 0 */
1284
    if (desired < 8)
1285
      ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 8);
1286
    /* Make sure colormap indexes can be represented by JSAMPLEs */
1287
    if (desired > MAXNUMCOLORS)
1288
      ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
1289
    cquantize->sv_colormap = (*cinfo->mem->alloc_sarray)
1290
      ((j_common_ptr) cinfo,JPOOL_IMAGE, (JDIMENSION) desired, (JDIMENSION) 3);
1291
    cquantize->desired = desired;
1292
  } else
1293
    cquantize->sv_colormap = NULL;
1294

1295
  /* Only F-S dithering or no dithering is supported. */
1296
  /* If user asks for ordered dither, give him F-S. */
1297
  if (cinfo->dither_mode != JDITHER_NONE)
1298
    cinfo->dither_mode = JDITHER_FS;
1299

1300
  /* Allocate Floyd-Steinberg workspace if necessary.
1301
   * This isn't really needed until pass 2, but again it is FAR storage.
1302
   * Although we will cope with a later change in dither_mode,
1303
   * we do not promise to honor max_memory_to_use if dither_mode changes.
1304
   */
1305
  if (cinfo->dither_mode == JDITHER_FS) {
1306
    cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)
1307
      ((j_common_ptr) cinfo, JPOOL_IMAGE,
1308
       (size_t) ((cinfo->output_width + 2) * (3 * SIZEOF(FSERROR))));
1309
    /* Might as well create the error-limiting table too. */
1310
    init_error_limit(cinfo);
1311
  }
1312
}
1313

1314
#endif /* QUANT_2PASS_SUPPORTED */
1315