jquant2.c
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1 /*
2  * jquant2.c
3  *
4  * Copyright (C) 1991-1996, Thomas G. Lane.
5  * This file is part of the Independent JPEG Group's software.
6  * For conditions of distribution and use, see the accompanying README file.
7  *
8  * This file contains 2-pass color quantization (color mapping) routines.
9  * These routines provide selection of a custom color map for an image,
10  * followed by mapping of the image to that color map, with optional
11  * Floyd-Steinberg dithering.
12  * It is also possible to use just the second pass to map to an arbitrary
13  * externally-given color map.
14  *
15  * Note: ordered dithering is not supported, since there isn't any fast
16  * way to compute intercolor distances; it's unclear that ordered dither's
17  * fundamental assumptions even hold with an irregularly spaced color map.
18  */
19 
20 #define JPEG_INTERNALS
21 #include "jinclude.h"
22 #include "jpeglib.h"
23 
24 #ifdef QUANT_2PASS_SUPPORTED
25 
26 
27 /*
28  * This module implements the well-known Heckbert paradigm for color
29  * quantization. Most of the ideas used here can be traced back to
30  * Heckbert's seminal paper
31  * Heckbert, Paul. "Color Image Quantization for Frame Buffer Display",
32  * Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304.
33  *
34  * In the first pass over the image, we accumulate a histogram showing the
35  * usage count of each possible color. To keep the histogram to a reasonable
36  * size, we reduce the precision of the input; typical practice is to retain
37  * 5 or 6 bits per color, so that 8 or 4 different input values are counted
38  * in the same histogram cell.
39  *
40  * Next, the color-selection step begins with a box representing the whole
41  * color space, and repeatedly splits the "largest" remaining box until we
42  * have as many boxes as desired colors. Then the mean color in each
43  * remaining box becomes one of the possible output colors.
44  *
45  * The second pass over the image maps each input pixel to the closest output
46  * color (optionally after applying a Floyd-Steinberg dithering correction).
47  * This mapping is logically trivial, but making it go fast enough requires
48  * considerable care.
49  *
50  * Heckbert-style quantizers vary a good deal in their policies for choosing
51  * the "largest" box and deciding where to cut it. The particular policies
52  * used here have proved out well in experimental comparisons, but better ones
53  * may yet be found.
54  *
55  * In earlier versions of the IJG code, this module quantized in YCbCr color
56  * space, processing the raw upsampled data without a color conversion step.
57  * This allowed the color conversion math to be done only once per colormap
58  * entry, not once per pixel. However, that optimization precluded other
59  * useful optimizations (such as merging color conversion with upsampling)
60  * and it also interfered with desired capabilities such as quantizing to an
61  * externally-supplied colormap. We have therefore abandoned that approach.
62  * The present code works in the post-conversion color space, typically RGB.
63  *
64  * To improve the visual quality of the results, we actually work in scaled
65  * RGB space, giving G distances more weight than R, and R in turn more than
66  * B. To do everything in integer math, we must use integer scale factors.
67  * The 2/3/1 scale factors used here correspond loosely to the relative
68  * weights of the colors in the NTSC grayscale equation.
69  * If you want to use this code to quantize a non-RGB color space, you'll
70  * probably need to change these scale factors.
71  */
72 
73 #define R_SCALE 2 /* scale R distances by this much */
74 #define G_SCALE 3 /* scale G distances by this much */
75 #define B_SCALE 1 /* and B by this much */
76 
77 /* Relabel R/G/B as components 0/1/2, respecting the RGB ordering defined
78  * in jmorecfg.h. As the code stands, it will do the right thing for R,G,B
79  * and B,G,R orders. If you define some other weird order in jmorecfg.h,
80  * you'll get compile errors until you extend this logic. In that case
81  * you'll probably want to tweak the histogram sizes too.
82  */
83 
84 #if RGB_RED == 0
85 #define C0_SCALE R_SCALE
86 #endif
87 #if RGB_BLUE == 0
88 #define C0_SCALE B_SCALE
89 #endif
90 #if RGB_GREEN == 1
91 #define C1_SCALE G_SCALE
92 #endif
93 #if RGB_RED == 2
94 #define C2_SCALE R_SCALE
95 #endif
96 #if RGB_BLUE == 2
97 #define C2_SCALE B_SCALE
98 #endif
99 
100 
101 /*
102  * First we have the histogram data structure and routines for creating it.
103  *
104  * The number of bits of precision can be adjusted by changing these symbols.
105  * We recommend keeping 6 bits for G and 5 each for R and B.
106  * If you have plenty of memory and cycles, 6 bits all around gives marginally
107  * better results; if you are short of memory, 5 bits all around will save
108  * some space but degrade the results.
109  * To maintain a fully accurate histogram, we'd need to allocate a "long"
110  * (preferably unsigned long) for each cell. In practice this is overkill;
111  * we can get by with 16 bits per cell. Few of the cell counts will overflow,
112  * and clamping those that do overflow to the maximum value will give close-
113  * enough results. This reduces the recommended histogram size from 256Kb
114  * to 128Kb, which is a useful savings on PC-class machines.
115  * (In the second pass the histogram space is re-used for pixel mapping data;
116  * in that capacity, each cell must be able to store zero to the number of
117  * desired colors. 16 bits/cell is plenty for that too.)
118  * Since the JPEG code is intended to run in small memory model on 80x86
119  * machines, we can't just allocate the histogram in one chunk. Instead
120  * of a true 3-D array, we use a row of pointers to 2-D arrays. Each
121  * pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and
122  * each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries. Note that
123  * on 80x86 machines, the pointer row is in near memory but the actual
124  * arrays are in far memory (same arrangement as we use for image arrays).
125  */
126 
127 #define MAXNUMCOLORS (MAXJSAMPLE+1) /* maximum size of colormap */
128 
129 /* These will do the right thing for either R,G,B or B,G,R color order,
130  * but you may not like the results for other color orders.
131  */
132 #define HIST_C0_BITS 5 /* bits of precision in R/B histogram */
133 #define HIST_C1_BITS 6 /* bits of precision in G histogram */
134 #define HIST_C2_BITS 5 /* bits of precision in B/R histogram */
135 
136 /* Number of elements along histogram axes. */
137 #define HIST_C0_ELEMS (1<<HIST_C0_BITS)
138 #define HIST_C1_ELEMS (1<<HIST_C1_BITS)
139 #define HIST_C2_ELEMS (1<<HIST_C2_BITS)
140 
141 /* These are the amounts to shift an input value to get a histogram index. */
142 #define C0_SHIFT (BITS_IN_JSAMPLE-HIST_C0_BITS)
143 #define C1_SHIFT (BITS_IN_JSAMPLE-HIST_C1_BITS)
144 #define C2_SHIFT (BITS_IN_JSAMPLE-HIST_C2_BITS)
145 
146 
147 typedef UINT16 histcell; /* histogram cell; prefer an unsigned type */
148 
149 typedef histcell FAR * histptr; /* for pointers to histogram cells */
150 
151 typedef histcell hist1d[HIST_C2_ELEMS]; /* typedefs for the array */
152 typedef hist1d FAR * hist2d; /* type for the 2nd-level pointers */
153 typedef hist2d * hist3d; /* type for top-level pointer */
154 
155 
156 /* Declarations for Floyd-Steinberg dithering.
157  *
158  * Errors are accumulated into the array fserrors[], at a resolution of
159  * 1/16th of a pixel count. The error at a given pixel is propagated
160  * to its not-yet-processed neighbors using the standard F-S fractions,
161  * ... (here) 7/16
162  * 3/16 5/16 1/16
163  * We work left-to-right on even rows, right-to-left on odd rows.
164  *
165  * We can get away with a single array (holding one row's worth of errors)
166  * by using it to store the current row's errors at pixel columns not yet
167  * processed, but the next row's errors at columns already processed. We
168  * need only a few extra variables to hold the errors immediately around the
169  * current column. (If we are lucky, those variables are in registers, but
170  * even if not, they're probably cheaper to access than array elements are.)
171  *
172  * The fserrors[] array has (#columns + 2) entries; the extra entry at
173  * each end saves us from special-casing the first and last pixels.
174  * Each entry is three values long, one value for each color component.
175  *
176  * Note: on a wide image, we might not have enough room in a PC's near data
177  * segment to hold the error array; so it is allocated with alloc_large.
178  */
179 
180 #if BITS_IN_JSAMPLE == 8
181 typedef INT16 FSERROR; /* 16 bits should be enough */
182 typedef int LOCFSERROR; /* use 'int' for calculation temps */
183 #else
184 typedef INT32 FSERROR; /* may need more than 16 bits */
185 typedef INT32 LOCFSERROR; /* be sure calculation temps are big enough */
186 #endif
187 
188 typedef FSERROR FAR *FSERRPTR; /* pointer to error array (in FAR storage!) */
189 
190 
191 /* Private subobject */
192 
193 typedef struct {
194  struct jpeg_color_quantizer pub; /* public fields */
195 
196  /* Space for the eventually created colormap is stashed here */
197  JSAMPARRAY sv_colormap; /* colormap allocated at init time */
198  int desired; /* desired # of colors = size of colormap */
199 
200  /* Variables for accumulating image statistics */
201  hist3d histogram; /* pointer to the histogram */
202 
203  boolean needs_zeroed; /* TRUE if next pass must zero histogram */
204 
205  /* Variables for Floyd-Steinberg dithering */
206  FSERRPTR fserrors; /* accumulated errors */
207  boolean on_odd_row; /* flag to remember which row we are on */
208  int * error_limiter; /* table for clamping the applied error */
209 } my_cquantizer;
210 
212 
213 
214 /*
215  * Prescan some rows of pixels.
216  * In this module the prescan simply updates the histogram, which has been
217  * initialized to zeroes by start_pass.
218  * An output_buf parameter is required by the method signature, but no data
219  * is actually output (in fact the buffer controller is probably passing a
220  * NULL pointer).
221  */
222 
223 METHODDEF(void)
226 {
227  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
228  register JSAMPROW ptr;
229  register histptr histp;
230  register hist3d histogram = cquantize->histogram;
231  int row;
232  JDIMENSION col;
233  JDIMENSION width = cinfo->output_width;
234 
235  for (row = 0; row < num_rows; row++) {
236  ptr = input_buf[row];
237  for (col = width; col > 0; col--) {
238  /* get pixel value and index into the histogram */
239  histp = & histogram[GETJSAMPLE(ptr[0]) >> C0_SHIFT]
240  [GETJSAMPLE(ptr[1]) >> C1_SHIFT]
241  [GETJSAMPLE(ptr[2]) >> C2_SHIFT];
242  /* increment, check for overflow and undo increment if so. */
243  if (++(*histp) <= 0)
244  (*histp)--;
245  ptr += 3;
246  }
247  }
248 }
249 
250 
251 /*
252  * Next we have the really interesting routines: selection of a colormap
253  * given the completed histogram.
254  * These routines work with a list of "boxes", each representing a rectangular
255  * subset of the input color space (to histogram precision).
256  */
257 
258 typedef struct {
259  /* The bounds of the box (inclusive); expressed as histogram indexes */
260  int c0min, c0max;
261  int c1min, c1max;
262  int c2min, c2max;
263  /* The volume (actually 2-norm) of the box */
265  /* The number of nonzero histogram cells within this box */
267 } box;
268 
269 typedef box * boxptr;
270 
271 
272 LOCAL(boxptr)
273 find_biggest_color_pop (boxptr boxlist, int numboxes)
274 /* Find the splittable box with the largest color population */
275 /* Returns NULL if no splittable boxes remain */
276 {
277  register boxptr boxp;
278  register int i;
279  register long maxc = 0;
280  boxptr which = NULL;
281 
282  for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
283  if (boxp->colorcount > maxc && boxp->volume > 0) {
284  which = boxp;
285  maxc = boxp->colorcount;
286  }
287  }
288  return which;
289 }
290 
291 
292 LOCAL(boxptr)
293 find_biggest_volume (boxptr boxlist, int numboxes)
294 /* Find the splittable box with the largest (scaled) volume */
295 /* Returns NULL if no splittable boxes remain */
296 {
297  register boxptr boxp;
298  register int i;
299  register INT32 maxv = 0;
300  boxptr which = NULL;
301 
302  for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
303  if (boxp->volume > maxv) {
304  which = boxp;
305  maxv = boxp->volume;
306  }
307  }
308  return which;
309 }
310 
311 
312 LOCAL(void)
314 /* Shrink the min/max bounds of a box to enclose only nonzero elements, */
315 /* and recompute its volume and population */
316 {
317  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
318  hist3d histogram = cquantize->histogram;
319  histptr histp;
320  int c0,c1,c2;
321  int c0min,c0max,c1min,c1max,c2min,c2max;
322  INT32 dist0,dist1,dist2;
323  long ccount;
324 
325  c0min = boxp->c0min; c0max = boxp->c0max;
326  c1min = boxp->c1min; c1max = boxp->c1max;
327  c2min = boxp->c2min; c2max = boxp->c2max;
328 
329  if (c0max > c0min)
330  for (c0 = c0min; c0 <= c0max; c0++)
331  for (c1 = c1min; c1 <= c1max; c1++) {
332  histp = & histogram[c0][c1][c2min];
333  for (c2 = c2min; c2 <= c2max; c2++)
334  if (*histp++ != 0) {
335  boxp->c0min = c0min = c0;
336  goto have_c0min;
337  }
338  }
339  have_c0min:
340  if (c0max > c0min)
341  for (c0 = c0max; c0 >= c0min; c0--)
342  for (c1 = c1min; c1 <= c1max; c1++) {
343  histp = & histogram[c0][c1][c2min];
344  for (c2 = c2min; c2 <= c2max; c2++)
345  if (*histp++ != 0) {
346  boxp->c0max = c0max = c0;
347  goto have_c0max;
348  }
349  }
350  have_c0max:
351  if (c1max > c1min)
352  for (c1 = c1min; c1 <= c1max; c1++)
353  for (c0 = c0min; c0 <= c0max; c0++) {
354  histp = & histogram[c0][c1][c2min];
355  for (c2 = c2min; c2 <= c2max; c2++)
356  if (*histp++ != 0) {
357  boxp->c1min = c1min = c1;
358  goto have_c1min;
359  }
360  }
361  have_c1min:
362  if (c1max > c1min)
363  for (c1 = c1max; c1 >= c1min; c1--)
364  for (c0 = c0min; c0 <= c0max; c0++) {
365  histp = & histogram[c0][c1][c2min];
366  for (c2 = c2min; c2 <= c2max; c2++)
367  if (*histp++ != 0) {
368  boxp->c1max = c1max = c1;
369  goto have_c1max;
370  }
371  }
372  have_c1max:
373  if (c2max > c2min)
374  for (c2 = c2min; c2 <= c2max; c2++)
375  for (c0 = c0min; c0 <= c0max; c0++) {
376  histp = & histogram[c0][c1min][c2];
377  for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
378  if (*histp != 0) {
379  boxp->c2min = c2min = c2;
380  goto have_c2min;
381  }
382  }
383  have_c2min:
384  if (c2max > c2min)
385  for (c2 = c2max; c2 >= c2min; c2--)
386  for (c0 = c0min; c0 <= c0max; c0++) {
387  histp = & histogram[c0][c1min][c2];
388  for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
389  if (*histp != 0) {
390  boxp->c2max = c2max = c2;
391  goto have_c2max;
392  }
393  }
394  have_c2max:
395 
396  /* Update box volume.
397  * We use 2-norm rather than real volume here; this biases the method
398  * against making long narrow boxes, and it has the side benefit that
399  * a box is splittable iff norm > 0.
400  * Since the differences are expressed in histogram-cell units,
401  * we have to shift back to JSAMPLE units to get consistent distances;
402  * after which, we scale according to the selected distance scale factors.
403  */
404  dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE;
405  dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE;
406  dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE;
407  boxp->volume = dist0*dist0 + dist1*dist1 + dist2*dist2;
408 
409  /* Now scan remaining volume of box and compute population */
410  ccount = 0;
411  for (c0 = c0min; c0 <= c0max; c0++)
412  for (c1 = c1min; c1 <= c1max; c1++) {
413  histp = & histogram[c0][c1][c2min];
414  for (c2 = c2min; c2 <= c2max; c2++, histp++)
415  if (*histp != 0) {
416  ccount++;
417  }
418  }
419  boxp->colorcount = ccount;
420 }
421 
422 
423 LOCAL(int)
424 median_cut (j_decompress_ptr cinfo, boxptr boxlist, int numboxes,
425  int desired_colors)
426 /* Repeatedly select and split the largest box until we have enough boxes */
427 {
428  int n,lb;
429  int c0,c1,c2,cmax;
430  register boxptr b1,b2;
431 
432  while (numboxes < desired_colors) {
433  /* Select box to split.
434  * Current algorithm: by population for first half, then by volume.
435  */
436  if (numboxes*2 <= desired_colors) {
437  b1 = find_biggest_color_pop(boxlist, numboxes);
438  } else {
439  b1 = find_biggest_volume(boxlist, numboxes);
440  }
441  if (b1 == NULL) /* no splittable boxes left! */
442  break;
443  b2 = &boxlist[numboxes]; /* where new box will go */
444  /* Copy the color bounds to the new box. */
445  b2->c0max = b1->c0max; b2->c1max = b1->c1max; b2->c2max = b1->c2max;
446  b2->c0min = b1->c0min; b2->c1min = b1->c1min; b2->c2min = b1->c2min;
447  /* Choose which axis to split the box on.
448  * Current algorithm: longest scaled axis.
449  * See notes in update_box about scaling distances.
450  */
451  c0 = ((b1->c0max - b1->c0min) << C0_SHIFT) * C0_SCALE;
452  c1 = ((b1->c1max - b1->c1min) << C1_SHIFT) * C1_SCALE;
453  c2 = ((b1->c2max - b1->c2min) << C2_SHIFT) * C2_SCALE;
454  /* We want to break any ties in favor of green, then red, blue last.
455  * This code does the right thing for R,G,B or B,G,R color orders only.
456  */
457 #if RGB_RED == 0
458  cmax = c1; n = 1;
459  if (c0 > cmax) { cmax = c0; n = 0; }
460  if (c2 > cmax) { n = 2; }
461 #else
462  cmax = c1; n = 1;
463  if (c2 > cmax) { cmax = c2; n = 2; }
464  if (c0 > cmax) { n = 0; }
465 #endif
466  /* Choose split point along selected axis, and update box bounds.
467  * Current algorithm: split at halfway point.
468  * (Since the box has been shrunk to minimum volume,
469  * any split will produce two nonempty subboxes.)
470  * Note that lb value is max for lower box, so must be < old max.
471  */
472  switch (n) {
473  case 0:
474  lb = (b1->c0max + b1->c0min) / 2;
475  b1->c0max = lb;
476  b2->c0min = lb+1;
477  break;
478  case 1:
479  lb = (b1->c1max + b1->c1min) / 2;
480  b1->c1max = lb;
481  b2->c1min = lb+1;
482  break;
483  case 2:
484  lb = (b1->c2max + b1->c2min) / 2;
485  b1->c2max = lb;
486  b2->c2min = lb+1;
487  break;
488  }
489  /* Update stats for boxes */
490  update_box(cinfo, b1);
491  update_box(cinfo, b2);
492  numboxes++;
493  }
494  return numboxes;
495 }
496 
497 
498 LOCAL(void)
499 compute_color (j_decompress_ptr cinfo, boxptr boxp, int icolor)
500 /* Compute representative color for a box, put it in colormap[icolor] */
501 {
502  /* Current algorithm: mean weighted by pixels (not colors) */
503  /* Note it is important to get the rounding correct! */
504  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
505  hist3d histogram = cquantize->histogram;
506  histptr histp;
507  int c0,c1,c2;
508  int c0min,c0max,c1min,c1max,c2min,c2max;
509  long count;
510  long total = 0;
511  long c0total = 0;
512  long c1total = 0;
513  long c2total = 0;
514 
515  c0min = boxp->c0min; c0max = boxp->c0max;
516  c1min = boxp->c1min; c1max = boxp->c1max;
517  c2min = boxp->c2min; c2max = boxp->c2max;
518 
519  for (c0 = c0min; c0 <= c0max; c0++)
520  for (c1 = c1min; c1 <= c1max; c1++) {
521  histp = & histogram[c0][c1][c2min];
522  for (c2 = c2min; c2 <= c2max; c2++) {
523  if ((count = *histp++) != 0) {
524  total += count;
525  c0total += ((c0 << C0_SHIFT) + ((1<<C0_SHIFT)>>1)) * count;
526  c1total += ((c1 << C1_SHIFT) + ((1<<C1_SHIFT)>>1)) * count;
527  c2total += ((c2 << C2_SHIFT) + ((1<<C2_SHIFT)>>1)) * count;
528  }
529  }
530  }
531 
532  cinfo->colormap[0][icolor] = (JSAMPLE) ((c0total + (total>>1)) / total);
533  cinfo->colormap[1][icolor] = (JSAMPLE) ((c1total + (total>>1)) / total);
534  cinfo->colormap[2][icolor] = (JSAMPLE) ((c2total + (total>>1)) / total);
535 }
536 
537 
538 LOCAL(void)
539 select_colors (j_decompress_ptr cinfo, int desired_colors)
540 /* Master routine for color selection */
541 {
542  boxptr boxlist;
543  int numboxes;
544  int i;
545 
546  /* Allocate workspace for box list */
547  boxlist = (boxptr) (*cinfo->mem->alloc_small)
548  ((j_common_ptr) cinfo, JPOOL_IMAGE, desired_colors * SIZEOF(box));
549  /* Initialize one box containing whole space */
550  numboxes = 1;
551  boxlist[0].c0min = 0;
552  boxlist[0].c0max = MAXJSAMPLE >> C0_SHIFT;
553  boxlist[0].c1min = 0;
554  boxlist[0].c1max = MAXJSAMPLE >> C1_SHIFT;
555  boxlist[0].c2min = 0;
556  boxlist[0].c2max = MAXJSAMPLE >> C2_SHIFT;
557  /* Shrink it to actually-used volume and set its statistics */
558  update_box(cinfo, & boxlist[0]);
559  /* Perform median-cut to produce final box list */
560  numboxes = median_cut(cinfo, boxlist, numboxes, desired_colors);
561  /* Compute the representative color for each box, fill colormap */
562  for (i = 0; i < numboxes; i++)
563  compute_color(cinfo, & boxlist[i], i);
564  cinfo->actual_number_of_colors = numboxes;
565  TRACEMS1(cinfo, 1, JTRC_QUANT_SELECTED, numboxes);
566 }
567 
568 
569 /*
570  * These routines are concerned with the time-critical task of mapping input
571  * colors to the nearest color in the selected colormap.
572  *
573  * We re-use the histogram space as an "inverse color map", essentially a
574  * cache for the results of nearest-color searches. All colors within a
575  * histogram cell will be mapped to the same colormap entry, namely the one
576  * closest to the cell's center. This may not be quite the closest entry to
577  * the actual input color, but it's almost as good. A zero in the cache
578  * indicates we haven't found the nearest color for that cell yet; the array
579  * is cleared to zeroes before starting the mapping pass. When we find the
580  * nearest color for a cell, its colormap index plus one is recorded in the
581  * cache for future use. The pass2 scanning routines call fill_inverse_cmap
582  * when they need to use an unfilled entry in the cache.
583  *
584  * Our method of efficiently finding nearest colors is based on the "locally
585  * sorted search" idea described by Heckbert and on the incremental distance
586  * calculation described by Spencer W. Thomas in chapter III.1 of Graphics
587  * Gems II (James Arvo, ed. Academic Press, 1991). Thomas points out that
588  * the distances from a given colormap entry to each cell of the histogram can
589  * be computed quickly using an incremental method: the differences between
590  * distances to adjacent cells themselves differ by a constant. This allows a
591  * fairly fast implementation of the "brute force" approach of computing the
592  * distance from every colormap entry to every histogram cell. Unfortunately,
593  * it needs a work array to hold the best-distance-so-far for each histogram
594  * cell (because the inner loop has to be over cells, not colormap entries).
595  * The work array elements have to be INT32s, so the work array would need
596  * 256Kb at our recommended precision. This is not feasible in DOS machines.
597  *
598  * To get around these problems, we apply Thomas' method to compute the
599  * nearest colors for only the cells within a small subbox of the histogram.
600  * The work array need be only as big as the subbox, so the memory usage
601  * problem is solved. Furthermore, we need not fill subboxes that are never
602  * referenced in pass2; many images use only part of the color gamut, so a
603  * fair amount of work is saved. An additional advantage of this
604  * approach is that we can apply Heckbert's locality criterion to quickly
605  * eliminate colormap entries that are far away from the subbox; typically
606  * three-fourths of the colormap entries are rejected by Heckbert's criterion,
607  * and we need not compute their distances to individual cells in the subbox.
608  * The speed of this approach is heavily influenced by the subbox size: too
609  * small means too much overhead, too big loses because Heckbert's criterion
610  * can't eliminate as many colormap entries. Empirically the best subbox
611  * size seems to be about 1/512th of the histogram (1/8th in each direction).
612  *
613  * Thomas' article also describes a refined method which is asymptotically
614  * faster than the brute-force method, but it is also far more complex and
615  * cannot efficiently be applied to small subboxes. It is therefore not
616  * useful for programs intended to be portable to DOS machines. On machines
617  * with plenty of memory, filling the whole histogram in one shot with Thomas'
618  * refined method might be faster than the present code --- but then again,
619  * it might not be any faster, and it's certainly more complicated.
620  */
621 
622 
623 /* log2(histogram cells in update box) for each axis; this can be adjusted */
624 #define BOX_C0_LOG (HIST_C0_BITS-3)
625 #define BOX_C1_LOG (HIST_C1_BITS-3)
626 #define BOX_C2_LOG (HIST_C2_BITS-3)
627 
628 #define BOX_C0_ELEMS (1<<BOX_C0_LOG) /* # of hist cells in update box */
629 #define BOX_C1_ELEMS (1<<BOX_C1_LOG)
630 #define BOX_C2_ELEMS (1<<BOX_C2_LOG)
631 
632 #define BOX_C0_SHIFT (C0_SHIFT + BOX_C0_LOG)
633 #define BOX_C1_SHIFT (C1_SHIFT + BOX_C1_LOG)
634 #define BOX_C2_SHIFT (C2_SHIFT + BOX_C2_LOG)
635 
636 
637 /*
638  * The next three routines implement inverse colormap filling. They could
639  * all be folded into one big routine, but splitting them up this way saves
640  * some stack space (the mindist[] and bestdist[] arrays need not coexist)
641  * and may allow some compilers to produce better code by registerizing more
642  * inner-loop variables.
643  */
644 
645 LOCAL(int)
646 find_nearby_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
647  JSAMPLE colorlist[])
648 /* Locate the colormap entries close enough to an update box to be candidates
649  * for the nearest entry to some cell(s) in the update box. The update box
650  * is specified by the center coordinates of its first cell. The number of
651  * candidate colormap entries is returned, and their colormap indexes are
652  * placed in colorlist[].
653  * This routine uses Heckbert's "locally sorted search" criterion to select
654  * the colors that need further consideration.
655  */
656 {
657  int numcolors = cinfo->actual_number_of_colors;
658  int maxc0, maxc1, maxc2;
659  int centerc0, centerc1, centerc2;
660  int i, x, ncolors;
661  INT32 minmaxdist, min_dist, max_dist, tdist;
662  INT32 mindist[MAXNUMCOLORS]; /* min distance to colormap entry i */
663 
664  /* Compute true coordinates of update box's upper corner and center.
665  * Actually we compute the coordinates of the center of the upper-corner
666  * histogram cell, which are the upper bounds of the volume we care about.
667  * Note that since ">>" rounds down, the "center" values may be closer to
668  * min than to max; hence comparisons to them must be "<=", not "<".
669  */
670  maxc0 = minc0 + ((1 << BOX_C0_SHIFT) - (1 << C0_SHIFT));
671  centerc0 = (minc0 + maxc0) >> 1;
672  maxc1 = minc1 + ((1 << BOX_C1_SHIFT) - (1 << C1_SHIFT));
673  centerc1 = (minc1 + maxc1) >> 1;
674  maxc2 = minc2 + ((1 << BOX_C2_SHIFT) - (1 << C2_SHIFT));
675  centerc2 = (minc2 + maxc2) >> 1;
676 
677  /* For each color in colormap, find:
678  * 1. its minimum squared-distance to any point in the update box
679  * (zero if color is within update box);
680  * 2. its maximum squared-distance to any point in the update box.
681  * Both of these can be found by considering only the corners of the box.
682  * We save the minimum distance for each color in mindist[];
683  * only the smallest maximum distance is of interest.
684  */
685  minmaxdist = 0x7FFFFFFFL;
686 
687  for (i = 0; i < numcolors; i++) {
688  /* We compute the squared-c0-distance term, then add in the other two. */
689  x = GETJSAMPLE(cinfo->colormap[0][i]);
690  if (x < minc0) {
691  tdist = (x - minc0) * C0_SCALE;
692  min_dist = tdist*tdist;
693  tdist = (x - maxc0) * C0_SCALE;
694  max_dist = tdist*tdist;
695  } else if (x > maxc0) {
696  tdist = (x - maxc0) * C0_SCALE;
697  min_dist = tdist*tdist;
698  tdist = (x - minc0) * C0_SCALE;
699  max_dist = tdist*tdist;
700  } else {
701  /* within cell range so no contribution to min_dist */
702  min_dist = 0;
703  if (x <= centerc0) {
704  tdist = (x - maxc0) * C0_SCALE;
705  max_dist = tdist*tdist;
706  } else {
707  tdist = (x - minc0) * C0_SCALE;
708  max_dist = tdist*tdist;
709  }
710  }
711 
712  x = GETJSAMPLE(cinfo->colormap[1][i]);
713  if (x < minc1) {
714  tdist = (x - minc1) * C1_SCALE;
715  min_dist += tdist*tdist;
716  tdist = (x - maxc1) * C1_SCALE;
717  max_dist += tdist*tdist;
718  } else if (x > maxc1) {
719  tdist = (x - maxc1) * C1_SCALE;
720  min_dist += tdist*tdist;
721  tdist = (x - minc1) * C1_SCALE;
722  max_dist += tdist*tdist;
723  } else {
724  /* within cell range so no contribution to min_dist */
725  if (x <= centerc1) {
726  tdist = (x - maxc1) * C1_SCALE;
727  max_dist += tdist*tdist;
728  } else {
729  tdist = (x - minc1) * C1_SCALE;
730  max_dist += tdist*tdist;
731  }
732  }
733 
734  x = GETJSAMPLE(cinfo->colormap[2][i]);
735  if (x < minc2) {
736  tdist = (x - minc2) * C2_SCALE;
737  min_dist += tdist*tdist;
738  tdist = (x - maxc2) * C2_SCALE;
739  max_dist += tdist*tdist;
740  } else if (x > maxc2) {
741  tdist = (x - maxc2) * C2_SCALE;
742  min_dist += tdist*tdist;
743  tdist = (x - minc2) * C2_SCALE;
744  max_dist += tdist*tdist;
745  } else {
746  /* within cell range so no contribution to min_dist */
747  if (x <= centerc2) {
748  tdist = (x - maxc2) * C2_SCALE;
749  max_dist += tdist*tdist;
750  } else {
751  tdist = (x - minc2) * C2_SCALE;
752  max_dist += tdist*tdist;
753  }
754  }
755 
756  mindist[i] = min_dist; /* save away the results */
757  if (max_dist < minmaxdist)
758  minmaxdist = max_dist;
759  }
760 
761  /* Now we know that no cell in the update box is more than minmaxdist
762  * away from some colormap entry. Therefore, only colors that are
763  * within minmaxdist of some part of the box need be considered.
764  */
765  ncolors = 0;
766  for (i = 0; i < numcolors; i++) {
767  if (mindist[i] <= minmaxdist)
768  colorlist[ncolors++] = (JSAMPLE) i;
769  }
770  return ncolors;
771 }
772 
773 
774 LOCAL(void)
775 find_best_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
776  int numcolors, JSAMPLE colorlist[], JSAMPLE bestcolor[])
777 /* Find the closest colormap entry for each cell in the update box,
778  * given the list of candidate colors prepared by find_nearby_colors.
779  * Return the indexes of the closest entries in the bestcolor[] array.
780  * This routine uses Thomas' incremental distance calculation method to
781  * find the distance from a colormap entry to successive cells in the box.
782  */
783 {
784  int ic0, ic1, ic2;
785  int i, icolor;
786  register INT32 * bptr; /* pointer into bestdist[] array */
787  JSAMPLE * cptr; /* pointer into bestcolor[] array */
788  INT32 dist0, dist1; /* initial distance values */
789  register INT32 dist2; /* current distance in inner loop */
790  INT32 xx0, xx1; /* distance increments */
791  register INT32 xx2;
792  INT32 inc0, inc1, inc2; /* initial values for increments */
793  /* This array holds the distance to the nearest-so-far color for each cell */
795 
796  /* Initialize best-distance for each cell of the update box */
797  bptr = bestdist;
798  for (i = BOX_C0_ELEMS*BOX_C1_ELEMS*BOX_C2_ELEMS-1; i >= 0; i--)
799  *bptr++ = 0x7FFFFFFFL;
800 
801  /* For each color selected by find_nearby_colors,
802  * compute its distance to the center of each cell in the box.
803  * If that's less than best-so-far, update best distance and color number.
804  */
805 
806  /* Nominal steps between cell centers ("x" in Thomas article) */
807 #define STEP_C0 ((1 << C0_SHIFT) * C0_SCALE)
808 #define STEP_C1 ((1 << C1_SHIFT) * C1_SCALE)
809 #define STEP_C2 ((1 << C2_SHIFT) * C2_SCALE)
810 
811  for (i = 0; i < numcolors; i++) {
812  icolor = GETJSAMPLE(colorlist[i]);
813  /* Compute (square of) distance from minc0/c1/c2 to this color */
814  inc0 = (minc0 - GETJSAMPLE(cinfo->colormap[0][icolor])) * C0_SCALE;
815  dist0 = inc0*inc0;
816  inc1 = (minc1 - GETJSAMPLE(cinfo->colormap[1][icolor])) * C1_SCALE;
817  dist0 += inc1*inc1;
818  inc2 = (minc2 - GETJSAMPLE(cinfo->colormap[2][icolor])) * C2_SCALE;
819  dist0 += inc2*inc2;
820  /* Form the initial difference increments */
821  inc0 = inc0 * (2 * STEP_C0) + STEP_C0 * STEP_C0;
822  inc1 = inc1 * (2 * STEP_C1) + STEP_C1 * STEP_C1;
823  inc2 = inc2 * (2 * STEP_C2) + STEP_C2 * STEP_C2;
824  /* Now loop over all cells in box, updating distance per Thomas method */
825  bptr = bestdist;
826  cptr = bestcolor;
827  xx0 = inc0;
828  for (ic0 = BOX_C0_ELEMS-1; ic0 >= 0; ic0--) {
829  dist1 = dist0;
830  xx1 = inc1;
831  for (ic1 = BOX_C1_ELEMS-1; ic1 >= 0; ic1--) {
832  dist2 = dist1;
833  xx2 = inc2;
834  for (ic2 = BOX_C2_ELEMS-1; ic2 >= 0; ic2--) {
835  if (dist2 < *bptr) {
836  *bptr = dist2;
837  *cptr = (JSAMPLE) icolor;
838  }
839  dist2 += xx2;
840  xx2 += 2 * STEP_C2 * STEP_C2;
841  bptr++;
842  cptr++;
843  }
844  dist1 += xx1;
845  xx1 += 2 * STEP_C1 * STEP_C1;
846  }
847  dist0 += xx0;
848  xx0 += 2 * STEP_C0 * STEP_C0;
849  }
850  }
851 }
852 
853 
854 LOCAL(void)
855 fill_inverse_cmap (j_decompress_ptr cinfo, int c0, int c1, int c2)
856 /* Fill the inverse-colormap entries in the update box that contains */
857 /* histogram cell c0/c1/c2. (Only that one cell MUST be filled, but */
858 /* we can fill as many others as we wish.) */
859 {
860  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
861  hist3d histogram = cquantize->histogram;
862  int minc0, minc1, minc2; /* lower left corner of update box */
863  int ic0, ic1, ic2;
864  register JSAMPLE * cptr; /* pointer into bestcolor[] array */
865  register histptr cachep; /* pointer into main cache array */
866  /* This array lists the candidate colormap indexes. */
867  JSAMPLE colorlist[MAXNUMCOLORS];
868  int numcolors; /* number of candidate colors */
869  /* This array holds the actually closest colormap index for each cell. */
871 
872  /* Convert cell coordinates to update box ID */
873  c0 >>= BOX_C0_LOG;
874  c1 >>= BOX_C1_LOG;
875  c2 >>= BOX_C2_LOG;
876 
877  /* Compute true coordinates of update box's origin corner.
878  * Actually we compute the coordinates of the center of the corner
879  * histogram cell, which are the lower bounds of the volume we care about.
880  */
881  minc0 = (c0 << BOX_C0_SHIFT) + ((1 << C0_SHIFT) >> 1);
882  minc1 = (c1 << BOX_C1_SHIFT) + ((1 << C1_SHIFT) >> 1);
883  minc2 = (c2 << BOX_C2_SHIFT) + ((1 << C2_SHIFT) >> 1);
884 
885  /* Determine which colormap entries are close enough to be candidates
886  * for the nearest entry to some cell in the update box.
887  */
888  numcolors = find_nearby_colors(cinfo, minc0, minc1, minc2, colorlist);
889 
890  /* Determine the actually nearest colors. */
891  find_best_colors(cinfo, minc0, minc1, minc2, numcolors, colorlist,
892  bestcolor);
893 
894  /* Save the best color numbers (plus 1) in the main cache array */
895  c0 <<= BOX_C0_LOG; /* convert ID back to base cell indexes */
896  c1 <<= BOX_C1_LOG;
897  c2 <<= BOX_C2_LOG;
898  cptr = bestcolor;
899  for (ic0 = 0; ic0 < BOX_C0_ELEMS; ic0++) {
900  for (ic1 = 0; ic1 < BOX_C1_ELEMS; ic1++) {
901  cachep = & histogram[c0+ic0][c1+ic1][c2];
902  for (ic2 = 0; ic2 < BOX_C2_ELEMS; ic2++) {
903  *cachep++ = (histcell) (GETJSAMPLE(*cptr++) + 1);
904  }
905  }
906  }
907 }
908 
909 
910 /*
911  * Map some rows of pixels to the output colormapped representation.
912  */
913 
914 METHODDEF(void)
916  JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)
917 /* This version performs no dithering */
918 {
919  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
920  hist3d histogram = cquantize->histogram;
921  register JSAMPROW inptr, outptr;
922  register histptr cachep;
923  register int c0, c1, c2;
924  int row;
925  JDIMENSION col;
926  JDIMENSION width = cinfo->output_width;
927 
928  for (row = 0; row < num_rows; row++) {
929  inptr = input_buf[row];
930  outptr = output_buf[row];
931  for (col = width; col > 0; col--) {
932  /* get pixel value and index into the cache */
933  c0 = GETJSAMPLE(*inptr++) >> C0_SHIFT;
934  c1 = GETJSAMPLE(*inptr++) >> C1_SHIFT;
935  c2 = GETJSAMPLE(*inptr++) >> C2_SHIFT;
936  cachep = & histogram[c0][c1][c2];
937  /* If we have not seen this color before, find nearest colormap entry */
938  /* and update the cache */
939  if (*cachep == 0)
940  fill_inverse_cmap(cinfo, c0,c1,c2);
941  /* Now emit the colormap index for this cell */
942  *outptr++ = (JSAMPLE) (*cachep - 1);
943  }
944  }
945 }
946 
947 
948 METHODDEF(void)
950  JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)
951 /* This version performs Floyd-Steinberg dithering */
952 {
953  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
954  hist3d histogram = cquantize->histogram;
955  register LOCFSERROR cur0, cur1, cur2; /* current error or pixel value */
956  LOCFSERROR belowerr0, belowerr1, belowerr2; /* error for pixel below cur */
957  LOCFSERROR bpreverr0, bpreverr1, bpreverr2; /* error for below/prev col */
958  register FSERRPTR errorptr; /* => fserrors[] at column before current */
959  JSAMPROW inptr; /* => current input pixel */
960  JSAMPROW outptr; /* => current output pixel */
961  histptr cachep;
962  int dir; /* +1 or -1 depending on direction */
963  int dir3; /* 3*dir, for advancing inptr & errorptr */
964  int row;
965  JDIMENSION col;
966  JDIMENSION width = cinfo->output_width;
967  JSAMPLE *range_limit = cinfo->sample_range_limit;
968  int *error_limit = cquantize->error_limiter;
969  JSAMPROW colormap0 = cinfo->colormap[0];
970  JSAMPROW colormap1 = cinfo->colormap[1];
971  JSAMPROW colormap2 = cinfo->colormap[2];
973 
974  for (row = 0; row < num_rows; row++) {
975  inptr = input_buf[row];
976  outptr = output_buf[row];
977  if (cquantize->on_odd_row) {
978  /* work right to left in this row */
979  inptr += (width-1) * 3; /* so point to rightmost pixel */
980  outptr += width-1;
981  dir = -1;
982  dir3 = -3;
983  errorptr = cquantize->fserrors + (width+1)*3; /* => entry after last column */
984  cquantize->on_odd_row = FALSE; /* flip for next time */
985  } else {
986  /* work left to right in this row */
987  dir = 1;
988  dir3 = 3;
989  errorptr = cquantize->fserrors; /* => entry before first real column */
990  cquantize->on_odd_row = TRUE; /* flip for next time */
991  }
992  /* Preset error values: no error propagated to first pixel from left */
993  cur0 = cur1 = cur2 = 0;
994  /* and no error propagated to row below yet */
995  belowerr0 = belowerr1 = belowerr2 = 0;
996  bpreverr0 = bpreverr1 = bpreverr2 = 0;
997 
998  for (col = width; col > 0; col--) {
999  /* curN holds the error propagated from the previous pixel on the
1000  * current line. Add the error propagated from the previous line
1001  * to form the complete error correction term for this pixel, and
1002  * round the error term (which is expressed * 16) to an integer.
1003  * RIGHT_SHIFT rounds towards minus infinity, so adding 8 is correct
1004  * for either sign of the error value.
1005  * Note: errorptr points to *previous* column's array entry.
1006  */
1007  cur0 = RIGHT_SHIFT(cur0 + errorptr[dir3+0] + 8, 4);
1008  cur1 = RIGHT_SHIFT(cur1 + errorptr[dir3+1] + 8, 4);
1009  cur2 = RIGHT_SHIFT(cur2 + errorptr[dir3+2] + 8, 4);
1010  /* Limit the error using transfer function set by init_error_limit.
1011  * See comments with init_error_limit for rationale.
1012  */
1013  cur0 = error_limit[cur0];
1014  cur1 = error_limit[cur1];
1015  cur2 = error_limit[cur2];
1016  /* Form pixel value + error, and range-limit to 0..MAXJSAMPLE.
1017  * The maximum error is +- MAXJSAMPLE (or less with error limiting);
1018  * this sets the required size of the range_limit array.
1019  */
1020  cur0 += GETJSAMPLE(inptr[0]);
1021  cur1 += GETJSAMPLE(inptr[1]);
1022  cur2 += GETJSAMPLE(inptr[2]);
1023  cur0 = GETJSAMPLE(range_limit[cur0]);
1024  cur1 = GETJSAMPLE(range_limit[cur1]);
1025  cur2 = GETJSAMPLE(range_limit[cur2]);
1026  /* Index into the cache with adjusted pixel value */
1027  cachep = & histogram[cur0>>C0_SHIFT][cur1>>C1_SHIFT][cur2>>C2_SHIFT];
1028  /* If we have not seen this color before, find nearest colormap */
1029  /* entry and update the cache */
1030  if (*cachep == 0)
1031  fill_inverse_cmap(cinfo, cur0>>C0_SHIFT,cur1>>C1_SHIFT,cur2>>C2_SHIFT);
1032  /* Now emit the colormap index for this cell */
1033  { register int pixcode = *cachep - 1;
1034  *outptr = (JSAMPLE) pixcode;
1035  /* Compute representation error for this pixel */
1036  cur0 -= GETJSAMPLE(colormap0[pixcode]);
1037  cur1 -= GETJSAMPLE(colormap1[pixcode]);
1038  cur2 -= GETJSAMPLE(colormap2[pixcode]);
1039  }
1040  /* Compute error fractions to be propagated to adjacent pixels.
1041  * Add these into the running sums, and simultaneously shift the
1042  * next-line error sums left by 1 column.
1043  */
1044  { register LOCFSERROR bnexterr, delta;
1045 
1046  bnexterr = cur0; /* Process component 0 */
1047  delta = cur0 * 2;
1048  cur0 += delta; /* form error * 3 */
1049  errorptr[0] = (FSERROR) (bpreverr0 + cur0);
1050  cur0 += delta; /* form error * 5 */
1051  bpreverr0 = belowerr0 + cur0;
1052  belowerr0 = bnexterr;
1053  cur0 += delta; /* form error * 7 */
1054  bnexterr = cur1; /* Process component 1 */
1055  delta = cur1 * 2;
1056  cur1 += delta; /* form error * 3 */
1057  errorptr[1] = (FSERROR) (bpreverr1 + cur1);
1058  cur1 += delta; /* form error * 5 */
1059  bpreverr1 = belowerr1 + cur1;
1060  belowerr1 = bnexterr;
1061  cur1 += delta; /* form error * 7 */
1062  bnexterr = cur2; /* Process component 2 */
1063  delta = cur2 * 2;
1064  cur2 += delta; /* form error * 3 */
1065  errorptr[2] = (FSERROR) (bpreverr2 + cur2);
1066  cur2 += delta; /* form error * 5 */
1067  bpreverr2 = belowerr2 + cur2;
1068  belowerr2 = bnexterr;
1069  cur2 += delta; /* form error * 7 */
1070  }
1071  /* At this point curN contains the 7/16 error value to be propagated
1072  * to the next pixel on the current line, and all the errors for the
1073  * next line have been shifted over. We are therefore ready to move on.
1074  */
1075  inptr += dir3; /* Advance pixel pointers to next column */
1076  outptr += dir;
1077  errorptr += dir3; /* advance errorptr to current column */
1078  }
1079  /* Post-loop cleanup: we must unload the final error values into the
1080  * final fserrors[] entry. Note we need not unload belowerrN because
1081  * it is for the dummy column before or after the actual array.
1082  */
1083  errorptr[0] = (FSERROR) bpreverr0; /* unload prev errs into array */
1084  errorptr[1] = (FSERROR) bpreverr1;
1085  errorptr[2] = (FSERROR) bpreverr2;
1086  }
1087 }
1088 
1089 
1090 /*
1091  * Initialize the error-limiting transfer function (lookup table).
1092  * The raw F-S error computation can potentially compute error values of up to
1093  * +- MAXJSAMPLE. But we want the maximum correction applied to a pixel to be
1094  * much less, otherwise obviously wrong pixels will be created. (Typical
1095  * effects include weird fringes at color-area boundaries, isolated bright
1096  * pixels in a dark area, etc.) The standard advice for avoiding this problem
1097  * is to ensure that the "corners" of the color cube are allocated as output
1098  * colors; then repeated errors in the same direction cannot cause cascading
1099  * error buildup. However, that only prevents the error from getting
1100  * completely out of hand; Aaron Giles reports that error limiting improves
1101  * the results even with corner colors allocated.
1102  * A simple clamping of the error values to about +- MAXJSAMPLE/8 works pretty
1103  * well, but the smoother transfer function used below is even better. Thanks
1104  * to Aaron Giles for this idea.
1105  */
1106 
1107 LOCAL(void)
1109 /* Allocate and fill in the error_limiter table */
1110 {
1111  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1112  int * table;
1113  int in, out;
1114 
1115  table = (int *) (*cinfo->mem->alloc_small)
1116  ((j_common_ptr) cinfo, JPOOL_IMAGE, (MAXJSAMPLE*2+1) * SIZEOF(int));
1117  table += MAXJSAMPLE; /* so can index -MAXJSAMPLE .. +MAXJSAMPLE */
1118  cquantize->error_limiter = table;
1119 
1120 #define STEPSIZE ((MAXJSAMPLE+1)/16)
1121  /* Map errors 1:1 up to +- MAXJSAMPLE/16 */
1122  out = 0;
1123  for (in = 0; in < STEPSIZE; in++, out++) {
1124  table[in] = out; table[-in] = -out;
1125  }
1126  /* Map errors 1:2 up to +- 3*MAXJSAMPLE/16 */
1127  for (; in < STEPSIZE*3; in++, out += (in&1) ? 0 : 1) {
1128  table[in] = out; table[-in] = -out;
1129  }
1130  /* Clamp the rest to final out value (which is (MAXJSAMPLE+1)/8) */
1131  for (; in <= MAXJSAMPLE; in++) {
1132  table[in] = out; table[-in] = -out;
1133  }
1134 #undef STEPSIZE
1135 }
1136 
1137 
1138 /*
1139  * Finish up at the end of each pass.
1140  */
1141 
1142 METHODDEF(void)
1144 {
1145  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1146 
1147  /* Select the representative colors and fill in cinfo->colormap */
1148  cinfo->colormap = cquantize->sv_colormap;
1149  select_colors(cinfo, cquantize->desired);
1150  /* Force next pass to zero the color index table */
1151  cquantize->needs_zeroed = TRUE;
1152 }
1153 
1154 
1155 METHODDEF(void)
1157 {
1158  /* no work */
1159 }
1160 
1161 
1162 /*
1163  * Initialize for each processing pass.
1164  */
1165 
1166 METHODDEF(void)
1167 start_pass_2_quant (j_decompress_ptr cinfo, boolean is_pre_scan)
1168 {
1169  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1170  hist3d histogram = cquantize->histogram;
1171  int i;
1172 
1173  /* Only F-S dithering or no dithering is supported. */
1174  /* If user asks for ordered dither, give him F-S. */
1175  if (cinfo->dither_mode != JDITHER_NONE)
1176  cinfo->dither_mode = JDITHER_FS;
1177 
1178  if (is_pre_scan) {
1179  /* Set up method pointers */
1180  cquantize->pub.color_quantize = prescan_quantize;
1181  cquantize->pub.finish_pass = finish_pass1;
1182  cquantize->needs_zeroed = TRUE; /* Always zero histogram */
1183  } else {
1184  /* Set up method pointers */
1185  if (cinfo->dither_mode == JDITHER_FS)
1186  cquantize->pub.color_quantize = pass2_fs_dither;
1187  else
1188  cquantize->pub.color_quantize = pass2_no_dither;
1189  cquantize->pub.finish_pass = finish_pass2;
1190 
1191  /* Make sure color count is acceptable */
1192  i = cinfo->actual_number_of_colors;
1193  if (i < 1)
1194  ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 1);
1195  if (i > MAXNUMCOLORS)
1196  ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
1197 
1198  if (cinfo->dither_mode == JDITHER_FS) {
1199  size_t arraysize = (size_t) ((cinfo->output_width + 2) *
1200  (3 * SIZEOF(FSERROR)));
1201  /* Allocate Floyd-Steinberg workspace if we didn't already. */
1202  if (cquantize->fserrors == NULL)
1203  cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)
1204  ((j_common_ptr) cinfo, JPOOL_IMAGE, arraysize);
1205  /* Initialize the propagated errors to zero. */
1206  jzero_far((void FAR *) cquantize->fserrors, arraysize);
1207  /* Make the error-limit table if we didn't already. */
1208  if (cquantize->error_limiter == NULL)
1209  init_error_limit(cinfo);
1210  cquantize->on_odd_row = FALSE;
1211  }
1212 
1213  }
1214  /* Zero the histogram or inverse color map, if necessary */
1215  if (cquantize->needs_zeroed) {
1216  for (i = 0; i < HIST_C0_ELEMS; i++) {
1217  jzero_far((void FAR *) histogram[i],
1219  }
1220  cquantize->needs_zeroed = FALSE;
1221  }
1222 }
1223 
1224 
1225 /*
1226  * Switch to a new external colormap between output passes.
1227  */
1228 
1229 METHODDEF(void)
1231 {
1232  my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1233 
1234  /* Reset the inverse color map */
1235  cquantize->needs_zeroed = TRUE;
1236 }
1237 
1238 
1239 /*
1240  * Module initialization routine for 2-pass color quantization.
1241  */
1242 
1243 GLOBAL(void)
1245 {
1246  my_cquantize_ptr cquantize;
1247  int i;
1248 
1249  cquantize = (my_cquantize_ptr)
1250  (*cinfo->mem->alloc_small) ((j_common_ptr) cinfo, JPOOL_IMAGE,
1252  cinfo->cquantize = (struct jpeg_color_quantizer *) cquantize;
1253  cquantize->pub.start_pass = start_pass_2_quant;
1254  cquantize->pub.new_color_map = new_color_map_2_quant;
1255  cquantize->fserrors = NULL; /* flag optional arrays not allocated */
1256  cquantize->error_limiter = NULL;
1257 
1258  /* Make sure jdmaster didn't give me a case I can't handle */
1259  if (cinfo->out_color_components != 3)
1260  ERREXIT(cinfo, JERR_NOTIMPL);
1261 
1262  /* Allocate the histogram/inverse colormap storage */
1263  cquantize->histogram = (hist3d) (*cinfo->mem->alloc_small)
1265  for (i = 0; i < HIST_C0_ELEMS; i++) {
1266  cquantize->histogram[i] = (hist2d) (*cinfo->mem->alloc_large)
1267  ((j_common_ptr) cinfo, JPOOL_IMAGE,
1269  }
1270  cquantize->needs_zeroed = TRUE; /* histogram is garbage now */
1271 
1272  /* Allocate storage for the completed colormap, if required.
1273  * We do this now since it is FAR storage and may affect
1274  * the memory manager's space calculations.
1275  */
1276  if (cinfo->enable_2pass_quant) {
1277  /* Make sure color count is acceptable */
1278  int desired = cinfo->desired_number_of_colors;
1279  /* Lower bound on # of colors ... somewhat arbitrary as long as > 0 */
1280  if (desired < 8)
1281  ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 8);
1282  /* Make sure colormap indexes can be represented by JSAMPLEs */
1283  if (desired > MAXNUMCOLORS)
1284  ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
1285  cquantize->sv_colormap = (*cinfo->mem->alloc_sarray)
1287  cquantize->desired = desired;
1288  } else
1289  cquantize->sv_colormap = NULL;
1290 
1291  /* Only F-S dithering or no dithering is supported. */
1292  /* If user asks for ordered dither, give him F-S. */
1293  if (cinfo->dither_mode != JDITHER_NONE)
1294  cinfo->dither_mode = JDITHER_FS;
1295 
1296  /* Allocate Floyd-Steinberg workspace if necessary.
1297  * This isn't really needed until pass 2, but again it is FAR storage.
1298  * Although we will cope with a later change in dither_mode,
1299  * we do not promise to honor max_memory_to_use if dither_mode changes.
1300  */
1301  if (cinfo->dither_mode == JDITHER_FS) {
1302  cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)
1303  ((j_common_ptr) cinfo, JPOOL_IMAGE,
1304  (size_t) ((cinfo->output_width + 2) * (3 * SIZEOF(FSERROR))));
1305  /* Might as well create the error-limiting table too. */
1306  init_error_limit(cinfo);
1307  }
1308 }
1309 
1310 #endif /* QUANT_2PASS_SUPPORTED */
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openhrp3
Author(s): AIST, General Robotix Inc., Nakamura Lab of Dept. of Mechano Informatics at University of Tokyo
autogenerated on Wed Sep 7 2022 02:51:03