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/src/FreeImage/Source/LibJPEG/jquant2.c

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   1/*
   2 * jquant2.c
   3 *
   4 * Copyright (C) 1991-1996, Thomas G. Lane.
   5 * Modified 2011 by Guido Vollbeding.
   6 * This file is part of the Independent JPEG Group's software.
   7 * For conditions of distribution and use, see the accompanying README file.
   8 *
   9 * This file contains 2-pass color quantization (color mapping) routines.
  10 * These routines provide selection of a custom color map for an image,
  11 * followed by mapping of the image to that color map, with optional
  12 * Floyd-Steinberg dithering.
  13 * It is also possible to use just the second pass to map to an arbitrary
  14 * externally-given color map.
  15 *
  16 * Note: ordered dithering is not supported, since there isn't any fast
  17 * way to compute intercolor distances; it's unclear that ordered dither's
  18 * fundamental assumptions even hold with an irregularly spaced color map.
  19 */
  20
  21#define JPEG_INTERNALS
  22#include "jinclude.h"
  23#include "jpeglib.h"
  24
  25#ifdef QUANT_2PASS_SUPPORTED
  26
  27
  28/*
  29 * This module implements the well-known Heckbert paradigm for color
  30 * quantization.  Most of the ideas used here can be traced back to
  31 * Heckbert's seminal paper
  32 *   Heckbert, Paul.  "Color Image Quantization for Frame Buffer Display",
  33 *   Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304.
  34 *
  35 * In the first pass over the image, we accumulate a histogram showing the
  36 * usage count of each possible color.  To keep the histogram to a reasonable
  37 * size, we reduce the precision of the input; typical practice is to retain
  38 * 5 or 6 bits per color, so that 8 or 4 different input values are counted
  39 * in the same histogram cell.
  40 *
  41 * Next, the color-selection step begins with a box representing the whole
  42 * color space, and repeatedly splits the "largest" remaining box until we
  43 * have as many boxes as desired colors.  Then the mean color in each
  44 * remaining box becomes one of the possible output colors.
  45 * 
  46 * The second pass over the image maps each input pixel to the closest output
  47 * color (optionally after applying a Floyd-Steinberg dithering correction).
  48 * This mapping is logically trivial, but making it go fast enough requires
  49 * considerable care.
  50 *
  51 * Heckbert-style quantizers vary a good deal in their policies for choosing
  52 * the "largest" box and deciding where to cut it.  The particular policies
  53 * used here have proved out well in experimental comparisons, but better ones
  54 * may yet be found.
  55 *
  56 * In earlier versions of the IJG code, this module quantized in YCbCr color
  57 * space, processing the raw upsampled data without a color conversion step.
  58 * This allowed the color conversion math to be done only once per colormap
  59 * entry, not once per pixel.  However, that optimization precluded other
  60 * useful optimizations (such as merging color conversion with upsampling)
  61 * and it also interfered with desired capabilities such as quantizing to an
  62 * externally-supplied colormap.  We have therefore abandoned that approach.
  63 * The present code works in the post-conversion color space, typically RGB.
  64 *
  65 * To improve the visual quality of the results, we actually work in scaled
  66 * RGB space, giving G distances more weight than R, and R in turn more than
  67 * B.  To do everything in integer math, we must use integer scale factors.
  68 * The 2/3/1 scale factors used here correspond loosely to the relative
  69 * weights of the colors in the NTSC grayscale equation.
  70 * If you want to use this code to quantize a non-RGB color space, you'll
  71 * probably need to change these scale factors.
  72 */
  73
  74#define R_SCALE 2		/* scale R distances by this much */
  75#define G_SCALE 3		/* scale G distances by this much */
  76#define B_SCALE 1		/* and B by this much */
  77
  78/* Relabel R/G/B as components 0/1/2, respecting the RGB ordering defined
  79 * in jmorecfg.h.  As the code stands, it will do the right thing for R,G,B
  80 * and B,G,R orders.  If you define some other weird order in jmorecfg.h,
  81 * you'll get compile errors until you extend this logic.  In that case
  82 * you'll probably want to tweak the histogram sizes too.
  83 */
  84
  85#if RGB_RED == 0
  86#define C0_SCALE R_SCALE
  87#endif
  88#if RGB_BLUE == 0
  89#define C0_SCALE B_SCALE
  90#endif
  91#if RGB_GREEN == 1
  92#define C1_SCALE G_SCALE
  93#endif
  94#if RGB_RED == 2
  95#define C2_SCALE R_SCALE
  96#endif
  97#if RGB_BLUE == 2
  98#define C2_SCALE B_SCALE
  99#endif
 100
 101
 102/*
 103 * First we have the histogram data structure and routines for creating it.
 104 *
 105 * The number of bits of precision can be adjusted by changing these symbols.
 106 * We recommend keeping 6 bits for G and 5 each for R and B.
 107 * If you have plenty of memory and cycles, 6 bits all around gives marginally
 108 * better results; if you are short of memory, 5 bits all around will save
 109 * some space but degrade the results.
 110 * To maintain a fully accurate histogram, we'd need to allocate a "long"
 111 * (preferably unsigned long) for each cell.  In practice this is overkill;
 112 * we can get by with 16 bits per cell.  Few of the cell counts will overflow,
 113 * and clamping those that do overflow to the maximum value will give close-
 114 * enough results.  This reduces the recommended histogram size from 256Kb
 115 * to 128Kb, which is a useful savings on PC-class machines.
 116 * (In the second pass the histogram space is re-used for pixel mapping data;
 117 * in that capacity, each cell must be able to store zero to the number of
 118 * desired colors.  16 bits/cell is plenty for that too.)
 119 * Since the JPEG code is intended to run in small memory model on 80x86
 120 * machines, we can't just allocate the histogram in one chunk.  Instead
 121 * of a true 3-D array, we use a row of pointers to 2-D arrays.  Each
 122 * pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and
 123 * each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries.  Note that
 124 * on 80x86 machines, the pointer row is in near memory but the actual
 125 * arrays are in far memory (same arrangement as we use for image arrays).
 126 */
 127
 128#define MAXNUMCOLORS  (MAXJSAMPLE+1) /* maximum size of colormap */
 129
 130/* These will do the right thing for either R,G,B or B,G,R color order,
 131 * but you may not like the results for other color orders.
 132 */
 133#define HIST_C0_BITS  5		/* bits of precision in R/B histogram */
 134#define HIST_C1_BITS  6		/* bits of precision in G histogram */
 135#define HIST_C2_BITS  5		/* bits of precision in B/R histogram */
 136
 137/* Number of elements along histogram axes. */
 138#define HIST_C0_ELEMS  (1<<HIST_C0_BITS)
 139#define HIST_C1_ELEMS  (1<<HIST_C1_BITS)
 140#define HIST_C2_ELEMS  (1<<HIST_C2_BITS)
 141
 142/* These are the amounts to shift an input value to get a histogram index. */
 143#define C0_SHIFT  (BITS_IN_JSAMPLE-HIST_C0_BITS)
 144#define C1_SHIFT  (BITS_IN_JSAMPLE-HIST_C1_BITS)
 145#define C2_SHIFT  (BITS_IN_JSAMPLE-HIST_C2_BITS)
 146
 147
 148typedef UINT16 histcell;	/* histogram cell; prefer an unsigned type */
 149
 150typedef histcell FAR * histptr;	/* for pointers to histogram cells */
 151
 152typedef histcell hist1d[HIST_C2_ELEMS]; /* typedefs for the array */
 153typedef hist1d FAR * hist2d;	/* type for the 2nd-level pointers */
 154typedef hist2d * hist3d;	/* type for top-level pointer */
 155
 156
 157/* Declarations for Floyd-Steinberg dithering.
 158 *
 159 * Errors are accumulated into the array fserrors[], at a resolution of
 160 * 1/16th of a pixel count.  The error at a given pixel is propagated
 161 * to its not-yet-processed neighbors using the standard F-S fractions,
 162 *		...	(here)	7/16
 163 *		3/16	5/16	1/16
 164 * We work left-to-right on even rows, right-to-left on odd rows.
 165 *
 166 * We can get away with a single array (holding one row's worth of errors)
 167 * by using it to store the current row's errors at pixel columns not yet
 168 * processed, but the next row's errors at columns already processed.  We
 169 * need only a few extra variables to hold the errors immediately around the
 170 * current column.  (If we are lucky, those variables are in registers, but
 171 * even if not, they're probably cheaper to access than array elements are.)
 172 *
 173 * The fserrors[] array has (#columns + 2) entries; the extra entry at
 174 * each end saves us from special-casing the first and last pixels.
 175 * Each entry is three values long, one value for each color component.
 176 *
 177 * Note: on a wide image, we might not have enough room in a PC's near data
 178 * segment to hold the error array; so it is allocated with alloc_large.
 179 */
 180
 181#if BITS_IN_JSAMPLE == 8
 182typedef INT16 FSERROR;		/* 16 bits should be enough */
 183typedef int LOCFSERROR;		/* use 'int' for calculation temps */
 184#else
 185typedef INT32 FSERROR;		/* may need more than 16 bits */
 186typedef INT32 LOCFSERROR;	/* be sure calculation temps are big enough */
 187#endif
 188
 189typedef FSERROR FAR *FSERRPTR;	/* pointer to error array (in FAR storage!) */
 190
 191
 192/* Private subobject */
 193
 194typedef struct {
 195  struct jpeg_color_quantizer pub; /* public fields */
 196
 197  /* Space for the eventually created colormap is stashed here */
 198  JSAMPARRAY sv_colormap;	/* colormap allocated at init time */
 199  int desired;			/* desired # of colors = size of colormap */
 200
 201  /* Variables for accumulating image statistics */
 202  hist3d histogram;		/* pointer to the histogram */
 203
 204  boolean needs_zeroed;		/* TRUE if next pass must zero histogram */
 205
 206  /* Variables for Floyd-Steinberg dithering */
 207  FSERRPTR fserrors;		/* accumulated errors */
 208  boolean on_odd_row;		/* flag to remember which row we are on */
 209  int * error_limiter;		/* table for clamping the applied error */
 210} my_cquantizer;
 211
 212typedef my_cquantizer * my_cquantize_ptr;
 213
 214
 215/*
 216 * Prescan some rows of pixels.
 217 * In this module the prescan simply updates the histogram, which has been
 218 * initialized to zeroes by start_pass.
 219 * An output_buf parameter is required by the method signature, but no data
 220 * is actually output (in fact the buffer controller is probably passing a
 221 * NULL pointer).
 222 */
 223
 224METHODDEF(void)
 225prescan_quantize (j_decompress_ptr cinfo, JSAMPARRAY input_buf,
 226		  JSAMPARRAY output_buf, int num_rows)
 227{
 228  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
 229  register JSAMPROW ptr;
 230  register histptr histp;
 231  register hist3d histogram = cquantize->histogram;
 232  int row;
 233  JDIMENSION col;
 234  JDIMENSION width = cinfo->output_width;
 235
 236  for (row = 0; row < num_rows; row++) {
 237    ptr = input_buf[row];
 238    for (col = width; col > 0; col--) {
 239      /* get pixel value and index into the histogram */
 240      histp = & histogram[GETJSAMPLE(ptr[0]) >> C0_SHIFT]
 241			 [GETJSAMPLE(ptr[1]) >> C1_SHIFT]
 242			 [GETJSAMPLE(ptr[2]) >> C2_SHIFT];
 243      /* increment, check for overflow and undo increment if so. */
 244      if (++(*histp) <= 0)
 245	(*histp)--;
 246      ptr += 3;
 247    }
 248  }
 249}
 250
 251
 252/*
 253 * Next we have the really interesting routines: selection of a colormap
 254 * given the completed histogram.
 255 * These routines work with a list of "boxes", each representing a rectangular
 256 * subset of the input color space (to histogram precision).
 257 */
 258
 259typedef struct {
 260  /* The bounds of the box (inclusive); expressed as histogram indexes */
 261  int c0min, c0max;
 262  int c1min, c1max;
 263  int c2min, c2max;
 264  /* The volume (actually 2-norm) of the box */
 265  INT32 volume;
 266  /* The number of nonzero histogram cells within this box */
 267  long colorcount;
 268} box;
 269
 270typedef box * boxptr;
 271
 272
 273LOCAL(boxptr)
 274find_biggest_color_pop (boxptr boxlist, int numboxes)
 275/* Find the splittable box with the largest color population */
 276/* Returns NULL if no splittable boxes remain */
 277{
 278  register boxptr boxp;
 279  register int i;
 280  register long maxc = 0;
 281  boxptr which = NULL;
 282  
 283  for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
 284    if (boxp->colorcount > maxc && boxp->volume > 0) {
 285      which = boxp;
 286      maxc = boxp->colorcount;
 287    }
 288  }
 289  return which;
 290}
 291
 292
 293LOCAL(boxptr)
 294find_biggest_volume (boxptr boxlist, int numboxes)
 295/* Find the splittable box with the largest (scaled) volume */
 296/* Returns NULL if no splittable boxes remain */
 297{
 298  register boxptr boxp;
 299  register int i;
 300  register INT32 maxv = 0;
 301  boxptr which = NULL;
 302  
 303  for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
 304    if (boxp->volume > maxv) {
 305      which = boxp;
 306      maxv = boxp->volume;
 307    }
 308  }
 309  return which;
 310}
 311
 312
 313LOCAL(void)
 314update_box (j_decompress_ptr cinfo, boxptr boxp)
 315/* Shrink the min/max bounds of a box to enclose only nonzero elements, */
 316/* and recompute its volume and population */
 317{
 318  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
 319  hist3d histogram = cquantize->histogram;
 320  histptr histp;
 321  int c0,c1,c2;
 322  int c0min,c0max,c1min,c1max,c2min,c2max;
 323  INT32 dist0,dist1,dist2;
 324  long ccount;
 325  
 326  c0min = boxp->c0min;  c0max = boxp->c0max;
 327  c1min = boxp->c1min;  c1max = boxp->c1max;
 328  c2min = boxp->c2min;  c2max = boxp->c2max;
 329  
 330  if (c0max > c0min)
 331    for (c0 = c0min; c0 <= c0max; c0++)
 332      for (c1 = c1min; c1 <= c1max; c1++) {
 333	histp = & histogram[c0][c1][c2min];
 334	for (c2 = c2min; c2 <= c2max; c2++)
 335	  if (*histp++ != 0) {
 336	    boxp->c0min = c0min = c0;
 337	    goto have_c0min;
 338	  }
 339      }
 340 have_c0min:
 341  if (c0max > c0min)
 342    for (c0 = c0max; c0 >= c0min; c0--)
 343      for (c1 = c1min; c1 <= c1max; c1++) {
 344	histp = & histogram[c0][c1][c2min];
 345	for (c2 = c2min; c2 <= c2max; c2++)
 346	  if (*histp++ != 0) {
 347	    boxp->c0max = c0max = c0;
 348	    goto have_c0max;
 349	  }
 350      }
 351 have_c0max:
 352  if (c1max > c1min)
 353    for (c1 = c1min; c1 <= c1max; c1++)
 354      for (c0 = c0min; c0 <= c0max; c0++) {
 355	histp = & histogram[c0][c1][c2min];
 356	for (c2 = c2min; c2 <= c2max; c2++)
 357	  if (*histp++ != 0) {
 358	    boxp->c1min = c1min = c1;
 359	    goto have_c1min;
 360	  }
 361      }
 362 have_c1min:
 363  if (c1max > c1min)
 364    for (c1 = c1max; c1 >= c1min; c1--)
 365      for (c0 = c0min; c0 <= c0max; c0++) {
 366	histp = & histogram[c0][c1][c2min];
 367	for (c2 = c2min; c2 <= c2max; c2++)
 368	  if (*histp++ != 0) {
 369	    boxp->c1max = c1max = c1;
 370	    goto have_c1max;
 371	  }
 372      }
 373 have_c1max:
 374  if (c2max > c2min)
 375    for (c2 = c2min; c2 <= c2max; c2++)
 376      for (c0 = c0min; c0 <= c0max; c0++) {
 377	histp = & histogram[c0][c1min][c2];
 378	for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
 379	  if (*histp != 0) {
 380	    boxp->c2min = c2min = c2;
 381	    goto have_c2min;
 382	  }
 383      }
 384 have_c2min:
 385  if (c2max > c2min)
 386    for (c2 = c2max; c2 >= c2min; c2--)
 387      for (c0 = c0min; c0 <= c0max; c0++) {
 388	histp = & histogram[c0][c1min][c2];
 389	for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
 390	  if (*histp != 0) {
 391	    boxp->c2max = c2max = c2;
 392	    goto have_c2max;
 393	  }
 394      }
 395 have_c2max:
 396
 397  /* Update box volume.
 398   * We use 2-norm rather than real volume here; this biases the method
 399   * against making long narrow boxes, and it has the side benefit that
 400   * a box is splittable iff norm > 0.
 401   * Since the differences are expressed in histogram-cell units,
 402   * we have to shift back to JSAMPLE units to get consistent distances;
 403   * after which, we scale according to the selected distance scale factors.
 404   */
 405  dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE;
 406  dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE;
 407  dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE;
 408  boxp->volume = dist0*dist0 + dist1*dist1 + dist2*dist2;
 409  
 410  /* Now scan remaining volume of box and compute population */
 411  ccount = 0;
 412  for (c0 = c0min; c0 <= c0max; c0++)
 413    for (c1 = c1min; c1 <= c1max; c1++) {
 414      histp = & histogram[c0][c1][c2min];
 415      for (c2 = c2min; c2 <= c2max; c2++, histp++)
 416	if (*histp != 0) {
 417	  ccount++;
 418	}
 419    }
 420  boxp->colorcount = ccount;
 421}
 422
 423
 424LOCAL(int)
 425median_cut (j_decompress_ptr cinfo, boxptr boxlist, int numboxes,
 426	    int desired_colors)
 427/* Repeatedly select and split the largest box until we have enough boxes */
 428{
 429  int n,lb;
 430  int c0,c1,c2,cmax;
 431  register boxptr b1,b2;
 432
 433  while (numboxes < desired_colors) {
 434    /* Select box to split.
 435     * Current algorithm: by population for first half, then by volume.
 436     */
 437    if (numboxes*2 <= desired_colors) {
 438      b1 = find_biggest_color_pop(boxlist, numboxes);
 439    } else {
 440      b1 = find_biggest_volume(boxlist, numboxes);
 441    }
 442    if (b1 == NULL)		/* no splittable boxes left! */
 443      break;
 444    b2 = &boxlist[numboxes];	/* where new box will go */
 445    /* Copy the color bounds to the new box. */
 446    b2->c0max = b1->c0max; b2->c1max = b1->c1max; b2->c2max = b1->c2max;
 447    b2->c0min = b1->c0min; b2->c1min = b1->c1min; b2->c2min = b1->c2min;
 448    /* Choose which axis to split the box on.
 449     * Current algorithm: longest scaled axis.
 450     * See notes in update_box about scaling distances.
 451     */
 452    c0 = ((b1->c0max - b1->c0min) << C0_SHIFT) * C0_SCALE;
 453    c1 = ((b1->c1max - b1->c1min) << C1_SHIFT) * C1_SCALE;
 454    c2 = ((b1->c2max - b1->c2min) << C2_SHIFT) * C2_SCALE;
 455    /* We want to break any ties in favor of green, then red, blue last.
 456     * This code does the right thing for R,G,B or B,G,R color orders only.
 457     */
 458#if RGB_RED == 0
 459    cmax = c1; n = 1;
 460    if (c0 > cmax) { cmax = c0; n = 0; }
 461    if (c2 > cmax) { n = 2; }
 462#else
 463    cmax = c1; n = 1;
 464    if (c2 > cmax) { cmax = c2; n = 2; }
 465    if (c0 > cmax) { n = 0; }
 466#endif
 467    /* Choose split point along selected axis, and update box bounds.
 468     * Current algorithm: split at halfway point.
 469     * (Since the box has been shrunk to minimum volume,
 470     * any split will produce two nonempty subboxes.)
 471     * Note that lb value is max for lower box, so must be < old max.
 472     */
 473    switch (n) {
 474    case 0:
 475      lb = (b1->c0max + b1->c0min) / 2;
 476      b1->c0max = lb;
 477      b2->c0min = lb+1;
 478      break;
 479    case 1:
 480      lb = (b1->c1max + b1->c1min) / 2;
 481      b1->c1max = lb;
 482      b2->c1min = lb+1;
 483      break;
 484    case 2:
 485      lb = (b1->c2max + b1->c2min) / 2;
 486      b1->c2max = lb;
 487      b2->c2min = lb+1;
 488      break;
 489    }
 490    /* Update stats for boxes */
 491    update_box(cinfo, b1);
 492    update_box(cinfo, b2);
 493    numboxes++;
 494  }
 495  return numboxes;
 496}
 497
 498
 499LOCAL(void)
 500compute_color (j_decompress_ptr cinfo, boxptr boxp, int icolor)
 501/* Compute representative color for a box, put it in colormap[icolor] */
 502{
 503  /* Current algorithm: mean weighted by pixels (not colors) */
 504  /* Note it is important to get the rounding correct! */
 505  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
 506  hist3d histogram = cquantize->histogram;
 507  histptr histp;
 508  int c0,c1,c2;
 509  int c0min,c0max,c1min,c1max,c2min,c2max;
 510  long count;
 511  long total = 0;
 512  long c0total = 0;
 513  long c1total = 0;
 514  long c2total = 0;
 515  
 516  c0min = boxp->c0min;  c0max = boxp->c0max;
 517  c1min = boxp->c1min;  c1max = boxp->c1max;
 518  c2min = boxp->c2min;  c2max = boxp->c2max;
 519  
 520  for (c0 = c0min; c0 <= c0max; c0++)
 521    for (c1 = c1min; c1 <= c1max; c1++) {
 522      histp = & histogram[c0][c1][c2min];
 523      for (c2 = c2min; c2 <= c2max; c2++) {
 524	if ((count = *histp++) != 0) {
 525	  total += count;
 526	  c0total += ((c0 << C0_SHIFT) + ((1<<C0_SHIFT)>>1)) * count;
 527	  c1total += ((c1 << C1_SHIFT) + ((1<<C1_SHIFT)>>1)) * count;
 528	  c2total += ((c2 << C2_SHIFT) + ((1<<C2_SHIFT)>>1)) * count;
 529	}
 530      }
 531    }
 532  
 533  cinfo->colormap[0][icolor] = (JSAMPLE) ((c0total + (total>>1)) / total);
 534  cinfo->colormap[1][icolor] = (JSAMPLE) ((c1total + (total>>1)) / total);
 535  cinfo->colormap[2][icolor] = (JSAMPLE) ((c2total + (total>>1)) / total);
 536}
 537
 538
 539LOCAL(void)
 540select_colors (j_decompress_ptr cinfo, int desired_colors)
 541/* Master routine for color selection */
 542{
 543  boxptr boxlist;
 544  int numboxes;
 545  int i;
 546
 547  /* Allocate workspace for box list */
 548  boxlist = (boxptr) (*cinfo->mem->alloc_small)
 549    ((j_common_ptr) cinfo, JPOOL_IMAGE, desired_colors * SIZEOF(box));
 550  /* Initialize one box containing whole space */
 551  numboxes = 1;
 552  boxlist[0].c0min = 0;
 553  boxlist[0].c0max = MAXJSAMPLE >> C0_SHIFT;
 554  boxlist[0].c1min = 0;
 555  boxlist[0].c1max = MAXJSAMPLE >> C1_SHIFT;
 556  boxlist[0].c2min = 0;
 557  boxlist[0].c2max = MAXJSAMPLE >> C2_SHIFT;
 558  /* Shrink it to actually-used volume and set its statistics */
 559  update_box(cinfo, & boxlist[0]);
 560  /* Perform median-cut to produce final box list */
 561  numboxes = median_cut(cinfo, boxlist, numboxes, desired_colors);
 562  /* Compute the representative color for each box, fill colormap */
 563  for (i = 0; i < numboxes; i++)
 564    compute_color(cinfo, & boxlist[i], i);
 565  cinfo->actual_number_of_colors = numboxes;
 566  TRACEMS1(cinfo, 1, JTRC_QUANT_SELECTED, numboxes);
 567}
 568
 569
 570/*
 571 * These routines are concerned with the time-critical task of mapping input
 572 * colors to the nearest color in the selected colormap.
 573 *
 574 * We re-use the histogram space as an "inverse color map", essentially a
 575 * cache for the results of nearest-color searches.  All colors within a
 576 * histogram cell will be mapped to the same colormap entry, namely the one
 577 * closest to the cell's center.  This may not be quite the closest entry to
 578 * the actual input color, but it's almost as good.  A zero in the cache
 579 * indicates we haven't found the nearest color for that cell yet; the array
 580 * is cleared to zeroes before starting the mapping pass.  When we find the
 581 * nearest color for a cell, its colormap index plus one is recorded in the
 582 * cache for future use.  The pass2 scanning routines call fill_inverse_cmap
 583 * when they need to use an unfilled entry in the cache.
 584 *
 585 * Our method of efficiently finding nearest colors is based on the "locally
 586 * sorted search" idea described by Heckbert and on the incremental distance
 587 * calculation described by Spencer W. Thomas in chapter III.1 of Graphics
 588 * Gems II (James Arvo, ed.  Academic Press, 1991).  Thomas points out that
 589 * the distances from a given colormap entry to each cell of the histogram can
 590 * be computed quickly using an incremental method: the differences between
 591 * distances to adjacent cells themselves differ by a constant.  This allows a
 592 * fairly fast implementation of the "brute force" approach of computing the
 593 * distance from every colormap entry to every histogram cell.  Unfortunately,
 594 * it needs a work array to hold the best-distance-so-far for each histogram
 595 * cell (because the inner loop has to be over cells, not colormap entries).
 596 * The work array elements have to be INT32s, so the work array would need
 597 * 256Kb at our recommended precision.  This is not feasible in DOS machines.
 598 *
 599 * To get around these problems, we apply Thomas' method to compute the
 600 * nearest colors for only the cells within a small subbox of the histogram.
 601 * The work array need be only as big as the subbox, so the memory usage
 602 * problem is solved.  Furthermore, we need not fill subboxes that are never
 603 * referenced in pass2; many images use only part of the color gamut, so a
 604 * fair amount of work is saved.  An additional advantage of this
 605 * approach is that we can apply Heckbert's locality criterion to quickly
 606 * eliminate colormap entries that are far away from the subbox; typically
 607 * three-fourths of the colormap entries are rejected by Heckbert's criterion,
 608 * and we need not compute their distances to individual cells in the subbox.
 609 * The speed of this approach is heavily influenced by the subbox size: too
 610 * small means too much overhead, too big loses because Heckbert's criterion
 611 * can't eliminate as many colormap entries.  Empirically the best subbox
 612 * size seems to be about 1/512th of the histogram (1/8th in each direction).
 613 *
 614 * Thomas' article also describes a refined method which is asymptotically
 615 * faster than the brute-force method, but it is also far more complex and
 616 * cannot efficiently be applied to small subboxes.  It is therefore not
 617 * useful for programs intended to be portable to DOS machines.  On machines
 618 * with plenty of memory, filling the whole histogram in one shot with Thomas'
 619 * refined method might be faster than the present code --- but then again,
 620 * it might not be any faster, and it's certainly more complicated.
 621 */
 622
 623
 624/* log2(histogram cells in update box) for each axis; this can be adjusted */
 625#define BOX_C0_LOG  (HIST_C0_BITS-3)
 626#define BOX_C1_LOG  (HIST_C1_BITS-3)
 627#define BOX_C2_LOG  (HIST_C2_BITS-3)
 628
 629#define BOX_C0_ELEMS  (1<<BOX_C0_LOG) /* # of hist cells in update box */
 630#define BOX_C1_ELEMS  (1<<BOX_C1_LOG)
 631#define BOX_C2_ELEMS  (1<<BOX_C2_LOG)
 632
 633#define BOX_C0_SHIFT  (C0_SHIFT + BOX_C0_LOG)
 634#define BOX_C1_SHIFT  (C1_SHIFT + BOX_C1_LOG)
 635#define BOX_C2_SHIFT  (C2_SHIFT + BOX_C2_LOG)
 636
 637
 638/*
 639 * The next three routines implement inverse colormap filling.  They could
 640 * all be folded into one big routine, but splitting them up this way saves
 641 * some stack space (the mindist[] and bestdist[] arrays need not coexist)
 642 * and may allow some compilers to produce better code by registerizing more
 643 * inner-loop variables.
 644 */
 645
 646LOCAL(int)
 647find_nearby_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
 648		    JSAMPLE colorlist[])
 649/* Locate the colormap entries close enough to an update box to be candidates
 650 * for the nearest entry to some cell(s) in the update box.  The update box
 651 * is specified by the center coordinates of its first cell.  The number of
 652 * candidate colormap entries is returned, and their colormap indexes are
 653 * placed in colorlist[].
 654 * This routine uses Heckbert's "locally sorted search" criterion to select
 655 * the colors that need further consideration.
 656 */
 657{
 658  int numcolors = cinfo->actual_number_of_colors;
 659  int maxc0, maxc1, maxc2;
 660  int centerc0, centerc1, centerc2;
 661  int i, x, ncolors;
 662  INT32 minmaxdist, min_dist, max_dist, tdist;
 663  INT32 mindist[MAXNUMCOLORS];	/* min distance to colormap entry i */
 664
 665  /* Compute true coordinates of update box's upper corner and center.
 666   * Actually we compute the coordinates of the center of the upper-corner
 667   * histogram cell, which are the upper bounds of the volume we care about.
 668   * Note that since ">>" rounds down, the "center" values may be closer to
 669   * min than to max; hence comparisons to them must be "<=", not "<".
 670   */
 671  maxc0 = minc0 + ((1 << BOX_C0_SHIFT) - (1 << C0_SHIFT));
 672  centerc0 = (minc0 + maxc0) >> 1;
 673  maxc1 = minc1 + ((1 << BOX_C1_SHIFT) - (1 << C1_SHIFT));
 674  centerc1 = (minc1 + maxc1) >> 1;
 675  maxc2 = minc2 + ((1 << BOX_C2_SHIFT) - (1 << C2_SHIFT));
 676  centerc2 = (minc2 + maxc2) >> 1;
 677
 678  /* For each color in colormap, find:
 679   *  1. its minimum squared-distance to any point in the update box
 680   *     (zero if color is within update box);
 681   *  2. its maximum squared-distance to any point in the update box.
 682   * Both of these can be found by considering only the corners of the box.
 683   * We save the minimum distance for each color in mindist[];
 684   * only the smallest maximum distance is of interest.
 685   */
 686  minmaxdist = 0x7FFFFFFFL;
 687
 688  for (i = 0; i < numcolors; i++) {
 689    /* We compute the squared-c0-distance term, then add in the other two. */
 690    x = GETJSAMPLE(cinfo->colormap[0][i]);
 691    if (x < minc0) {
 692      tdist = (x - minc0) * C0_SCALE;
 693      min_dist = tdist*tdist;
 694      tdist = (x - maxc0) * C0_SCALE;
 695      max_dist = tdist*tdist;
 696    } else if (x > maxc0) {
 697      tdist = (x - maxc0) * C0_SCALE;
 698      min_dist = tdist*tdist;
 699      tdist = (x - minc0) * C0_SCALE;
 700      max_dist = tdist*tdist;
 701    } else {
 702      /* within cell range so no contribution to min_dist */
 703      min_dist = 0;
 704      if (x <= centerc0) {
 705	tdist = (x - maxc0) * C0_SCALE;
 706	max_dist = tdist*tdist;
 707      } else {
 708	tdist = (x - minc0) * C0_SCALE;
 709	max_dist = tdist*tdist;
 710      }
 711    }
 712
 713    x = GETJSAMPLE(cinfo->colormap[1][i]);
 714    if (x < minc1) {
 715      tdist = (x - minc1) * C1_SCALE;
 716      min_dist += tdist*tdist;
 717      tdist = (x - maxc1) * C1_SCALE;
 718      max_dist += tdist*tdist;
 719    } else if (x > maxc1) {
 720      tdist = (x - maxc1) * C1_SCALE;
 721      min_dist += tdist*tdist;
 722      tdist = (x - minc1) * C1_SCALE;
 723      max_dist += tdist*tdist;
 724    } else {
 725      /* within cell range so no contribution to min_dist */
 726      if (x <= centerc1) {
 727	tdist = (x - maxc1) * C1_SCALE;
 728	max_dist += tdist*tdist;
 729      } else {
 730	tdist = (x - minc1) * C1_SCALE;
 731	max_dist += tdist*tdist;
 732      }
 733    }
 734
 735    x = GETJSAMPLE(cinfo->colormap[2][i]);
 736    if (x < minc2) {
 737      tdist = (x - minc2) * C2_SCALE;
 738      min_dist += tdist*tdist;
 739      tdist = (x - maxc2) * C2_SCALE;
 740      max_dist += tdist*tdist;
 741    } else if (x > maxc2) {
 742      tdist = (x - maxc2) * C2_SCALE;
 743      min_dist += tdist*tdist;
 744      tdist = (x - minc2) * C2_SCALE;
 745      max_dist += tdist*tdist;
 746    } else {
 747      /* within cell range so no contribution to min_dist */
 748      if (x <= centerc2) {
 749	tdist = (x - maxc2) * C2_SCALE;
 750	max_dist += tdist*tdist;
 751      } else {
 752	tdist = (x - minc2) * C2_SCALE;
 753	max_dist += tdist*tdist;
 754      }
 755    }
 756
 757    mindist[i] = min_dist;	/* save away the results */
 758    if (max_dist < minmaxdist)
 759      minmaxdist = max_dist;
 760  }
 761
 762  /* Now we know that no cell in the update box is more than minmaxdist
 763   * away from some colormap entry.  Therefore, only colors that are
 764   * within minmaxdist of some part of the box need be considered.
 765   */
 766  ncolors = 0;
 767  for (i = 0; i < numcolors; i++) {
 768    if (mindist[i] <= minmaxdist)
 769      colorlist[ncolors++] = (JSAMPLE) i;
 770  }
 771  return ncolors;
 772}
 773
 774
 775LOCAL(void)
 776find_best_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
 777		  int numcolors, JSAMPLE colorlist[], JSAMPLE bestcolor[])
 778/* Find the closest colormap entry for each cell in the update box,
 779 * given the list of candidate colors prepared by find_nearby_colors.
 780 * Return the indexes of the closest entries in the bestcolor[] array.
 781 * This routine uses Thomas' incremental distance calculation method to
 782 * find the distance from a colormap entry to successive cells in the box.
 783 */
 784{
 785  int ic0, ic1, ic2;
 786  int i, icolor;
 787  register INT32 * bptr;	/* pointer into bestdist[] array */
 788  JSAMPLE * cptr;		/* pointer into bestcolor[] array */
 789  INT32 dist0, dist1;		/* initial distance values */
 790  register INT32 dist2;		/* current distance in inner loop */
 791  INT32 xx0, xx1;		/* distance increments */
 792  register INT32 xx2;
 793  INT32 inc0, inc1, inc2;	/* initial values for increments */
 794  /* This array holds the distance to the nearest-so-far color for each cell */
 795  INT32 bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
 796
 797  /* Initialize best-distance for each cell of the update box */
 798  bptr = bestdist;
 799  for (i = BOX_C0_ELEMS*BOX_C1_ELEMS*BOX_C2_ELEMS-1; i >= 0; i--)
 800    *bptr++ = 0x7FFFFFFFL;
 801  
 802  /* For each color selected by find_nearby_colors,
 803   * compute its distance to the center of each cell in the box.
 804   * If that's less than best-so-far, update best distance and color number.
 805   */
 806  
 807  /* Nominal steps between cell centers ("x" in Thomas article) */
 808#define STEP_C0  ((1 << C0_SHIFT) * C0_SCALE)
 809#define STEP_C1  ((1 << C1_SHIFT) * C1_SCALE)
 810#define STEP_C2  ((1 << C2_SHIFT) * C2_SCALE)
 811  
 812  for (i = 0; i < numcolors; i++) {
 813    icolor = GETJSAMPLE(colorlist[i]);
 814    /* Compute (square of) distance from minc0/c1/c2 to this color */
 815    inc0 = (minc0 - GETJSAMPLE(cinfo->colormap[0][icolor])) * C0_SCALE;
 816    dist0 = inc0*inc0;
 817    inc1 = (minc1 - GETJSAMPLE(cinfo->colormap[1][icolor])) * C1_SCALE;
 818    dist0 += inc1*inc1;
 819    inc2 = (minc2 - GETJSAMPLE(cinfo->colormap[2][icolor])) * C2_SCALE;
 820    dist0 += inc2*inc2;
 821    /* Form the initial difference increments */
 822    inc0 = inc0 * (2 * STEP_C0) + STEP_C0 * STEP_C0;
 823    inc1 = inc1 * (2 * STEP_C1) + STEP_C1 * STEP_C1;
 824    inc2 = inc2 * (2 * STEP_C2) + STEP_C2 * STEP_C2;
 825    /* Now loop over all cells in box, updating distance per Thomas method */
 826    bptr = bestdist;
 827    cptr = bestcolor;
 828    xx0 = inc0;
 829    for (ic0 = BOX_C0_ELEMS-1; ic0 >= 0; ic0--) {
 830      dist1 = dist0;
 831      xx1 = inc1;
 832      for (ic1 = BOX_C1_ELEMS-1; ic1 >= 0; ic1--) {
 833	dist2 = dist1;
 834	xx2 = inc2;
 835	for (ic2 = BOX_C2_ELEMS-1; ic2 >= 0; ic2--) {
 836	  if (dist2 < *bptr) {
 837	    *bptr = dist2;
 838	    *cptr = (JSAMPLE) icolor;
 839	  }
 840	  dist2 += xx2;
 841	  xx2 += 2 * STEP_C2 * STEP_C2;
 842	  bptr++;
 843	  cptr++;
 844	}
 845	dist1 += xx1;
 846	xx1 += 2 * STEP_C1 * STEP_C1;
 847      }
 848      dist0 += xx0;
 849      xx0 += 2 * STEP_C0 * STEP_C0;
 850    }
 851  }
 852}
 853
 854
 855LOCAL(void)
 856fill_inverse_cmap (j_decompress_ptr cinfo, int c0, int c1, int c2)
 857/* Fill the inverse-colormap entries in the update box that contains */
 858/* histogram cell c0/c1/c2.  (Only that one cell MUST be filled, but */
 859/* we can fill as many others as we wish.) */
 860{
 861  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
 862  hist3d histogram = cquantize->histogram;
 863  int minc0, minc1, minc2;	/* lower left corner of update box */
 864  int ic0, ic1, ic2;
 865  register JSAMPLE * cptr;	/* pointer into bestcolor[] array */
 866  register histptr cachep;	/* pointer into main cache array */
 867  /* This array lists the candidate colormap indexes. */
 868  JSAMPLE colorlist[MAXNUMCOLORS];
 869  int numcolors;		/* number of candidate colors */
 870  /* This array holds the actually closest colormap index for each cell. */
 871  JSAMPLE bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
 872
 873  /* Convert cell coordinates to update box ID */
 874  c0 >>= BOX_C0_LOG;
 875  c1 >>= BOX_C1_LOG;
 876  c2 >>= BOX_C2_LOG;
 877
 878  /* Compute true coordinates of update box's origin corner.
 879   * Actually we compute the coordinates of the center of the corner
 880   * histogram cell, which are the lower bounds of the volume we care about.
 881   */
 882  minc0 = (c0 << BOX_C0_SHIFT) + ((1 << C0_SHIFT) >> 1);
 883  minc1 = (c1 << BOX_C1_SHIFT) + ((1 << C1_SHIFT) >> 1);
 884  minc2 = (c2 << BOX_C2_SHIFT) + ((1 << C2_SHIFT) >> 1);
 885  
 886  /* Determine which colormap entries are close enough to be candidates
 887   * for the nearest entry to some cell in the update box.
 888   */
 889  numcolors = find_nearby_colors(cinfo, minc0, minc1, minc2, colorlist);
 890
 891  /* Determine the actually nearest colors. */
 892  find_best_colors(cinfo, minc0, minc1, minc2, numcolors, colorlist,
 893		   bestcolor);
 894
 895  /* Save the best color numbers (plus 1) in the main cache array */
 896  c0 <<= BOX_C0_LOG;		/* convert ID back to base cell indexes */
 897  c1 <<= BOX_C1_LOG;
 898  c2 <<= BOX_C2_LOG;
 899  cptr = bestcolor;
 900  for (ic0 = 0; ic0 < BOX_C0_ELEMS; ic0++) {
 901    for (ic1 = 0; ic1 < BOX_C1_ELEMS; ic1++) {
 902      cachep = & histogram[c0+ic0][c1+ic1][c2];
 903      for (ic2 = 0; ic2 < BOX_C2_ELEMS; ic2++) {
 904	*cachep++ = (histcell) (GETJSAMPLE(*cptr++) + 1);
 905      }
 906    }
 907  }
 908}
 909
 910
 911/*
 912 * Map some rows of pixels to the output colormapped representation.
 913 */
 914
 915METHODDEF(void)
 916pass2_no_dither (j_decompress_ptr cinfo,
 917		 JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)
 918/* This version performs no dithering */
 919{
 920  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
 921  hist3d histogram = cquantize->histogram;
 922  register JSAMPROW inptr, outptr;
 923  register histptr cachep;
 924  register int c0, c1, c2;
 925  int row;
 926  JDIMENSION col;
 927  JDIMENSION width = cinfo->output_width;
 928
 929  for (row = 0; row < num_rows; row++) {
 930    inptr = input_buf[row];
 931    outptr = output_buf[row];
 932    for (col = width; col > 0; col--) {
 933      /* get pixel value and index into the cache */
 934      c0 = GETJSAMPLE(*inptr++) >> C0_SHIFT;
 935      c1 = GETJSAMPLE(*inptr++) >> C1_SHIFT;
 936      c2 = GETJSAMPLE(*inptr++) >> C2_SHIFT;
 937      cachep = & histogram[c0][c1][c2];
 938      /* If we have not seen this color before, find nearest colormap entry */
 939      /* and update the cache */
 940      if (*cachep == 0)
 941	fill_inverse_cmap(cinfo, c0,c1,c2);
 942      /* Now emit the colormap index for this cell */
 943      *outptr++ = (JSAMPLE) (*cachep - 1);
 944    }
 945  }
 946}
 947
 948
 949METHODDEF(void)
 950pass2_fs_dither (j_decompress_ptr cinfo,
 951		 JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)
 952/* This version performs Floyd-Steinberg dithering */
 953{
 954  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
 955  hist3d histogram = cquantize->histogram;
 956  register LOCFSERROR cur0, cur1, cur2;	/* current error or pixel value */
 957  LOCFSERROR belowerr0, belowerr1, belowerr2; /* error for pixel below cur */
 958  LOCFSERROR bpreverr0, bpreverr1, bpreverr2; /* error for below/prev col */
 959  register FSERRPTR errorptr;	/* => fserrors[] at column before current */
 960  JSAMPROW inptr;		/* => current input pixel */
 961  JSAMPROW outptr;		/* => current output pixel */
 962  histptr cachep;
 963  int dir;			/* +1 or -1 depending on direction */
 964  int dir3;			/* 3*dir, for advancing inptr & errorptr */
 965  int row;
 966  JDIMENSION col;
 967  JDIMENSION width = cinfo->output_width;
 968  JSAMPLE *range_limit = cinfo->sample_range_limit;
 969  int *error_limit = cquantize->error_limiter;
 970  JSAMPROW colormap0 = cinfo->colormap[0];
 971  JSAMPROW colormap1 = cinfo->colormap[1];
 972  JSAMPROW colormap2 = cinfo->colormap[2];
 973  SHIFT_TEMPS
 974
 975  for (row = 0; row < num_rows; row++) {
 976    inptr = input_buf[row];
 977    outptr = output_buf[row];
 978    if (cquantize->on_odd_row) {
 979      /* work right to left in this row */
 980      inptr += (width-1) * 3;	/* so point to rightmost pixel */
 981      outptr += width-1;
 982      dir = -1;
 983      dir3 = -3;
 984      errorptr = cquantize->fserrors + (width+1)*3; /* => entry after last column */
 985      cquantize->on_odd_row = FALSE; /* flip for next time */
 986    } else {
 987      /* work left to right in this row */
 988      dir = 1;
 989      dir3 = 3;
 990      errorptr = cquantize->fserrors; /* => entry before first real column */
 991      cquantize->on_odd_row = TRUE; /* flip for next time */
 992    }
 993    /* Preset error values: no error propagated to first pixel from left */
 994    cur0 = cur1 = cur2 = 0;
 995    /* and no error propagated to row below yet */
 996    belowerr0 = belowerr1 = belowerr2 = 0;
 997    bpreverr0 = bpreverr1 = bpreverr2 = 0;
 998
 999    for (col = width; col > 0; col--) {
1000      /* curN holds the error propagated from the previous pixel on the
1001       * current line.  Add the error propagated from the previous line
1002       * to form the complete error correction term for this pixel, and
1003       * round the error term (which is expressed * 16) to an integer.
1004       * RIGHT_SHIFT rounds towards minus infinity, so adding 8 is correct
1005       * for either sign of the error value.
1006       * Note: errorptr points to *previous* column's array entry.
1007       */
1008      cur0 = RIGHT_SHIFT(cur0 + errorptr[dir3+0] + 8, 4);
1009      cur1 = RIGHT_SHIFT(cur1 + errorptr[dir3+1] + 8, 4);
1010      cur2 = RIGHT_SHIFT(cur2 + errorptr[dir3+2] + 8, 4);
1011      /* Limit the error using transfer function set by init_error_limit.
1012       * See comments with init_error_limit for rationale.
1013       */
1014      cur0 = error_limit[cur0];
1015      cur1 = error_limit[cur1];
1016      cur2 = error_limit[cur2];
1017      /* Form pixel value + error, and range-limit to 0..MAXJSAMPLE.
1018       * The maximum error is +- MAXJSAMPLE (or less with error limiting);
1019       * this sets the required size of the range_limit array.
1020       */
1021      cur0 += GETJSAMPLE(inptr[0]);
1022      cur1 += GETJSAMPLE(inptr[1]);
1023      cur2 += GETJSAMPLE(inptr[2]);
1024      cur0 = GETJSAMPLE(range_limit[cur0]);
1025      cur1 = GETJSAMPLE(range_limit[cur1]);
1026      cur2 = GETJSAMPLE(range_limit[cur2]);
1027      /* Index into the cache with adjusted pixel value */
1028      cachep = & histogram[cur0>>C0_SHIFT][cur1>>C1_SHIFT][cur2>>C2_SHIFT];
1029      /* If we have not seen this color before, find nearest colormap */
1030      /* entry and update the cache */
1031      if (*cachep == 0)
1032	fill_inverse_cmap(cinfo, cur0>>C0_SHIFT,cur1>>C1_SHIFT,cur2>>C2_SHIFT);
1033      /* Now emit the colormap index for this cell */
1034      { register int pixcode = *cachep - 1;
1035	*outptr = (JSAMPLE) pixcode;
1036	/* Compute representation error for this pixel */
1037	cur0 -= GETJSAMPLE(colormap0[pixcode]);
1038	cur1 -= GETJSAMPLE(colormap1[pixcode]);
1039	cur2 -= GETJSAMPLE(colormap2[pixcode]);
1040      }
1041      /* Compute error fractions to be propagated to adjacent pixels.
1042       * Add these into the running sums, and simultaneously shift the
1043       * next-line error sums left by 1 column.
1044       */
1045      { register LOCFSERROR bnexterr, delta;
1046
1047	bnexterr = cur0;	/* Process component 0 */
1048	delta = cur0 * 2;
1049	cur0 += delta;		/* form error * 3 */
1050	errorptr[0] = (FSERROR) (bpreverr0 + cur0);
1051	cur0 += delta;		/* form error * 5 */
1052	bpreverr0 = belowerr0 + cur0;
1053	belowerr0 = bnexterr;
1054	cur0 += delta;		/* form error * 7 */
1055	bnexterr = cur1;	/* Process component 1 */
1056	delta = cur1 * 2;
1057	cur1 += delta;		/* form error * 3 */
1058	errorptr[1] = (FSERROR) (bpreverr1 + cur1);
1059	cur1 += delta;		/* form error * 5 */
1060	bpreverr1 = belowerr1 + cur1;
1061	belowerr1 = bnexterr;
1062	cur1 += delta;		/* form error * 7 */
1063	bnexterr = cur2;	/* Process component 2 */
1064	delta = cur2 * 2;
1065	cur2 += delta;		/* form error * 3 */
1066	errorptr[2] = (FSERROR) (bpreverr2 + cur2);
1067	cur2 += delta;		/* form error * 5 */
1068	bpreverr2 = belowerr2 + cur2;
1069	belowerr2 = bnexterr;
1070	cur2 += delta;		/* form error * 7 */
1071      }
1072      /* At this point curN contains the 7/16 error value to be propagated
1073       * to the next pixel on the current line, and all the errors for the
1074       * next line have been shifted over.  We are therefore ready to move on.
1075       */
1076      inptr += dir3;		/* Advance pixel pointers to next column */
1077      outptr += dir;
1078      errorptr += dir3;		/* advance errorptr to current column */
1079    }
1080    /* Post-loop cleanup: we must unload the final error values into the
1081     * final fserrors[] entry.  Note we need not unload belowerrN because
1082     * it is for the dummy column before or after the actual array.
1083     */
1084    errorptr[0] = (FSERROR) bpreverr0; /* unload prev errs into array */
1085    errorptr[1] = (FSERROR) bpreverr1;
1086    errorptr[2] = (FSERROR) bpreverr2;
1087  }
1088}
1089
1090
1091/*
1092 * Initialize the error-limiting transfer function (lookup table).
1093 * The raw F-S error computation can potentially compute error values of up to
1094 * +- MAXJSAMPLE.  But we want the maximum correction applied to a pixel to be
1095 * much less, otherwise obviously wrong pixels will be created.  (Typical
1096 * effects include weird fringes at color-area boundaries, isolated bright
1097 * pixels in a dark area, etc.)  The standard advice for avoiding this problem
1098 * is to ensure that the "corners" of the color cube are allocated as output
1099 * colors; then repeated errors in the same direction cannot cause cascading
1100 * error buildup.  However, that only prevents the error from getting
1101 * completely out of hand; Aaron Giles reports that error limiting improves
1102 * the results even with corner colors allocated.
1103 * A simple clamping of the error values to about +- MAXJSAMPLE/8 works pretty
1104 * well, but the smoother transfer function used below is even better.  Thanks
1105 * to Aaron Giles for this idea.
1106 */
1107
1108LOCAL(void)
1109init_error_limit (j_decompress_ptr cinfo)
1110/* Allocate and fill in the error_limiter table */
1111{
1112  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1113  int * table;
1114  int in, out;
1115
1116  table = (int *) (*cinfo->mem->alloc_small)
1117    ((j_common_ptr) cinfo, JPOOL_IMAGE, (MAXJSAMPLE*2+1) * SIZEOF(int));
1118  table += MAXJSAMPLE;		/* so can index -MAXJSAMPLE .. +MAXJSAMPLE */
1119  cquantize->error_limiter = table;
1120
1121#define STEPSIZE ((MAXJSAMPLE+1)/16)
1122  /* Map errors 1:1 up to +- MAXJSAMPLE/16 */
1123  out = 0;
1124  for (in = 0; in < STEPSIZE; in++, out++) {
1125    table[in] = out; table[-in] = -out;
1126  }
1127  /* Map errors 1:2 up to +- 3*MAXJSAMPLE/16 */
1128  for (; in < STEPSIZE*3; in++, out += (in&1) ? 0 : 1) {
1129    table[in] = out; table[-in] = -out;
1130  }
1131  /* Clamp the rest to final out value (which is (MAXJSAMPLE+1)/8) */
1132  for (; in <= MAXJSAMPLE; in++) {
1133    table[in] = out; table[-in] = -out;
1134  }
1135#undef STEPSIZE
1136}
1137
1138
1139/*
1140 * Finish up at the end of each pass.
1141 */
1142
1143METHODDEF(void)
1144finish_pass1 (j_decompress_ptr cinfo)
1145{
1146  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1147
1148  /* Select the representative colors and fill in cinfo->colormap */
1149  cinfo->colormap = cquantize->sv_colormap;
1150  select_colors(cinfo, cquantize->desired);
1151  /* Force next pass to zero the color index table */
1152  cquantize->needs_zeroed = TRUE;
1153}
1154
1155
1156METHODDEF(void)
1157finish_pass2 (j_decompress_ptr cinfo)
1158{
1159  /* no work */
1160}
1161
1162
1163/*
1164 * Initialize for each processing pass.
1165 */
1166
1167METHODDEF(void)
1168start_pass_2_quant (j_decompress_ptr cinfo, boolean is_pre_scan)
1169{
1170  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1171  hist3d histogram = cquantize->histogram;
1172  int i;
1173
1174  /* Only F-S dithering or no dithering is supported. */
1175  /* If user asks for ordered dither, give him F-S. */
1176  if (cinfo->dither_mode != JDITHER_NONE)
1177    cinfo->dither_mode = JDITHER_FS;
1178
1179  if (is_pre_scan) {
1180    /* Set up method pointers */
1181    cquantize->pub.color_quantize = prescan_quantize;
1182    cquantize->pub.finish_pass = finish_pass1;
1183    cquantize->needs_zeroed = TRUE; /* Always zero histogram */
1184  } else {
1185    /* Set up method pointers */
1186    if (cinfo->dither_mode == JDITHER_FS)
1187      cquantize->pub.color_quantize = pass2_fs_dither;
1188    else
1189      cquantize->pub.color_quantize = pass2_no_dither;
1190    cquantize->pub.finish_pass = finish_pass2;
1191
1192    /* Make sure color count is acceptable */
1193    i = cinfo->actual_number_of_colors;
1194    if (i < 1)
1195      ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 1);
1196    if (i > MAXNUMCOLORS)
1197      ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
1198
1199    if (cinfo->dither_mode == JDITHER_FS) {
1200      size_t arraysize = (size_t) ((cinfo->output_width + 2) *
1201				   (3 * SIZEOF(FSERROR)));
1202      /* Allocate Floyd-Steinberg workspace if we didn't already. */
1203      if (cquantize->fserrors == NULL)
1204	cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)
1205	  ((j_common_ptr) cinfo, JPOOL_IMAGE, arraysize);
1206      /* Initialize the propagated errors to zero. */
1207      FMEMZERO((void FAR *) cquantize->fserrors, arraysize);
1208      /* Make the error-limit table if we didn't already. */
1209      if (cquantize->error_limiter == NULL)
1210	init_error_limit(cinfo);
1211      cquantize->on_odd_row = FALSE;
1212    }
1213
1214  }
1215  /* Zero the histogram or inverse color map, if necessary */
1216  if (cquantize->needs_zeroed) {
1217    for (i = 0; i < HIST_C0_ELEMS; i++) {
1218      FMEMZERO((void FAR *) histogram[i],
1219	       HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));
1220    }
1221    cquantize->needs_zeroed = FALSE;
1222  }
1223}
1224
1225
1226/*
1227 * Switch to a new external colormap between output passes.
1228 */
1229
1230METHODDEF(void)
1231new_color_map_2_quant (j_decompress_ptr cinfo)
1232{
1233  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1234
1235  /* Reset the inverse color map */
1236  cquantize->needs_zeroed = TRUE;
1237}
1238
1239
1240/*
1241 * Module initialization routine for 2-pass color quantization.
1242 */
1243
1244GLOBAL(void)
1245jinit_2pass_quantizer (j_decompress_ptr cinfo)
1246{
1247  my_cquantize_ptr cquantize;
1248  int i;
1249
1250  cquantize = (my_cquantize_ptr)
1251    (*cinfo->mem->alloc_small) ((j_common_ptr) cinfo, JPOOL_IMAGE,
1252				SIZEOF(my_cquantizer));
1253  cinfo->cquantize = (struct jpeg_color_quantizer *) cquantize;
1254  cquantize->pub.start_pass = start_pass_2_quant;
1255  cquantize->pub.new_color_map = new_color_map_2_quant;
1256  cquantize->fserrors = NULL;	/* flag optional arrays not allocated */
1257  cquantize->error_limiter = NULL;
1258
1259  /* Make sure jdmaster didn't give me a case I can't handle */
1260  if (cinfo->out_color_components != 3)
1261    ERREXIT(cinfo, JERR_NOTIMPL);
1262
1263  /* Allocate the histogram/inverse colormap storage */
1264  cquantize->histogram = (hist3d) (*cinfo->mem->alloc_small)
1265    ((j_common_ptr) cinfo, JPOOL_IMAGE, HIST_C0_ELEMS * SIZEOF(hist2d));
1266  for (i = 0; i < HIST_C0_ELEMS; i++) {
1267    cquantize->histogram[i] = (hist2d) (*cinfo->mem->alloc_large)
1268      ((j_common_ptr) cinfo, JPOOL_IMAGE,
1269       HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));
1270  }
1271  cquantize->needs_zeroed = TRUE; /* histogram is garbage now */
1272
1273  /* Allocate storage for the completed colormap, if required.
1274   * We do this now since it is FAR storage and may affect
1275   * the memory manager's space calculations.
1276   */
1277  if (cinfo->enable_2pass_quant) {
1278    /* Make sure color count is acceptable */
1279    int desired = cinfo->desired_number_of_colors;
1280    /* Lower bound on # of colors ... somewhat arbitrary as long as > 0 */
1281    if (desired < 8)
1282      ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 8);
1283    /* Make sure colormap indexes can be represented by JSAMPLEs */
1284    if (desired > MAXNUMCOLORS)
1285      ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
1286    cquantize->sv_colormap = (*cinfo->mem->alloc_sarray)
1287      ((j_common_ptr) cinfo,JPOOL_IMAGE, (JDIMENSION) desired, (JDIMENSION) 3);
1288    cquantize->desired = desired;
1289  } else
1290    cquantize->sv_colormap = NULL;
1291
1292  /* Only F-S dithering or no dithering is supported. */
1293  /* If user asks for ordered dither, give him F-S. */
1294  if (cinfo->dither_mode != JDITHER_NONE)
1295    cinfo->dither_mode = JDITHER_FS;
1296
1297  /* Allocate Floyd-Steinberg workspace if necessary.
1298   * This isn't really needed until pass 2, but again it is FAR storage.
1299   * Although we will cope with a later change in dither_mode,
1300   * we do not promise to honor max_memory_to_use if dither_mode changes.
1301   */
1302  if (cinfo->dither_mode == JDITHER_FS) {
1303    cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)
1304      ((j_common_ptr) cinfo, JPOOL_IMAGE,
1305       (size_t) ((cinfo->output_width + 2) * (3 * SIZEOF(FSERROR))));
1306    /* Might as well create the error-limiting table too. */
1307    init_error_limit(cinfo);
1308  }
1309}
1310
1311#endif /* QUANT_2PASS_SUPPORTED */