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- Committer: Bazaar Package Importer
- Author(s): Aaron M. Ucko
- Date: 2004-12-04 12:59:54 UTC
- mfrom: (1.1.1 upstream)
- Revision ID: james.westby@ubuntu.com-20041204125954-0bnefh426a67cjgl
Tags: 1.1.6-2
test/*.cxx: Apply patch from Andreas Jochens <aj@andaco.de> to fix
bogus pointer->int casts, which GCC 4.0 rightly treats as errors on
64-bit systems. (Closes: #284168.)
bogus pointer->int casts, which GCC 4.0 rightly treats as errors on
64-bit systems. (Closes: #284168.)
- files added:
- CMake
- jpeg
- png
- watcom
- zlib
- files removed:
- src/fl_set_gray.cxx
- files modified:
- ANNOUNCEMENT
added
removed
jpeg/jquant2.c
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
211
typedef my_cquantizer * my_cquantize_ptr;
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)
224
prescan_quantize (j_decompress_ptr cinfo, JSAMPARRAY input_buf,
225
JSAMPARRAY output_buf, int num_rows)
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 */
264
INT32 volume;
265
/* The number of nonzero histogram cells within this box */
266
long colorcount;
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)
313
update_box (j_decompress_ptr cinfo, boxptr boxp)
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 */
794
INT32 bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
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. */
870
JSAMPLE bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS];
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)
915
pass2_no_dither (j_decompress_ptr cinfo,
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)
949
pass2_fs_dither (j_decompress_ptr cinfo,
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];
972
SHIFT_TEMPS
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)
1108
init_error_limit (j_decompress_ptr cinfo)
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)
1143
finish_pass1 (j_decompress_ptr cinfo)
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)
1156
finish_pass2 (j_decompress_ptr cinfo)
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],
1218
HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));
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)
1230
new_color_map_2_quant (j_decompress_ptr cinfo)
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)
1244
jinit_2pass_quantizer (j_decompress_ptr cinfo)
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,
1251
SIZEOF(my_cquantizer));
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)
1264
((j_common_ptr) cinfo, JPOOL_IMAGE, HIST_C0_ELEMS * SIZEOF(hist2d));
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,
1268
HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell));
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)
1286
((j_common_ptr) cinfo,JPOOL_IMAGE, (JDIMENSION) desired, (JDIMENSION) 3);
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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