1/*
2 * reserved comment block
3 * DO NOT REMOVE OR ALTER!
4 */
5/*
6 * jquant2.c
7 *
8 * Copyright (C) 1991-1996, Thomas G. Lane.
9 * This file is part of the Independent JPEG Group's software.
10 * For conditions of distribution and use, see the accompanying README file.
11 *
12 * This file contains 2-pass color quantization (color mapping) routines.
13 * These routines provide selection of a custom color map for an image,
14 * followed by mapping of the image to that color map, with optional
15 * Floyd-Steinberg dithering.
16 * It is also possible to use just the second pass to map to an arbitrary
17 * externally-given color map.
18 *
19 * Note: ordered dithering is not supported, since there isn't any fast
20 * way to compute intercolor distances; it's unclear that ordered dither's
21 * fundamental assumptions even hold with an irregularly spaced color map.
22 */
23
24#define JPEG_INTERNALS
25#include "jinclude.h"
26#include "jpeglib.h"
27
28#ifdef QUANT_2PASS_SUPPORTED
29
30
31/*
32 * This module implements the well-known Heckbert paradigm for color
33 * quantization. Most of the ideas used here can be traced back to
34 * Heckbert's seminal paper
35 * Heckbert, Paul. "Color Image Quantization for Frame Buffer Display",
36 * Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304.
37 *
38 * In the first pass over the image, we accumulate a histogram showing the
39 * usage count of each possible color. To keep the histogram to a reasonable
40 * size, we reduce the precision of the input; typical practice is to retain
41 * 5 or 6 bits per color, so that 8 or 4 different input values are counted
42 * in the same histogram cell.
43 *
44 * Next, the color-selection step begins with a box representing the whole
45 * color space, and repeatedly splits the "largest" remaining box until we
46 * have as many boxes as desired colors. Then the mean color in each
47 * remaining box becomes one of the possible output colors.
48 *
49 * The second pass over the image maps each input pixel to the closest output
50 * color (optionally after applying a Floyd-Steinberg dithering correction).
51 * This mapping is logically trivial, but making it go fast enough requires
52 * considerable care.
53 *
54 * Heckbert-style quantizers vary a good deal in their policies for choosing
55 * the "largest" box and deciding where to cut it. The particular policies
56 * used here have proved out well in experimental comparisons, but better ones
57 * may yet be found.
58 *
59 * In earlier versions of the IJG code, this module quantized in YCbCr color
60 * space, processing the raw upsampled data without a color conversion step.
61 * This allowed the color conversion math to be done only once per colormap
62 * entry, not once per pixel. However, that optimization precluded other
63 * useful optimizations (such as merging color conversion with upsampling)
64 * and it also interfered with desired capabilities such as quantizing to an
65 * externally-supplied colormap. We have therefore abandoned that approach.
66 * The present code works in the post-conversion color space, typically RGB.
67 *
68 * To improve the visual quality of the results, we actually work in scaled
69 * RGB space, giving G distances more weight than R, and R in turn more than
70 * B. To do everything in integer math, we must use integer scale factors.
71 * The 2/3/1 scale factors used here correspond loosely to the relative
72 * weights of the colors in the NTSC grayscale equation.
73 * If you want to use this code to quantize a non-RGB color space, you'll
74 * probably need to change these scale factors.
75 */
76
77#define R_SCALE 2 /* scale R distances by this much */
78#define G_SCALE 3 /* scale G distances by this much */
79#define B_SCALE 1 /* and B by this much */
80
81/* Relabel R/G/B as components 0/1/2, respecting the RGB ordering defined
82 * in jmorecfg.h. As the code stands, it will do the right thing for R,G,B
83 * and B,G,R orders. If you define some other weird order in jmorecfg.h,
84 * you'll get compile errors until you extend this logic. In that case
85 * you'll probably want to tweak the histogram sizes too.
86 */
87
88#if RGB_RED == 0
89#define C0_SCALE R_SCALE
90#endif
91#if RGB_BLUE == 0
92#define C0_SCALE B_SCALE
93#endif
94#if RGB_GREEN == 1
95#define C1_SCALE G_SCALE
96#endif
97#if RGB_RED == 2
98#define C2_SCALE R_SCALE
99#endif
100#if RGB_BLUE == 2
101#define C2_SCALE B_SCALE
102#endif
103
104
105/*
106 * First we have the histogram data structure and routines for creating it.
107 *
108 * The number of bits of precision can be adjusted by changing these symbols.
109 * We recommend keeping 6 bits for G and 5 each for R and B.
110 * If you have plenty of memory and cycles, 6 bits all around gives marginally
111 * better results; if you are short of memory, 5 bits all around will save
112 * some space but degrade the results.
113 * To maintain a fully accurate histogram, we'd need to allocate a "long"
114 * (preferably unsigned long) for each cell. In practice this is overkill;
115 * we can get by with 16 bits per cell. Few of the cell counts will overflow,
116 * and clamping those that do overflow to the maximum value will give close-
117 * enough results. This reduces the recommended histogram size from 256Kb
118 * to 128Kb, which is a useful savings on PC-class machines.
119 * (In the second pass the histogram space is re-used for pixel mapping data;
120 * in that capacity, each cell must be able to store zero to the number of
121 * desired colors. 16 bits/cell is plenty for that too.)
122 * Since the JPEG code is intended to run in small memory model on 80x86
123 * machines, we can't just allocate the histogram in one chunk. Instead
124 * of a true 3-D array, we use a row of pointers to 2-D arrays. Each
125 * pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and
126 * each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries. Note that
127 * on 80x86 machines, the pointer row is in near memory but the actual
128 * arrays are in far memory (same arrangement as we use for image arrays).
129 */
130
131#define MAXNUMCOLORS (MAXJSAMPLE+1) /* maximum size of colormap */
132
133/* These will do the right thing for either R,G,B or B,G,R color order,
134 * but you may not like the results for other color orders.
135 */
136#define HIST_C0_BITS 5 /* bits of precision in R/B histogram */
137#define HIST_C1_BITS 6 /* bits of precision in G histogram */
138#define HIST_C2_BITS 5 /* bits of precision in B/R histogram */
139
140/* Number of elements along histogram axes. */
141#define HIST_C0_ELEMS (1<<HIST_C0_BITS)
142#define HIST_C1_ELEMS (1<<HIST_C1_BITS)
143#define HIST_C2_ELEMS (1<<HIST_C2_BITS)
144
145/* These are the amounts to shift an input value to get a histogram index. */
146#define C0_SHIFT (BITS_IN_JSAMPLE-HIST_C0_BITS)
147#define C1_SHIFT (BITS_IN_JSAMPLE-HIST_C1_BITS)
148#define C2_SHIFT (BITS_IN_JSAMPLE-HIST_C2_BITS)
149
150
151typedef UINT16 histcell; /* histogram cell; prefer an unsigned type */
152
153typedef histcell FAR * histptr; /* for pointers to histogram cells */
154
155typedef histcell hist1d[HIST_C2_ELEMS]; /* typedefs for the array */
156typedef hist1d FAR * hist2d; /* type for the 2nd-level pointers */
157typedef hist2d * hist3d; /* type for top-level pointer */
158
159
160/* Declarations for Floyd-Steinberg dithering.
161 *
162 * Errors are accumulated into the array fserrors[], at a resolution of
163 * 1/16th of a pixel count. The error at a given pixel is propagated
164 * to its not-yet-processed neighbors using the standard F-S fractions,
165 * ... (here) 7/16
166 * 3/16 5/16 1/16
167 * We work left-to-right on even rows, right-to-left on odd rows.
168 *
169 * We can get away with a single array (holding one row's worth of errors)
170 * by using it to store the current row's errors at pixel columns not yet
171 * processed, but the next row's errors at columns already processed. We
172 * need only a few extra variables to hold the errors immediately around the
173 * current column. (If we are lucky, those variables are in registers, but
174 * even if not, they're probably cheaper to access than array elements are.)
175 *
176 * The fserrors[] array has (#columns + 2) entries; the extra entry at
177 * each end saves us from special-casing the first and last pixels.
178 * Each entry is three values long, one value for each color component.
179 *
180 * Note: on a wide image, we might not have enough room in a PC's near data
181 * segment to hold the error array; so it is allocated with alloc_large.
182 */
183
184#if BITS_IN_JSAMPLE == 8
185typedef INT16 FSERROR; /* 16 bits should be enough */
186typedef int LOCFSERROR; /* use 'int' for calculation temps */
187#else
188typedef INT32 FSERROR; /* may need more than 16 bits */
189typedef INT32 LOCFSERROR; /* be sure calculation temps are big enough */
190#endif
191
192typedef FSERROR FAR *FSERRPTR; /* pointer to error array (in FAR storage!) */
193
194
195/* Private subobject */
196
197typedef struct {
198 struct jpeg_color_quantizer pub; /* public fields */
199
200 /* Space for the eventually created colormap is stashed here */
201 JSAMPARRAY sv_colormap; /* colormap allocated at init time */
202 int desired; /* desired # of colors = size of colormap */
203
204 /* Variables for accumulating image statistics */
205 hist3d histogram; /* pointer to the histogram */
206
207 boolean needs_zeroed; /* TRUE if next pass must zero histogram */
208
209 /* Variables for Floyd-Steinberg dithering */
210 FSERRPTR fserrors; /* accumulated errors */
211 boolean on_odd_row; /* flag to remember which row we are on */
212 int * error_limiter; /* table for clamping the applied error */
213} my_cquantizer;
214
215typedef my_cquantizer * my_cquantize_ptr;
216
217
218/*
219 * Prescan some rows of pixels.
220 * In this module the prescan simply updates the histogram, which has been
221 * initialized to zeroes by start_pass.
222 * An output_buf parameter is required by the method signature, but no data
223 * is actually output (in fact the buffer controller is probably passing a
224 * NULL pointer).
225 */
226
227METHODDEF(void)
228prescan_quantize (j_decompress_ptr cinfo, JSAMPARRAY input_buf,
229 JSAMPARRAY output_buf, int num_rows)
230{
231 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
232 register JSAMPROW ptr;
233 register histptr histp;
234 register hist3d histogram = cquantize->histogram;
235 int row;
236 JDIMENSION col;
237 JDIMENSION width = cinfo->output_width;
238
239 for (row = 0; row < num_rows; row++) {
240 ptr = input_buf[row];
241 for (col = width; col > 0; col--) {
242 /* get pixel value and index into the histogram */
243 histp = & histogram[GETJSAMPLE(ptr[0]) >> C0_SHIFT]
244 [GETJSAMPLE(ptr[1]) >> C1_SHIFT]
245 [GETJSAMPLE(ptr[2]) >> C2_SHIFT];
246 /* increment, check for overflow and undo increment if so. */
247 if (++(*histp) <= 0)
248 (*histp)--;
249 ptr += 3;
250 }
251 }
252}
253
254
255/*
256 * Next we have the really interesting routines: selection of a colormap
257 * given the completed histogram.
258 * These routines work with a list of "boxes", each representing a rectangular
259 * subset of the input color space (to histogram precision).
260 */
261
262typedef struct {
263 /* The bounds of the box (inclusive); expressed as histogram indexes */
264 int c0min, c0max;
265 int c1min, c1max;
266 int c2min, c2max;
267 /* The volume (actually 2-norm) of the box */
268 INT32 volume;
269 /* The number of nonzero histogram cells within this box */
270 long colorcount;
271} box;
272
273typedef box * boxptr;
274
275
276LOCAL(boxptr)
277find_biggest_color_pop (boxptr boxlist, int numboxes)
278/* Find the splittable box with the largest color population */
279/* Returns NULL if no splittable boxes remain */
280{
281 register boxptr boxp;
282 register int i;
283 register long maxc = 0;
284 boxptr which = NULL;
285
286 for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
287 if (boxp->colorcount > maxc && boxp->volume > 0) {
288 which = boxp;
289 maxc = boxp->colorcount;
290 }
291 }
292 return which;
293}
294
295
296LOCAL(boxptr)
297find_biggest_volume (boxptr boxlist, int numboxes)
298/* Find the splittable box with the largest (scaled) volume */
299/* Returns NULL if no splittable boxes remain */
300{
301 register boxptr boxp;
302 register int i;
303 register INT32 maxv = 0;
304 boxptr which = NULL;
305
306 for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
307 if (boxp->volume > maxv) {
308 which = boxp;
309 maxv = boxp->volume;
310 }
311 }
312 return which;
313}
314
315
316LOCAL(void)
317update_box (j_decompress_ptr cinfo, boxptr boxp)
318/* Shrink the min/max bounds of a box to enclose only nonzero elements, */
319/* and recompute its volume and population */
320{
321 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
322 hist3d histogram = cquantize->histogram;
323 histptr histp;
324 int c0,c1,c2;
325 int c0min,c0max,c1min,c1max,c2min,c2max;
326 INT32 dist0,dist1,dist2;
327 long ccount;
328
329 c0min = boxp->c0min; c0max = boxp->c0max;
330 c1min = boxp->c1min; c1max = boxp->c1max;
331 c2min = boxp->c2min; c2max = boxp->c2max;
332
333 if (c0max > c0min)
334 for (c0 = c0min; c0 <= c0max; c0++)
335 for (c1 = c1min; c1 <= c1max; c1++) {
336 histp = & histogram[c0][c1][c2min];
337 for (c2 = c2min; c2 <= c2max; c2++)
338 if (*histp++ != 0) {
339 boxp->c0min = c0min = c0;
340 goto have_c0min;
341 }
342 }
343 have_c0min:
344 if (c0max > c0min)
345 for (c0 = c0max; c0 >= c0min; c0--)
346 for (c1 = c1min; c1 <= c1max; c1++) {
347 histp = & histogram[c0][c1][c2min];
348 for (c2 = c2min; c2 <= c2max; c2++)
349 if (*histp++ != 0) {
350 boxp->c0max = c0max = c0;
351 goto have_c0max;
352 }
353 }
354 have_c0max:
355 if (c1max > c1min)
356 for (c1 = c1min; c1 <= c1max; c1++)
357 for (c0 = c0min; c0 <= c0max; c0++) {
358 histp = & histogram[c0][c1][c2min];
359 for (c2 = c2min; c2 <= c2max; c2++)
360 if (*histp++ != 0) {
361 boxp->c1min = c1min = c1;
362 goto have_c1min;
363 }
364 }
365 have_c1min:
366 if (c1max > c1min)
367 for (c1 = c1max; c1 >= c1min; c1--)
368 for (c0 = c0min; c0 <= c0max; c0++) {
369 histp = & histogram[c0][c1][c2min];
370 for (c2 = c2min; c2 <= c2max; c2++)
371 if (*histp++ != 0) {
372 boxp->c1max = c1max = c1;
373 goto have_c1max;
374 }
375 }
376 have_c1max:
377 if (c2max > c2min)
378 for (c2 = c2min; c2 <= c2max; c2++)
379 for (c0 = c0min; c0 <= c0max; c0++) {
380 histp = & histogram[c0][c1min][c2];
381 for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
382 if (*histp != 0) {
383 boxp->c2min = c2min = c2;
384 goto have_c2min;
385 }
386 }
387 have_c2min:
388 if (c2max > c2min)
389 for (c2 = c2max; c2 >= c2min; c2--)
390 for (c0 = c0min; c0 <= c0max; c0++) {
391 histp = & histogram[c0][c1min][c2];
392 for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
393 if (*histp != 0) {
394 boxp->c2max = c2max = c2;
395 goto have_c2max;
396 }
397 }
398 have_c2max:
399
400 /* Update box volume.
401 * We use 2-norm rather than real volume here; this biases the method
402 * against making long narrow boxes, and it has the side benefit that
403 * a box is splittable iff norm > 0.
404 * Since the differences are expressed in histogram-cell units,
405 * we have to shift back to JSAMPLE units to get consistent distances;
406 * after which, we scale according to the selected distance scale factors.
407 */
408 dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE;
409 dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE;
410 dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE;
411 boxp->volume = dist0*dist0 + dist1*dist1 + dist2*dist2;
412
413 /* Now scan remaining volume of box and compute population */
414 ccount = 0;
415 for (c0 = c0min; c0 <= c0max; c0++)
416 for (c1 = c1min; c1 <= c1max; c1++) {
417 histp = & histogram[c0][c1][c2min];
418 for (c2 = c2min; c2 <= c2max; c2++, histp++)
419 if (*histp != 0) {
420 ccount++;
421 }
422 }
423 boxp->colorcount = ccount;
424}
425
426
427LOCAL(int)
428median_cut (j_decompress_ptr cinfo, boxptr boxlist, int numboxes,
429 int desired_colors)
430/* Repeatedly select and split the largest box until we have enough boxes */
431{
432 int n,lb;
433 int c0,c1,c2,cmax;
434 register boxptr b1,b2;
435
436 while (numboxes < desired_colors) {
437 /* Select box to split.
438 * Current algorithm: by population for first half, then by volume.
439 */
440 if (numboxes*2 <= desired_colors) {
441 b1 = find_biggest_color_pop(boxlist, numboxes);
442 } else {
443 b1 = find_biggest_volume(boxlist, numboxes);
444 }
445 if (b1 == NULL) /* no splittable boxes left! */
446 break;
447 b2 = &boxlist[numboxes]; /* where new box will go */
448 /* Copy the color bounds to the new box. */
449 b2->c0max = b1->c0max; b2->c1max = b1->c1max; b2->c2max = b1->c2max;
450 b2->c0min = b1->c0min; b2->c1min = b1->c1min; b2->c2min = b1->c2min;
451 /* Choose which axis to split the box on.
452 * Current algorithm: longest scaled axis.
453 * See notes in update_box about scaling distances.
454 */
455 c0 = ((b1->c0max - b1->c0min) << C0_SHIFT) * C0_SCALE;
456 c1 = ((b1->c1max - b1->c1min) << C1_SHIFT) * C1_SCALE;
457 c2 = ((b1->c2max - b1->c2min) << C2_SHIFT) * C2_SCALE;
458 /* We want to break any ties in favor of green, then red, blue last.
459 * This code does the right thing for R,G,B or B,G,R color orders only.
460 */
461#if RGB_RED == 0
462 cmax = c1; n = 1;
463 if (c0 > cmax) { cmax = c0; n = 0; }
464 if (c2 > cmax) { n = 2; }
465#else
466 cmax = c1; n = 1;
467 if (c2 > cmax) { cmax = c2; n = 2; }
468 if (c0 > cmax) { n = 0; }
469#endif
470 /* Choose split point along selected axis, and update box bounds.
471 * Current algorithm: split at halfway point.
472 * (Since the box has been shrunk to minimum volume,
473 * any split will produce two nonempty subboxes.)
474 * Note that lb value is max for lower box, so must be < old max.
475 */
476 switch (n) {
477 case 0:
478 lb = (b1->c0max + b1->c0min) / 2;
479 b1->c0max = lb;
480 b2->c0min = lb+1;
481 break;
482 case 1:
483 lb = (b1->c1max + b1->c1min) / 2;
484 b1->c1max = lb;
485 b2->c1min = lb+1;
486 break;
487 case 2:
488 lb = (b1->c2max + b1->c2min) / 2;
489 b1->c2max = lb;
490 b2->c2min = lb+1;
491 break;
492 }
493 /* Update stats for boxes */
494 update_box(cinfo, b1);
495 update_box(cinfo, b2);
496 numboxes++;
497 }
498 return numboxes;
499}
500
501
502LOCAL(void)
503compute_color (j_decompress_ptr cinfo, boxptr boxp, int icolor)
504/* Compute representative color for a box, put it in colormap[icolor] */
505{
506 /* Current algorithm: mean weighted by pixels (not colors) */
507 /* Note it is important to get the rounding correct! */
508 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
509 hist3d histogram = cquantize->histogram;
510 histptr histp;
511 int c0,c1,c2;
512 int c0min,c0max,c1min,c1max,c2min,c2max;
513 long count;
514 long total = 0;
515 long c0total = 0;
516 long c1total = 0;
517 long c2total = 0;
518
519 c0min = boxp->c0min; c0max = boxp->c0max;
520 c1min = boxp->c1min; c1max = boxp->c1max;
521 c2min = boxp->c2min; c2max = boxp->c2max;
522
523 for (c0 = c0min; c0 <= c0max; c0++)
524 for (c1 = c1min; c1 <= c1max; c1++) {
525 histp = & histogram[c0][c1][c2min];
526 for (c2 = c2min; c2 <= c2max; c2++) {
527 if ((count = *histp++) != 0) {
528 total += count;
529 c0total += ((c0 << C0_SHIFT) + ((1<<C0_SHIFT)>>1)) * count;
530 c1total += ((c1 << C1_SHIFT) + ((1<<C1_SHIFT)>>1)) * count;
531 c2total += ((c2 << C2_SHIFT) + ((1<<C2_SHIFT)>>1)) * count;
532 }
533 }
534 }
535
536 cinfo->colormap[0][icolor] = (JSAMPLE) ((c0total + (total>>1)) / total);
537 cinfo->colormap[1][icolor] = (JSAMPLE) ((c1total + (total>>1)) / total);
538 cinfo->colormap[2][icolor] = (JSAMPLE) ((c2total + (total>>1)) / total);
539}
540
541
542LOCAL(void)
543select_colors (j_decompress_ptr cinfo, int desired_colors)
544/* Master routine for color selection */
545{
546 boxptr boxlist;
547 int numboxes;
548 int i;
549
550 /* Allocate workspace for box list */
551 boxlist = (boxptr) (*cinfo->mem->alloc_small)
552 ((j_common_ptr) cinfo, JPOOL_IMAGE, desired_colors * SIZEOF(box));
553 /* Initialize one box containing whole space */
554 numboxes = 1;
555 boxlist[0].c0min = 0;
556 boxlist[0].c0max = MAXJSAMPLE >> C0_SHIFT;
557 boxlist[0].c1min = 0;
558 boxlist[0].c1max = MAXJSAMPLE >> C1_SHIFT;
559 boxlist[0].c2min = 0;
560 boxlist[0].c2max = MAXJSAMPLE >> C2_SHIFT;
561 /* Shrink it to actually-used volume and set its statistics */
562 update_box(cinfo, & boxlist[0]);
563 /* Perform median-cut to produce final box list */
564 numboxes = median_cut(cinfo, boxlist, numboxes, desired_colors);
565 /* Compute the representative color for each box, fill colormap */
566 for (i = 0; i < numboxes; i++)
567 compute_color(cinfo, & boxlist[i], i);
568 cinfo->actual_number_of_colors = numboxes;
569 TRACEMS1(cinfo, 1, JTRC_QUANT_SELECTED, numboxes);
570}
571
572
573/*
574 * These routines are concerned with the time-critical task of mapping input
575 * colors to the nearest color in the selected colormap.
576 *
577 * We re-use the histogram space as an "inverse color map", essentially a
578 * cache for the results of nearest-color searches. All colors within a
579 * histogram cell will be mapped to the same colormap entry, namely the one
580 * closest to the cell's center. This may not be quite the closest entry to
581 * the actual input color, but it's almost as good. A zero in the cache
582 * indicates we haven't found the nearest color for that cell yet; the array
583 * is cleared to zeroes before starting the mapping pass. When we find the
584 * nearest color for a cell, its colormap index plus one is recorded in the
585 * cache for future use. The pass2 scanning routines call fill_inverse_cmap
586 * when they need to use an unfilled entry in the cache.
587 *
588 * Our method of efficiently finding nearest colors is based on the "locally
589 * sorted search" idea described by Heckbert and on the incremental distance
590 * calculation described by Spencer W. Thomas in chapter III.1 of Graphics
591 * Gems II (James Arvo, ed. Academic Press, 1991). Thomas points out that
592 * the distances from a given colormap entry to each cell of the histogram can
593 * be computed quickly using an incremental method: the differences between
594 * distances to adjacent cells themselves differ by a constant. This allows a
595 * fairly fast implementation of the "brute force" approach of computing the
596 * distance from every colormap entry to every histogram cell. Unfortunately,
597 * it needs a work array to hold the best-distance-so-far for each histogram
598 * cell (because the inner loop has to be over cells, not colormap entries).
599 * The work array elements have to be INT32s, so the work array would need
600 * 256Kb at our recommended precision. This is not feasible in DOS machines.
601 *
602 * To get around these problems, we apply Thomas' method to compute the
603 * nearest colors for only the cells within a small subbox of the histogram.
604 * The work array need be only as big as the subbox, so the memory usage
605 * problem is solved. Furthermore, we need not fill subboxes that are never
606 * referenced in pass2; many images use only part of the color gamut, so a
607 * fair amount of work is saved. An additional advantage of this
608 * approach is that we can apply Heckbert's locality criterion to quickly
609 * eliminate colormap entries that are far away from the subbox; typically
610 * three-fourths of the colormap entries are rejected by Heckbert's criterion,
611 * and we need not compute their distances to individual cells in the subbox.
612 * The speed of this approach is heavily influenced by the subbox size: too
613 * small means too much overhead, too big loses because Heckbert's criterion
614 * can't eliminate as many colormap entries. Empirically the best subbox
615 * size seems to be about 1/512th of the histogram (1/8th in each direction).
616 *
617 * Thomas' article also describes a refined method which is asymptotically
618 * faster than the brute-force method, but it is also far more complex and
619 * cannot efficiently be applied to small subboxes. It is therefore not
620 * useful for programs intended to be portable to DOS machines. On machines
621 * with plenty of memory, filling the whole histogram in one shot with Thomas'
622 * refined method might be faster than the present code --- but then again,
623 * it might not be any faster, and it's certainly more complicated.
624 */
625
626
627/* log2(histogram cells in update box) for each axis; this can be adjusted */
628#define BOX_C0_LOG (HIST_C0_BITS-3)
629#define BOX_C1_LOG (HIST_C1_BITS-3)
630#define BOX_C2_LOG (HIST_C2_BITS-3)
631
632#define BOX_C0_ELEMS (1<<BOX_C0_LOG) /* # of hist cells in update box */
633#define BOX_C1_ELEMS (1<<BOX_C1_LOG)
634#define BOX_C2_ELEMS (1<<BOX_C2_LOG)
635
636#define BOX_C0_SHIFT (C0_SHIFT + BOX_C0_LOG)
637#define BOX_C1_SHIFT (C1_SHIFT + BOX_C1_LOG)
638#define BOX_C2_SHIFT (C2_SHIFT + BOX_C2_LOG)
639
640
641/*
642 * The next three routines implement inverse colormap filling. They could
643 * all be folded into one big routine, but splitting them up this way saves
644 * some stack space (the mindist[] and bestdist[] arrays need not coexist)
645 * and may allow some compilers to produce better code by registerizing more
646 * inner-loop variables.
647 */
648
649LOCAL(int)
650find_nearby_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
651 JSAMPLE colorlist[])
652/* Locate the colormap entries close enough to an update box to be candidates
653 * for the nearest entry to some cell(s) in the update box. The update box
654 * is specified by the center coordinates of its first cell. The number of
655 * candidate colormap entries is returned, and their colormap indexes are
656 * placed in colorlist[].
657 * This routine uses Heckbert's "locally sorted search" criterion to select
658 * the colors that need further consideration.
659 */
660{
661 int numcolors = cinfo->actual_number_of_colors;
662 int maxc0, maxc1, maxc2;
663 int centerc0, centerc1, centerc2;
664 int i, x, ncolors;
665 INT32 minmaxdist, min_dist, max_dist, tdist;
666 INT32 mindist[MAXNUMCOLORS]; /* min distance to colormap entry i */
667
668 /* Compute true coordinates of update box's upper corner and center.
669 * Actually we compute the coordinates of the center of the upper-corner
670 * histogram cell, which are the upper bounds of the volume we care about.
671 * Note that since ">>" rounds down, the "center" values may be closer to
672 * min than to max; hence comparisons to them must be "<=", not "<".
673 */
674 maxc0 = minc0 + ((1 << BOX_C0_SHIFT) - (1 << C0_SHIFT));
675 centerc0 = (minc0 + maxc0) >> 1;
676 maxc1 = minc1 + ((1 << BOX_C1_SHIFT) - (1 << C1_SHIFT));
677 centerc1 = (minc1 + maxc1) >> 1;
678 maxc2 = minc2 + ((1 << BOX_C2_SHIFT) - (1 << C2_SHIFT));
679 centerc2 = (minc2 + maxc2) >> 1;
680
681 /* For each color in colormap, find:
682 * 1. its minimum squared-distance to any point in the update box
683 * (zero if color is within update box);
684 * 2. its maximum squared-distance to any point in the update box.
685 * Both of these can be found by considering only the corners of the box.
686 * We save the minimum distance for each color in mindist[];
687 * only the smallest maximum distance is of interest.
688 */
689 minmaxdist = 0x7FFFFFFFL;
690
691 for (i = 0; i < numcolors; i++) {
692 /* We compute the squared-c0-distance term, then add in the other two. */
693 x = GETJSAMPLE(cinfo->colormap[0][i]);
694 if (x < minc0) {
695 tdist = (x - minc0) * C0_SCALE;
696 min_dist = tdist*tdist;
697 tdist = (x - maxc0) * C0_SCALE;
698 max_dist = tdist*tdist;
699 } else if (x > maxc0) {
700 tdist = (x - maxc0) * C0_SCALE;
701 min_dist = tdist*tdist;
702 tdist = (x - minc0) * C0_SCALE;
703 max_dist = tdist*tdist;
704 } else {
705 /* within cell range so no contribution to min_dist */
706 min_dist = 0;
707 if (x <= centerc0) {
708 tdist = (x - maxc0) * C0_SCALE;
709 max_dist = tdist*tdist;
710 } else {
711 tdist = (x - minc0) * C0_SCALE;
712 max_dist = tdist*tdist;
713 }
714 }
715
716 x = GETJSAMPLE(cinfo->colormap[1][i]);
717 if (x < minc1) {
718 tdist = (x - minc1) * C1_SCALE;
719 min_dist += tdist*tdist;
720 tdist = (x - maxc1) * C1_SCALE;
721 max_dist += tdist*tdist;
722 } else if (x > maxc1) {
723 tdist = (x - maxc1) * C1_SCALE;
724 min_dist += tdist*tdist;
725 tdist = (x - minc1) * C1_SCALE;
726 max_dist += tdist*tdist;
727 } else {
728 /* within cell range so no contribution to min_dist */
729 if (x <= centerc1) {
730 tdist = (x - maxc1) * C1_SCALE;
731 max_dist += tdist*tdist;
732 } else {
733 tdist = (x - minc1) * C1_SCALE;
734 max_dist += tdist*tdist;
735 }
736 }
737
738 x = GETJSAMPLE(cinfo->colormap[2][i]);
739 if (x < minc2) {
740 tdist = (x - minc2) * C2_SCALE;
741 min_dist += tdist*tdist;
742 tdist = (x - maxc2) * C2_SCALE;
743 max_dist += tdist*tdist;
744 } else if (x > maxc2) {
745 tdist = (x - maxc2) * C2_SCALE;
746 min_dist += tdist*tdist;
747 tdist = (x - minc2) * C2_SCALE;
748 max_dist += tdist*tdist;
749 } else {
750 /* within cell range so no contribution to min_dist */
751 if (x <= centerc2) {
752 tdist = (x - maxc2) * C2_SCALE;
753 max_dist += tdist*tdist;
754 } else {
755 tdist = (x - minc2) * C2_SCALE;
756 max_dist += tdist*tdist;
757 }
758 }
759
760 mindist[i] = min_dist; /* save away the results */
761 if (max_dist < minmaxdist)
762 minmaxdist = max_dist;
763 }
764
765 /* Now we know that no cell in the update box is more than minmaxdist
766 * away from some colormap entry. Therefore, only colors that are
767 * within minmaxdist of some part of the box need be considered.
768 */
769 ncolors = 0;
770 for (i = 0; i < numcolors; i++) {
771 if (mindist[i] <= minmaxdist)
772 colorlist[ncolors++] = (JSAMPLE) i;
773 }
774 return ncolors;
775}
776
777
778LOCAL(void)
779find_best_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2,
780 int numcolors, JSAMPLE colorlist[], JSAMPLE bestcolor[])
781/* Find the closest colormap entry for each cell in the update box,
782 * given the list of candidate colors prepared by find_nearby_colors.
783 * Return the indexes of the closest entries in the bestcolor[] array.
784 * This routine uses Thomas' incremental distance calculation method to
785 * find the distance from a colormap entry to successive cells in the box.
786 */
787{
788 int ic0, ic1, ic2;
789 int i, icolor;
790 register INT32 * bptr; /* pointer into bestdist[] array */
791 JSAMPLE * cptr; /* pointer into bestcolor[] array */
792 INT32 dist0, dist1; /* initial distance values */
793 register INT32 dist2; /* current distance in inner loop */
794 INT32 xx0, xx1; /* distance increments */
795 register INT32 xx2;
796 INT32 inc0, inc1, inc2; /* initial values for increments */
797 /* This array holds the distance to the nearest-so-far color for each cell */
798 INT32 bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
799
800 /* Initialize best-distance for each cell of the update box */
801 bptr = bestdist;
802 for (i = BOX_C0_ELEMS*BOX_C1_ELEMS*BOX_C2_ELEMS-1; i >= 0; i--)
803 *bptr++ = 0x7FFFFFFFL;
804
805 /* For each color selected by find_nearby_colors,
806 * compute its distance to the center of each cell in the box.
807 * If that's less than best-so-far, update best distance and color number.
808 */
809
810 /* Nominal steps between cell centers ("x" in Thomas article) */
811#define STEP_C0 ((1 << C0_SHIFT) * C0_SCALE)
812#define STEP_C1 ((1 << C1_SHIFT) * C1_SCALE)
813#define STEP_C2 ((1 << C2_SHIFT) * C2_SCALE)
814
815 for (i = 0; i < numcolors; i++) {
816 icolor = GETJSAMPLE(colorlist[i]);
817 /* Compute (square of) distance from minc0/c1/c2 to this color */
818 inc0 = (minc0 - GETJSAMPLE(cinfo->colormap[0][icolor])) * C0_SCALE;
819 dist0 = inc0*inc0;
820 inc1 = (minc1 - GETJSAMPLE(cinfo->colormap[1][icolor])) * C1_SCALE;
821 dist0 += inc1*inc1;
822 inc2 = (minc2 - GETJSAMPLE(cinfo->colormap[2][icolor])) * C2_SCALE;
823 dist0 += inc2*inc2;
824 /* Form the initial difference increments */
825 inc0 = inc0 * (2 * STEP_C0) + STEP_C0 * STEP_C0;
826 inc1 = inc1 * (2 * STEP_C1) + STEP_C1 * STEP_C1;
827 inc2 = inc2 * (2 * STEP_C2) + STEP_C2 * STEP_C2;
828 /* Now loop over all cells in box, updating distance per Thomas method */
829 bptr = bestdist;
830 cptr = bestcolor;
831 xx0 = inc0;
832 for (ic0 = BOX_C0_ELEMS-1; ic0 >= 0; ic0--) {
833 dist1 = dist0;
834 xx1 = inc1;
835 for (ic1 = BOX_C1_ELEMS-1; ic1 >= 0; ic1--) {
836 dist2 = dist1;
837 xx2 = inc2;
838 for (ic2 = BOX_C2_ELEMS-1; ic2 >= 0; ic2--) {
839 if (dist2 < *bptr) {
840 *bptr = dist2;
841 *cptr = (JSAMPLE) icolor;
842 }
843 dist2 += xx2;
844 xx2 += 2 * STEP_C2 * STEP_C2;
845 bptr++;
846 cptr++;
847 }
848 dist1 += xx1;
849 xx1 += 2 * STEP_C1 * STEP_C1;
850 }
851 dist0 += xx0;
852 xx0 += 2 * STEP_C0 * STEP_C0;
853 }
854 }
855}
856
857
858LOCAL(void)
859fill_inverse_cmap (j_decompress_ptr cinfo, int c0, int c1, int c2)
860/* Fill the inverse-colormap entries in the update box that contains */
861/* histogram cell c0/c1/c2. (Only that one cell MUST be filled, but */
862/* we can fill as many others as we wish.) */
863{
864 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
865 hist3d histogram = cquantize->histogram;
866 int minc0, minc1, minc2; /* lower left corner of update box */
867 int ic0, ic1, ic2;
868 register JSAMPLE * cptr; /* pointer into bestcolor[] array */
869 register histptr cachep; /* pointer into main cache array */
870 /* This array lists the candidate colormap indexes. */
871 JSAMPLE colorlist[MAXNUMCOLORS];
872 int numcolors; /* number of candidate colors */
873 /* This array holds the actually closest colormap index for each cell. */
874 JSAMPLE bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
875
876 /* Convert cell coordinates to update box ID */
877 c0 >>= BOX_C0_LOG;
878 c1 >>= BOX_C1_LOG;
879 c2 >>= BOX_C2_LOG;
880
881 /* Compute true coordinates of update box's origin corner.
882 * Actually we compute the coordinates of the center of the corner
883 * histogram cell, which are the lower bounds of the volume we care about.
884 */
885 minc0 = (c0 << BOX_C0_SHIFT) + ((1 << C0_SHIFT) >> 1);
886 minc1 = (c1 << BOX_C1_SHIFT) + ((1 << C1_SHIFT) >> 1);
887 minc2 = (c2 << BOX_C2_SHIFT) + ((1 << C2_SHIFT) >> 1);
888
889 /* Determine which colormap entries are close enough to be candidates
890 * for the nearest entry to some cell in the update box.
891 */
892 numcolors = find_nearby_colors(cinfo, minc0, minc1, minc2, colorlist);
893
894 /* Determine the actually nearest colors. */
895 find_best_colors(cinfo, minc0, minc1, minc2, numcolors, colorlist,
896 bestcolor);
897
898 /* Save the best color numbers (plus 1) in the main cache array */
899 c0 <<= BOX_C0_LOG; /* convert ID back to base cell indexes */
900 c1 <<= BOX_C1_LOG;
901 c2 <<= BOX_C2_LOG;
902 cptr = bestcolor;
903 for (ic0 = 0; ic0 < BOX_C0_ELEMS; ic0++) {
904 for (ic1 = 0; ic1 < BOX_C1_ELEMS; ic1++) {
905 cachep = & histogram[c0+ic0][c1+ic1][c2];
906 for (ic2 = 0; ic2 < BOX_C2_ELEMS; ic2++) {
907 *cachep++ = (histcell) (GETJSAMPLE(*cptr++) + 1);
908 }
909 }
910 }
911}
912
913
914/*
915 * Map some rows of pixels to the output colormapped representation.
916 */
917
918METHODDEF(void)
919pass2_no_dither (j_decompress_ptr cinfo,
920 JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)
921/* This version performs no dithering */
922{
923 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
924 hist3d histogram = cquantize->histogram;
925 register JSAMPROW inptr, outptr;
926 register histptr cachep;
927 register int c0, c1, c2;
928 int row;
929 JDIMENSION col;
930 JDIMENSION width = cinfo->output_width;
931
932 for (row = 0; row < num_rows; row++) {
933 inptr = input_buf[row];
934 outptr = output_buf[row];
935 for (col = width; col > 0; col--) {
936 /* get pixel value and index into the cache */
937 c0 = GETJSAMPLE(*inptr++) >> C0_SHIFT;
938 c1 = GETJSAMPLE(*inptr++) >> C1_SHIFT;
939 c2 = GETJSAMPLE(*inptr++) >> C2_SHIFT;
940 cachep = & histogram[c0][c1][c2];
941 /* If we have not seen this color before, find nearest colormap entry */
942 /* and update the cache */
943 if (*cachep == 0)
944 fill_inverse_cmap(cinfo, c0,c1,c2);
945 /* Now emit the colormap index for this cell */
946 *outptr++ = (JSAMPLE) (*cachep - 1);
947 }
948 }
949}
950
951
952METHODDEF(void)
953pass2_fs_dither (j_decompress_ptr cinfo,
954 JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows)
955/* This version performs Floyd-Steinberg dithering */
956{
957 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
958 hist3d histogram = cquantize->histogram;
959 register LOCFSERROR cur0, cur1, cur2; /* current error or pixel value */
960 LOCFSERROR belowerr0, belowerr1, belowerr2; /* error for pixel below cur */
961 LOCFSERROR bpreverr0, bpreverr1, bpreverr2; /* error for below/prev col */
962 register FSERRPTR errorptr; /* => fserrors[] at column before current */
963 JSAMPROW inptr; /* => current input pixel */
964 JSAMPROW outptr; /* => current output pixel */
965 histptr cachep;
966 int dir; /* +1 or -1 depending on direction */
967 int dir3; /* 3*dir, for advancing inptr & errorptr */
968 int row;
969 JDIMENSION col;
970 JDIMENSION width = cinfo->output_width;
971 JSAMPLE *range_limit = cinfo->sample_range_limit;
972 int *error_limit = cquantize->error_limiter;
973 JSAMPROW colormap0 = cinfo->colormap[0];
974 JSAMPROW colormap1 = cinfo->colormap[1];
975 JSAMPROW colormap2 = cinfo->colormap[2];
976 SHIFT_TEMPS
977
978 for (row = 0; row < num_rows; row++) {
979 inptr = input_buf[row];
980 outptr = output_buf[row];
981 if (cquantize->on_odd_row) {
982 /* work right to left in this row */
983 inptr += (width-1) * 3; /* so point to rightmost pixel */
984 outptr += width-1;
985 dir = -1;
986 dir3 = -3;
987 errorptr = cquantize->fserrors + (width+1)*3; /* => entry after last column */
988 cquantize->on_odd_row = FALSE; /* flip for next time */
989 } else {
990 /* work left to right in this row */
991 dir = 1;
992 dir3 = 3;
993 errorptr = cquantize->fserrors; /* => entry before first real column */
994 cquantize->on_odd_row = TRUE; /* flip for next time */
995 }
996 /* Preset error values: no error propagated to first pixel from left */
997 cur0 = cur1 = cur2 = 0;
998 /* and no error propagated to row below yet */
999 belowerr0 = belowerr1 = belowerr2 = 0;
1000 bpreverr0 = bpreverr1 = bpreverr2 = 0;
1001
1002 for (col = width; col > 0; col--) {
1003 /* curN holds the error propagated from the previous pixel on the
1004 * current line. Add the error propagated from the previous line
1005 * to form the complete error correction term for this pixel, and
1006 * round the error term (which is expressed * 16) to an integer.
1007 * RIGHT_SHIFT rounds towards minus infinity, so adding 8 is correct
1008 * for either sign of the error value.
1009 * Note: errorptr points to *previous* column's array entry.
1010 */
1011 cur0 = RIGHT_SHIFT(cur0 + errorptr[dir3+0] + 8, 4);
1012 cur1 = RIGHT_SHIFT(cur1 + errorptr[dir3+1] + 8, 4);
1013 cur2 = RIGHT_SHIFT(cur2 + errorptr[dir3+2] + 8, 4);
1014 /* Limit the error using transfer function set by init_error_limit.
1015 * See comments with init_error_limit for rationale.
1016 */
1017 cur0 = error_limit[cur0];
1018 cur1 = error_limit[cur1];
1019 cur2 = error_limit[cur2];
1020 /* Form pixel value + error, and range-limit to 0..MAXJSAMPLE.
1021 * The maximum error is +- MAXJSAMPLE (or less with error limiting);
1022 * this sets the required size of the range_limit array.
1023 */
1024 cur0 += GETJSAMPLE(inptr[0]);
1025 cur1 += GETJSAMPLE(inptr[1]);
1026 cur2 += GETJSAMPLE(inptr[2]);
1027 cur0 = GETJSAMPLE(range_limit[cur0]);
1028 cur1 = GETJSAMPLE(range_limit[cur1]);
1029 cur2 = GETJSAMPLE(range_limit[cur2]);
1030 /* Index into the cache with adjusted pixel value */
1031 cachep = & histogram[cur0>>C0_SHIFT][cur1>>C1_SHIFT][cur2>>C2_SHIFT];
1032 /* If we have not seen this color before, find nearest colormap */
1033 /* entry and update the cache */
1034 if (*cachep == 0)
1035 fill_inverse_cmap(cinfo, cur0>>C0_SHIFT,cur1>>C1_SHIFT,cur2>>C2_SHIFT);
1036 /* Now emit the colormap index for this cell */
1037 { register int pixcode = *cachep - 1;
1038 *outptr = (JSAMPLE) pixcode;
1039 /* Compute representation error for this pixel */
1040 cur0 -= GETJSAMPLE(colormap0[pixcode]);
1041 cur1 -= GETJSAMPLE(colormap1[pixcode]);
1042 cur2 -= GETJSAMPLE(colormap2[pixcode]);
1043 }
1044 /* Compute error fractions to be propagated to adjacent pixels.
1045 * Add these into the running sums, and simultaneously shift the
1046 * next-line error sums left by 1 column.
1047 */
1048 { register LOCFSERROR bnexterr, delta;
1049
1050 bnexterr = cur0; /* Process component 0 */
1051 delta = cur0 * 2;
1052 cur0 += delta; /* form error * 3 */
1053 errorptr[0] = (FSERROR) (bpreverr0 + cur0);
1054 cur0 += delta; /* form error * 5 */
1055 bpreverr0 = belowerr0 + cur0;
1056 belowerr0 = bnexterr;
1057 cur0 += delta; /* form error * 7 */
1058 bnexterr = cur1; /* Process component 1 */
1059 delta = cur1 * 2;
1060 cur1 += delta; /* form error * 3 */
1061 errorptr[1] = (FSERROR) (bpreverr1 + cur1);
1062 cur1 += delta; /* form error * 5 */
1063 bpreverr1 = belowerr1 + cur1;
1064 belowerr1 = bnexterr;
1065 cur1 += delta; /* form error * 7 */
1066 bnexterr = cur2; /* Process component 2 */
1067 delta = cur2 * 2;
1068 cur2 += delta; /* form error * 3 */
1069 errorptr[2] = (FSERROR) (bpreverr2 + cur2);
1070 cur2 += delta; /* form error * 5 */
1071 bpreverr2 = belowerr2 + cur2;
1072 belowerr2 = bnexterr;
1073 cur2 += delta; /* form error * 7 */
1074 }
1075 /* At this point curN contains the 7/16 error value to be propagated
1076 * to the next pixel on the current line, and all the errors for the
1077 * next line have been shifted over. We are therefore ready to move on.
1078 */
1079 inptr += dir3; /* Advance pixel pointers to next column */
1080 outptr += dir;
1081 errorptr += dir3; /* advance errorptr to current column */
1082 }
1083 /* Post-loop cleanup: we must unload the final error values into the
1084 * final fserrors[] entry. Note we need not unload belowerrN because
1085 * it is for the dummy column before or after the actual array.
1086 */
1087 errorptr[0] = (FSERROR) bpreverr0; /* unload prev errs into array */
1088 errorptr[1] = (FSERROR) bpreverr1;
1089 errorptr[2] = (FSERROR) bpreverr2;
1090 }
1091}
1092
1093
1094/*
1095 * Initialize the error-limiting transfer function (lookup table).
1096 * The raw F-S error computation can potentially compute error values of up to
1097 * +- MAXJSAMPLE. But we want the maximum correction applied to a pixel to be
1098 * much less, otherwise obviously wrong pixels will be created. (Typical
1099 * effects include weird fringes at color-area boundaries, isolated bright
1100 * pixels in a dark area, etc.) The standard advice for avoiding this problem
1101 * is to ensure that the "corners" of the color cube are allocated as output
1102 * colors; then repeated errors in the same direction cannot cause cascading
1103 * error buildup. However, that only prevents the error from getting
1104 * completely out of hand; Aaron Giles reports that error limiting improves
1105 * the results even with corner colors allocated.
1106 * A simple clamping of the error values to about +- MAXJSAMPLE/8 works pretty
1107 * well, but the smoother transfer function used below is even better. Thanks
1108 * to Aaron Giles for this idea.
1109 */
1110
1111LOCAL(void)
1112init_error_limit (j_decompress_ptr cinfo)
1113/* Allocate and fill in the error_limiter table */
1114{
1115 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1116 int * table;
1117 int in, out;
1118
1119 table = (int *) (*cinfo->mem->alloc_small)
1120 ((j_common_ptr) cinfo, JPOOL_IMAGE, (MAXJSAMPLE*2+1) * SIZEOF(int));
1121 table += MAXJSAMPLE; /* so can index -MAXJSAMPLE .. +MAXJSAMPLE */
1122 cquantize->error_limiter = table;
1123
1124#define STEPSIZE ((MAXJSAMPLE+1)/16)
1125 /* Map errors 1:1 up to +- MAXJSAMPLE/16 */
1126 out = 0;
1127 for (in = 0; in < STEPSIZE; in++, out++) {
1128 table[in] = out; table[-in] = -out;
1129 }
1130 /* Map errors 1:2 up to +- 3*MAXJSAMPLE/16 */
1131 for (; in < STEPSIZE*3; in++, out += (in&1) ? 0 : 1) {
1132 table[in] = out; table[-in] = -out;
1133 }
1134 /* Clamp the rest to final out value (which is (MAXJSAMPLE+1)/8) */
1135 for (; in <= MAXJSAMPLE; in++) {
1136 table[in] = out; table[-in] = -out;
1137 }
1138#undef STEPSIZE
1139}
1140
1141
1142/*
1143 * Finish up at the end of each pass.
1144 */
1145
1146METHODDEF(void)
1147finish_pass1 (j_decompress_ptr cinfo)
1148{
1149 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1150
1151 /* Select the representative colors and fill in cinfo->colormap */
1152 cinfo->colormap = cquantize->sv_colormap;
1153 select_colors(cinfo, cquantize->desired);
1154 /* Force next pass to zero the color index table */
1155 cquantize->needs_zeroed = TRUE;
1156}
1157
1158
1159METHODDEF(void)
1160finish_pass2 (j_decompress_ptr cinfo)
1161{
1162 /* no work */
1163}
1164
1165
1166/*
1167 * Initialize for each processing pass.
1168 */
1169
1170METHODDEF(void)
1171start_pass_2_quant (j_decompress_ptr cinfo, boolean is_pre_scan)
1172{
1173 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1174 hist3d histogram = cquantize->histogram;
1175 int i;
1176
1177 /* Only F-S dithering or no dithering is supported. */
1178 /* If user asks for ordered dither, give him F-S. */
1179 if (cinfo->dither_mode != JDITHER_NONE)
1180 cinfo->dither_mode = JDITHER_FS;
1181
1182 if (is_pre_scan) {
1183 /* Set up method pointers */
1184 cquantize->pub.color_quantize = prescan_quantize;
1185 cquantize->pub.finish_pass = finish_pass1;
1186 cquantize->needs_zeroed = TRUE; /* Always zero histogram */
1187 } else {
1188 /* Set up method pointers */
1189 if (cinfo->dither_mode == JDITHER_FS)
1190 cquantize->pub.color_quantize = pass2_fs_dither;
1191 else
1192 cquantize->pub.color_quantize = pass2_no_dither;
1193 cquantize->pub.finish_pass = finish_pass2;
1194
1195 /* Make sure color count is acceptable */
1196 i = cinfo->actual_number_of_colors;
1197 if (i < 1)
1198 ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 1);
1199 if (i > MAXNUMCOLORS)
1200 ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
1201
1202 if (cinfo->dither_mode == JDITHER_FS) {
1203 size_t arraysize = (size_t) ((cinfo->output_width + 2) *
1204 (3 * SIZEOF(FSERROR)));
1205 /* Allocate Floyd-Steinberg workspace if we didn't already. */
1206 if (cquantize->fserrors == NULL)
1207 cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)
1208 ((j_common_ptr) cinfo, JPOOL_IMAGE, arraysize);
1209 /* Initialize the propagated errors to zero. */
1210 jzero_far((void FAR *) cquantize->fserrors, arraysize);
1211 /* Make the error-limit table if we didn't already. */
1212 if (cquantize->error_limiter == NULL)
1213 init_error_limit(cinfo);
1214 cquantize->on_odd_row = FALSE;
1215 }
1216
1217 }
1218 /* Zero the histogram or inverse color map, if necessary */
1219 if (cquantize->needs_zeroed) {
1220 for (i = 0; i < HIST_C0_ELEMS; i++) {
1221 jzero_far((void FAR *) histogram[i],
1222 HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));
1223 }
1224 cquantize->needs_zeroed = FALSE;
1225 }
1226}
1227
1228
1229/*
1230 * Switch to a new external colormap between output passes.
1231 */
1232
1233METHODDEF(void)
1234new_color_map_2_quant (j_decompress_ptr cinfo)
1235{
1236 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
1237
1238 /* Reset the inverse color map */
1239 cquantize->needs_zeroed = TRUE;
1240}
1241
1242
1243/*
1244 * Module initialization routine for 2-pass color quantization.
1245 */
1246
1247GLOBAL(void)
1248jinit_2pass_quantizer (j_decompress_ptr cinfo)
1249{
1250 my_cquantize_ptr cquantize;
1251 int i;
1252
1253 cquantize = (my_cquantize_ptr)
1254 (*cinfo->mem->alloc_small) ((j_common_ptr) cinfo, JPOOL_IMAGE,
1255 SIZEOF(my_cquantizer));
1256 cinfo->cquantize = (struct jpeg_color_quantizer *) cquantize;
1257 cquantize->pub.start_pass = start_pass_2_quant;
1258 cquantize->pub.new_color_map = new_color_map_2_quant;
1259 cquantize->fserrors = NULL; /* flag optional arrays not allocated */
1260 cquantize->error_limiter = NULL;
1261
1262 /* Make sure jdmaster didn't give me a case I can't handle */
1263 if (cinfo->out_color_components != 3)
1264 ERREXIT(cinfo, JERR_NOTIMPL);
1265
1266 /* Allocate the histogram/inverse colormap storage */
1267 cquantize->histogram = (hist3d) (*cinfo->mem->alloc_small)
1268 ((j_common_ptr) cinfo, JPOOL_IMAGE, HIST_C0_ELEMS * SIZEOF(hist2d));
1269 for (i = 0; i < HIST_C0_ELEMS; i++) {
1270 cquantize->histogram[i] = (hist2d) (*cinfo->mem->alloc_large)
1271 ((j_common_ptr) cinfo, JPOOL_IMAGE,
1272 HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));
1273 }
1274 cquantize->needs_zeroed = TRUE; /* histogram is garbage now */
1275
1276 /* Allocate storage for the completed colormap, if required.
1277 * We do this now since it is FAR storage and may affect
1278 * the memory manager's space calculations.
1279 */
1280 if (cinfo->enable_2pass_quant) {
1281 /* Make sure color count is acceptable */
1282 int desired = cinfo->desired_number_of_colors;
1283 /* Lower bound on # of colors ... somewhat arbitrary as long as > 0 */
1284 if (desired < 8)
1285 ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 8);
1286 /* Make sure colormap indexes can be represented by JSAMPLEs */
1287 if (desired > MAXNUMCOLORS)
1288 ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS);
1289 cquantize->sv_colormap = (*cinfo->mem->alloc_sarray)
1290 ((j_common_ptr) cinfo,JPOOL_IMAGE, (JDIMENSION) desired, (JDIMENSION) 3);
1291 cquantize->desired = desired;
1292 } else
1293 cquantize->sv_colormap = NULL;
1294
1295 /* Only F-S dithering or no dithering is supported. */
1296 /* If user asks for ordered dither, give him F-S. */
1297 if (cinfo->dither_mode != JDITHER_NONE)
1298 cinfo->dither_mode = JDITHER_FS;
1299
1300 /* Allocate Floyd-Steinberg workspace if necessary.
1301 * This isn't really needed until pass 2, but again it is FAR storage.
1302 * Although we will cope with a later change in dither_mode,
1303 * we do not promise to honor max_memory_to_use if dither_mode changes.
1304 */
1305 if (cinfo->dither_mode == JDITHER_FS) {
1306 cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large)
1307 ((j_common_ptr) cinfo, JPOOL_IMAGE,
1308 (size_t) ((cinfo->output_width + 2) * (3 * SIZEOF(FSERROR))));
1309 /* Might as well create the error-limiting table too. */
1310 init_error_limit(cinfo);
1311 }
1312}
1313
1314#endif /* QUANT_2PASS_SUPPORTED */
1315