1 /* 2 * jquant2.c 3 * 4 * This file was part of the Independent JPEG Group's software: 5 * Copyright (C) 1991-1996, Thomas G. Lane. 6 * libjpeg-turbo Modifications: 7 * Copyright (C) 2009, 2014-2015, D. R. Commander. 8 * For conditions of distribution and use, see the accompanying README.ijg 9 * file. 10 * 11 * This file contains 2-pass color quantization (color mapping) routines. 12 * These routines provide selection of a custom color map for an image, 13 * followed by mapping of the image to that color map, with optional 14 * Floyd-Steinberg dithering. 15 * It is also possible to use just the second pass to map to an arbitrary 16 * externally-given color map. 17 * 18 * Note: ordered dithering is not supported, since there isn't any fast 19 * way to compute intercolor distances; it's unclear that ordered dither's 20 * fundamental assumptions even hold with an irregularly spaced color map. 21 */ 22 23 #define JPEG_INTERNALS 24 #include "jinclude.h" 25 #include "jpeglib.h" 26 27 #ifdef QUANT_2PASS_SUPPORTED 28 29 30 /* 31 * This module implements the well-known Heckbert paradigm for color 32 * quantization. Most of the ideas used here can be traced back to 33 * Heckbert's seminal paper 34 * Heckbert, Paul. "Color Image Quantization for Frame Buffer Display", 35 * Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304. 36 * 37 * In the first pass over the image, we accumulate a histogram showing the 38 * usage count of each possible color. To keep the histogram to a reasonable 39 * size, we reduce the precision of the input; typical practice is to retain 40 * 5 or 6 bits per color, so that 8 or 4 different input values are counted 41 * in the same histogram cell. 42 * 43 * Next, the color-selection step begins with a box representing the whole 44 * color space, and repeatedly splits the "largest" remaining box until we 45 * have as many boxes as desired colors. Then the mean color in each 46 * remaining box becomes one of the possible output colors. 47 * 48 * The second pass over the image maps each input pixel to the closest output 49 * color (optionally after applying a Floyd-Steinberg dithering correction). 50 * This mapping is logically trivial, but making it go fast enough requires 51 * considerable care. 52 * 53 * Heckbert-style quantizers vary a good deal in their policies for choosing 54 * the "largest" box and deciding where to cut it. The particular policies 55 * used here have proved out well in experimental comparisons, but better ones 56 * may yet be found. 57 * 58 * In earlier versions of the IJG code, this module quantized in YCbCr color 59 * space, processing the raw upsampled data without a color conversion step. 60 * This allowed the color conversion math to be done only once per colormap 61 * entry, not once per pixel. However, that optimization precluded other 62 * useful optimizations (such as merging color conversion with upsampling) 63 * and it also interfered with desired capabilities such as quantizing to an 64 * externally-supplied colormap. We have therefore abandoned that approach. 65 * The present code works in the post-conversion color space, typically RGB. 66 * 67 * To improve the visual quality of the results, we actually work in scaled 68 * RGB space, giving G distances more weight than R, and R in turn more than 69 * B. To do everything in integer math, we must use integer scale factors. 70 * The 2/3/1 scale factors used here correspond loosely to the relative 71 * weights of the colors in the NTSC grayscale equation. 72 * If you want to use this code to quantize a non-RGB color space, you'll 73 * probably need to change these scale factors. 74 */ 75 76 #define R_SCALE 2 /* scale R distances by this much */ 77 #define G_SCALE 3 /* scale G distances by this much */ 78 #define B_SCALE 1 /* and B by this much */ 79 80 static const int c_scales[3]={R_SCALE, G_SCALE, B_SCALE}; 81 #define C0_SCALE c_scales[rgb_red[cinfo->out_color_space]] 82 #define C1_SCALE c_scales[rgb_green[cinfo->out_color_space]] 83 #define C2_SCALE c_scales[rgb_blue[cinfo->out_color_space]] 84 85 /* 86 * First we have the histogram data structure and routines for creating it. 87 * 88 * The number of bits of precision can be adjusted by changing these symbols. 89 * We recommend keeping 6 bits for G and 5 each for R and B. 90 * If you have plenty of memory and cycles, 6 bits all around gives marginally 91 * better results; if you are short of memory, 5 bits all around will save 92 * some space but degrade the results. 93 * To maintain a fully accurate histogram, we'd need to allocate a "long" 94 * (preferably unsigned long) for each cell. In practice this is overkill; 95 * we can get by with 16 bits per cell. Few of the cell counts will overflow, 96 * and clamping those that do overflow to the maximum value will give close- 97 * enough results. This reduces the recommended histogram size from 256Kb 98 * to 128Kb, which is a useful savings on PC-class machines. 99 * (In the second pass the histogram space is re-used for pixel mapping data; 100 * in that capacity, each cell must be able to store zero to the number of 101 * desired colors. 16 bits/cell is plenty for that too.) 102 * Since the JPEG code is intended to run in small memory model on 80x86 103 * machines, we can't just allocate the histogram in one chunk. Instead 104 * of a true 3-D array, we use a row of pointers to 2-D arrays. Each 105 * pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and 106 * each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries. 107 */ 108 109 #define MAXNUMCOLORS (MAXJSAMPLE+1) /* maximum size of colormap */ 110 111 /* These will do the right thing for either R,G,B or B,G,R color order, 112 * but you may not like the results for other color orders. 113 */ 114 #define HIST_C0_BITS 5 /* bits of precision in R/B histogram */ 115 #define HIST_C1_BITS 6 /* bits of precision in G histogram */ 116 #define HIST_C2_BITS 5 /* bits of precision in B/R histogram */ 117 118 /* Number of elements along histogram axes. */ 119 #define HIST_C0_ELEMS (1<<HIST_C0_BITS) 120 #define HIST_C1_ELEMS (1<<HIST_C1_BITS) 121 #define HIST_C2_ELEMS (1<<HIST_C2_BITS) 122 123 /* These are the amounts to shift an input value to get a histogram index. */ 124 #define C0_SHIFT (BITS_IN_JSAMPLE-HIST_C0_BITS) 125 #define C1_SHIFT (BITS_IN_JSAMPLE-HIST_C1_BITS) 126 #define C2_SHIFT (BITS_IN_JSAMPLE-HIST_C2_BITS) 127 128 129 typedef UINT16 histcell; /* histogram cell; prefer an unsigned type */ 130 131 typedef histcell *histptr; /* for pointers to histogram cells */ 132 133 typedef histcell hist1d[HIST_C2_ELEMS]; /* typedefs for the array */ 134 typedef hist1d *hist2d; /* type for the 2nd-level pointers */ 135 typedef hist2d *hist3d; /* type for top-level pointer */ 136 137 138 /* Declarations for Floyd-Steinberg dithering. 139 * 140 * Errors are accumulated into the array fserrors[], at a resolution of 141 * 1/16th of a pixel count. The error at a given pixel is propagated 142 * to its not-yet-processed neighbors using the standard F-S fractions, 143 * ... (here) 7/16 144 * 3/16 5/16 1/16 145 * We work left-to-right on even rows, right-to-left on odd rows. 146 * 147 * We can get away with a single array (holding one row's worth of errors) 148 * by using it to store the current row's errors at pixel columns not yet 149 * processed, but the next row's errors at columns already processed. We 150 * need only a few extra variables to hold the errors immediately around the 151 * current column. (If we are lucky, those variables are in registers, but 152 * even if not, they're probably cheaper to access than array elements are.) 153 * 154 * The fserrors[] array has (#columns + 2) entries; the extra entry at 155 * each end saves us from special-casing the first and last pixels. 156 * Each entry is three values long, one value for each color component. 157 */ 158 159 #if BITS_IN_JSAMPLE == 8 160 typedef INT16 FSERROR; /* 16 bits should be enough */ 161 typedef int LOCFSERROR; /* use 'int' for calculation temps */ 162 #else 163 typedef JLONG FSERROR; /* may need more than 16 bits */ 164 typedef JLONG LOCFSERROR; /* be sure calculation temps are big enough */ 165 #endif 166 167 typedef FSERROR *FSERRPTR; /* pointer to error array */ 168 169 170 /* Private subobject */ 171 172 typedef struct { 173 struct jpeg_color_quantizer pub; /* public fields */ 174 175 /* Space for the eventually created colormap is stashed here */ 176 JSAMPARRAY sv_colormap; /* colormap allocated at init time */ 177 int desired; /* desired # of colors = size of colormap */ 178 179 /* Variables for accumulating image statistics */ 180 hist3d histogram; /* pointer to the histogram */ 181 182 boolean needs_zeroed; /* TRUE if next pass must zero histogram */ 183 184 /* Variables for Floyd-Steinberg dithering */ 185 FSERRPTR fserrors; /* accumulated errors */ 186 boolean on_odd_row; /* flag to remember which row we are on */ 187 int *error_limiter; /* table for clamping the applied error */ 188 } my_cquantizer; 189 190 typedef my_cquantizer *my_cquantize_ptr; 191 192 193 /* 194 * Prescan some rows of pixels. 195 * In this module the prescan simply updates the histogram, which has been 196 * initialized to zeroes by start_pass. 197 * An output_buf parameter is required by the method signature, but no data 198 * is actually output (in fact the buffer controller is probably passing a 199 * NULL pointer). 200 */ 201 202 METHODDEF(void) 203 prescan_quantize (j_decompress_ptr cinfo, JSAMPARRAY input_buf, 204 JSAMPARRAY output_buf, int num_rows) 205 { 206 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; 207 register JSAMPROW ptr; 208 register histptr histp; 209 register hist3d histogram = cquantize->histogram; 210 int row; 211 JDIMENSION col; 212 JDIMENSION width = cinfo->output_width; 213 214 for (row = 0; row < num_rows; row++) { 215 ptr = input_buf[row]; 216 for (col = width; col > 0; col--) { 217 /* get pixel value and index into the histogram */ 218 histp = & histogram[GETJSAMPLE(ptr[0]) >> C0_SHIFT] 219 [GETJSAMPLE(ptr[1]) >> C1_SHIFT] 220 [GETJSAMPLE(ptr[2]) >> C2_SHIFT]; 221 /* increment, check for overflow and undo increment if so. */ 222 if (++(*histp) <= 0) 223 (*histp)--; 224 ptr += 3; 225 } 226 } 227 } 228 229 230 /* 231 * Next we have the really interesting routines: selection of a colormap 232 * given the completed histogram. 233 * These routines work with a list of "boxes", each representing a rectangular 234 * subset of the input color space (to histogram precision). 235 */ 236 237 typedef struct { 238 /* The bounds of the box (inclusive); expressed as histogram indexes */ 239 int c0min, c0max; 240 int c1min, c1max; 241 int c2min, c2max; 242 /* The volume (actually 2-norm) of the box */ 243 JLONG volume; 244 /* The number of nonzero histogram cells within this box */ 245 long colorcount; 246 } box; 247 248 typedef box *boxptr; 249 250 251 LOCAL(boxptr) 252 find_biggest_color_pop (boxptr boxlist, int numboxes) 253 /* Find the splittable box with the largest color population */ 254 /* Returns NULL if no splittable boxes remain */ 255 { 256 register boxptr boxp; 257 register int i; 258 register long maxc = 0; 259 boxptr which = NULL; 260 261 for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) { 262 if (boxp->colorcount > maxc && boxp->volume > 0) { 263 which = boxp; 264 maxc = boxp->colorcount; 265 } 266 } 267 return which; 268 } 269 270 271 LOCAL(boxptr) 272 find_biggest_volume (boxptr boxlist, int numboxes) 273 /* Find the splittable box with the largest (scaled) volume */ 274 /* Returns NULL if no splittable boxes remain */ 275 { 276 register boxptr boxp; 277 register int i; 278 register JLONG maxv = 0; 279 boxptr which = NULL; 280 281 for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) { 282 if (boxp->volume > maxv) { 283 which = boxp; 284 maxv = boxp->volume; 285 } 286 } 287 return which; 288 } 289 290 291 LOCAL(void) 292 update_box (j_decompress_ptr cinfo, boxptr boxp) 293 /* Shrink the min/max bounds of a box to enclose only nonzero elements, */ 294 /* and recompute its volume and population */ 295 { 296 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; 297 hist3d histogram = cquantize->histogram; 298 histptr histp; 299 int c0,c1,c2; 300 int c0min,c0max,c1min,c1max,c2min,c2max; 301 JLONG dist0,dist1,dist2; 302 long ccount; 303 304 c0min = boxp->c0min; c0max = boxp->c0max; 305 c1min = boxp->c1min; c1max = boxp->c1max; 306 c2min = boxp->c2min; c2max = boxp->c2max; 307 308 if (c0max > c0min) 309 for (c0 = c0min; c0 <= c0max; c0++) 310 for (c1 = c1min; c1 <= c1max; c1++) { 311 histp = & histogram[c0][c1][c2min]; 312 for (c2 = c2min; c2 <= c2max; c2++) 313 if (*histp++ != 0) { 314 boxp->c0min = c0min = c0; 315 goto have_c0min; 316 } 317 } 318 have_c0min: 319 if (c0max > c0min) 320 for (c0 = c0max; c0 >= c0min; c0--) 321 for (c1 = c1min; c1 <= c1max; c1++) { 322 histp = & histogram[c0][c1][c2min]; 323 for (c2 = c2min; c2 <= c2max; c2++) 324 if (*histp++ != 0) { 325 boxp->c0max = c0max = c0; 326 goto have_c0max; 327 } 328 } 329 have_c0max: 330 if (c1max > c1min) 331 for (c1 = c1min; c1 <= c1max; c1++) 332 for (c0 = c0min; c0 <= c0max; c0++) { 333 histp = & histogram[c0][c1][c2min]; 334 for (c2 = c2min; c2 <= c2max; c2++) 335 if (*histp++ != 0) { 336 boxp->c1min = c1min = c1; 337 goto have_c1min; 338 } 339 } 340 have_c1min: 341 if (c1max > c1min) 342 for (c1 = c1max; c1 >= c1min; c1--) 343 for (c0 = c0min; c0 <= c0max; c0++) { 344 histp = & histogram[c0][c1][c2min]; 345 for (c2 = c2min; c2 <= c2max; c2++) 346 if (*histp++ != 0) { 347 boxp->c1max = c1max = c1; 348 goto have_c1max; 349 } 350 } 351 have_c1max: 352 if (c2max > c2min) 353 for (c2 = c2min; c2 <= c2max; c2++) 354 for (c0 = c0min; c0 <= c0max; c0++) { 355 histp = & histogram[c0][c1min][c2]; 356 for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS) 357 if (*histp != 0) { 358 boxp->c2min = c2min = c2; 359 goto have_c2min; 360 } 361 } 362 have_c2min: 363 if (c2max > c2min) 364 for (c2 = c2max; c2 >= c2min; c2--) 365 for (c0 = c0min; c0 <= c0max; c0++) { 366 histp = & histogram[c0][c1min][c2]; 367 for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS) 368 if (*histp != 0) { 369 boxp->c2max = c2max = c2; 370 goto have_c2max; 371 } 372 } 373 have_c2max: 374 375 /* Update box volume. 376 * We use 2-norm rather than real volume here; this biases the method 377 * against making long narrow boxes, and it has the side benefit that 378 * a box is splittable iff norm > 0. 379 * Since the differences are expressed in histogram-cell units, 380 * we have to shift back to JSAMPLE units to get consistent distances; 381 * after which, we scale according to the selected distance scale factors. 382 */ 383 dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE; 384 dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE; 385 dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE; 386 boxp->volume = dist0*dist0 + dist1*dist1 + dist2*dist2; 387 388 /* Now scan remaining volume of box and compute population */ 389 ccount = 0; 390 for (c0 = c0min; c0 <= c0max; c0++) 391 for (c1 = c1min; c1 <= c1max; c1++) { 392 histp = & histogram[c0][c1][c2min]; 393 for (c2 = c2min; c2 <= c2max; c2++, histp++) 394 if (*histp != 0) { 395 ccount++; 396 } 397 } 398 boxp->colorcount = ccount; 399 } 400 401 402 LOCAL(int) 403 median_cut (j_decompress_ptr cinfo, boxptr boxlist, int numboxes, 404 int desired_colors) 405 /* Repeatedly select and split the largest box until we have enough boxes */ 406 { 407 int n,lb; 408 int c0,c1,c2,cmax; 409 register boxptr b1,b2; 410 411 while (numboxes < desired_colors) { 412 /* Select box to split. 413 * Current algorithm: by population for first half, then by volume. 414 */ 415 if (numboxes*2 <= desired_colors) { 416 b1 = find_biggest_color_pop(boxlist, numboxes); 417 } else { 418 b1 = find_biggest_volume(boxlist, numboxes); 419 } 420 if (b1 == NULL) /* no splittable boxes left! */ 421 break; 422 b2 = &boxlist[numboxes]; /* where new box will go */ 423 /* Copy the color bounds to the new box. */ 424 b2->c0max = b1->c0max; b2->c1max = b1->c1max; b2->c2max = b1->c2max; 425 b2->c0min = b1->c0min; b2->c1min = b1->c1min; b2->c2min = b1->c2min; 426 /* Choose which axis to split the box on. 427 * Current algorithm: longest scaled axis. 428 * See notes in update_box about scaling distances. 429 */ 430 c0 = ((b1->c0max - b1->c0min) << C0_SHIFT) * C0_SCALE; 431 c1 = ((b1->c1max - b1->c1min) << C1_SHIFT) * C1_SCALE; 432 c2 = ((b1->c2max - b1->c2min) << C2_SHIFT) * C2_SCALE; 433 /* We want to break any ties in favor of green, then red, blue last. 434 * This code does the right thing for R,G,B or B,G,R color orders only. 435 */ 436 if (rgb_red[cinfo->out_color_space] == 0) { 437 cmax = c1; n = 1; 438 if (c0 > cmax) { cmax = c0; n = 0; } 439 if (c2 > cmax) { n = 2; } 440 } 441 else { 442 cmax = c1; n = 1; 443 if (c2 > cmax) { cmax = c2; n = 2; } 444 if (c0 > cmax) { n = 0; } 445 } 446 /* Choose split point along selected axis, and update box bounds. 447 * Current algorithm: split at halfway point. 448 * (Since the box has been shrunk to minimum volume, 449 * any split will produce two nonempty subboxes.) 450 * Note that lb value is max for lower box, so must be < old max. 451 */ 452 switch (n) { 453 case 0: 454 lb = (b1->c0max + b1->c0min) / 2; 455 b1->c0max = lb; 456 b2->c0min = lb+1; 457 break; 458 case 1: 459 lb = (b1->c1max + b1->c1min) / 2; 460 b1->c1max = lb; 461 b2->c1min = lb+1; 462 break; 463 case 2: 464 lb = (b1->c2max + b1->c2min) / 2; 465 b1->c2max = lb; 466 b2->c2min = lb+1; 467 break; 468 } 469 /* Update stats for boxes */ 470 update_box(cinfo, b1); 471 update_box(cinfo, b2); 472 numboxes++; 473 } 474 return numboxes; 475 } 476 477 478 LOCAL(void) 479 compute_color (j_decompress_ptr cinfo, boxptr boxp, int icolor) 480 /* Compute representative color for a box, put it in colormap[icolor] */ 481 { 482 /* Current algorithm: mean weighted by pixels (not colors) */ 483 /* Note it is important to get the rounding correct! */ 484 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; 485 hist3d histogram = cquantize->histogram; 486 histptr histp; 487 int c0,c1,c2; 488 int c0min,c0max,c1min,c1max,c2min,c2max; 489 long count; 490 long total = 0; 491 long c0total = 0; 492 long c1total = 0; 493 long c2total = 0; 494 495 c0min = boxp->c0min; c0max = boxp->c0max; 496 c1min = boxp->c1min; c1max = boxp->c1max; 497 c2min = boxp->c2min; c2max = boxp->c2max; 498 499 for (c0 = c0min; c0 <= c0max; c0++) 500 for (c1 = c1min; c1 <= c1max; c1++) { 501 histp = & histogram[c0][c1][c2min]; 502 for (c2 = c2min; c2 <= c2max; c2++) { 503 if ((count = *histp++) != 0) { 504 total += count; 505 c0total += ((c0 << C0_SHIFT) + ((1<<C0_SHIFT)>>1)) * count; 506 c1total += ((c1 << C1_SHIFT) + ((1<<C1_SHIFT)>>1)) * count; 507 c2total += ((c2 << C2_SHIFT) + ((1<<C2_SHIFT)>>1)) * count; 508 } 509 } 510 } 511 512 cinfo->colormap[0][icolor] = (JSAMPLE) ((c0total + (total>>1)) / total); 513 cinfo->colormap[1][icolor] = (JSAMPLE) ((c1total + (total>>1)) / total); 514 cinfo->colormap[2][icolor] = (JSAMPLE) ((c2total + (total>>1)) / total); 515 } 516 517 518 LOCAL(void) 519 select_colors (j_decompress_ptr cinfo, int desired_colors) 520 /* Master routine for color selection */ 521 { 522 boxptr boxlist; 523 int numboxes; 524 int i; 525 526 /* Allocate workspace for box list */ 527 boxlist = (boxptr) (*cinfo->mem->alloc_small) 528 ((j_common_ptr) cinfo, JPOOL_IMAGE, desired_colors * sizeof(box)); 529 /* Initialize one box containing whole space */ 530 numboxes = 1; 531 boxlist[0].c0min = 0; 532 boxlist[0].c0max = MAXJSAMPLE >> C0_SHIFT; 533 boxlist[0].c1min = 0; 534 boxlist[0].c1max = MAXJSAMPLE >> C1_SHIFT; 535 boxlist[0].c2min = 0; 536 boxlist[0].c2max = MAXJSAMPLE >> C2_SHIFT; 537 /* Shrink it to actually-used volume and set its statistics */ 538 update_box(cinfo, & boxlist[0]); 539 /* Perform median-cut to produce final box list */ 540 numboxes = median_cut(cinfo, boxlist, numboxes, desired_colors); 541 /* Compute the representative color for each box, fill colormap */ 542 for (i = 0; i < numboxes; i++) 543 compute_color(cinfo, & boxlist[i], i); 544 cinfo->actual_number_of_colors = numboxes; 545 TRACEMS1(cinfo, 1, JTRC_QUANT_SELECTED, numboxes); 546 } 547 548 549 /* 550 * These routines are concerned with the time-critical task of mapping input 551 * colors to the nearest color in the selected colormap. 552 * 553 * We re-use the histogram space as an "inverse color map", essentially a 554 * cache for the results of nearest-color searches. All colors within a 555 * histogram cell will be mapped to the same colormap entry, namely the one 556 * closest to the cell's center. This may not be quite the closest entry to 557 * the actual input color, but it's almost as good. A zero in the cache 558 * indicates we haven't found the nearest color for that cell yet; the array 559 * is cleared to zeroes before starting the mapping pass. When we find the 560 * nearest color for a cell, its colormap index plus one is recorded in the 561 * cache for future use. The pass2 scanning routines call fill_inverse_cmap 562 * when they need to use an unfilled entry in the cache. 563 * 564 * Our method of efficiently finding nearest colors is based on the "locally 565 * sorted search" idea described by Heckbert and on the incremental distance 566 * calculation described by Spencer W. Thomas in chapter III.1 of Graphics 567 * Gems II (James Arvo, ed. Academic Press, 1991). Thomas points out that 568 * the distances from a given colormap entry to each cell of the histogram can 569 * be computed quickly using an incremental method: the differences between 570 * distances to adjacent cells themselves differ by a constant. This allows a 571 * fairly fast implementation of the "brute force" approach of computing the 572 * distance from every colormap entry to every histogram cell. Unfortunately, 573 * it needs a work array to hold the best-distance-so-far for each histogram 574 * cell (because the inner loop has to be over cells, not colormap entries). 575 * The work array elements have to be JLONGs, so the work array would need 576 * 256Kb at our recommended precision. This is not feasible in DOS machines. 577 * 578 * To get around these problems, we apply Thomas' method to compute the 579 * nearest colors for only the cells within a small subbox of the histogram. 580 * The work array need be only as big as the subbox, so the memory usage 581 * problem is solved. Furthermore, we need not fill subboxes that are never 582 * referenced in pass2; many images use only part of the color gamut, so a 583 * fair amount of work is saved. An additional advantage of this 584 * approach is that we can apply Heckbert's locality criterion to quickly 585 * eliminate colormap entries that are far away from the subbox; typically 586 * three-fourths of the colormap entries are rejected by Heckbert's criterion, 587 * and we need not compute their distances to individual cells in the subbox. 588 * The speed of this approach is heavily influenced by the subbox size: too 589 * small means too much overhead, too big loses because Heckbert's criterion 590 * can't eliminate as many colormap entries. Empirically the best subbox 591 * size seems to be about 1/512th of the histogram (1/8th in each direction). 592 * 593 * Thomas' article also describes a refined method which is asymptotically 594 * faster than the brute-force method, but it is also far more complex and 595 * cannot efficiently be applied to small subboxes. It is therefore not 596 * useful for programs intended to be portable to DOS machines. On machines 597 * with plenty of memory, filling the whole histogram in one shot with Thomas' 598 * refined method might be faster than the present code --- but then again, 599 * it might not be any faster, and it's certainly more complicated. 600 */ 601 602 603 /* log2(histogram cells in update box) for each axis; this can be adjusted */ 604 #define BOX_C0_LOG (HIST_C0_BITS-3) 605 #define BOX_C1_LOG (HIST_C1_BITS-3) 606 #define BOX_C2_LOG (HIST_C2_BITS-3) 607 608 #define BOX_C0_ELEMS (1<<BOX_C0_LOG) /* # of hist cells in update box */ 609 #define BOX_C1_ELEMS (1<<BOX_C1_LOG) 610 #define BOX_C2_ELEMS (1<<BOX_C2_LOG) 611 612 #define BOX_C0_SHIFT (C0_SHIFT + BOX_C0_LOG) 613 #define BOX_C1_SHIFT (C1_SHIFT + BOX_C1_LOG) 614 #define BOX_C2_SHIFT (C2_SHIFT + BOX_C2_LOG) 615 616 617 /* 618 * The next three routines implement inverse colormap filling. They could 619 * all be folded into one big routine, but splitting them up this way saves 620 * some stack space (the mindist[] and bestdist[] arrays need not coexist) 621 * and may allow some compilers to produce better code by registerizing more 622 * inner-loop variables. 623 */ 624 625 LOCAL(int) 626 find_nearby_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2, 627 JSAMPLE colorlist[]) 628 /* Locate the colormap entries close enough to an update box to be candidates 629 * for the nearest entry to some cell(s) in the update box. The update box 630 * is specified by the center coordinates of its first cell. The number of 631 * candidate colormap entries is returned, and their colormap indexes are 632 * placed in colorlist[]. 633 * This routine uses Heckbert's "locally sorted search" criterion to select 634 * the colors that need further consideration. 635 */ 636 { 637 int numcolors = cinfo->actual_number_of_colors; 638 int maxc0, maxc1, maxc2; 639 int centerc0, centerc1, centerc2; 640 int i, x, ncolors; 641 JLONG minmaxdist, min_dist, max_dist, tdist; 642 JLONG mindist[MAXNUMCOLORS]; /* min distance to colormap entry i */ 643 644 /* Compute true coordinates of update box's upper corner and center. 645 * Actually we compute the coordinates of the center of the upper-corner 646 * histogram cell, which are the upper bounds of the volume we care about. 647 * Note that since ">>" rounds down, the "center" values may be closer to 648 * min than to max; hence comparisons to them must be "<=", not "<". 649 */ 650 maxc0 = minc0 + ((1 << BOX_C0_SHIFT) - (1 << C0_SHIFT)); 651 centerc0 = (minc0 + maxc0) >> 1; 652 maxc1 = minc1 + ((1 << BOX_C1_SHIFT) - (1 << C1_SHIFT)); 653 centerc1 = (minc1 + maxc1) >> 1; 654 maxc2 = minc2 + ((1 << BOX_C2_SHIFT) - (1 << C2_SHIFT)); 655 centerc2 = (minc2 + maxc2) >> 1; 656 657 /* For each color in colormap, find: 658 * 1. its minimum squared-distance to any point in the update box 659 * (zero if color is within update box); 660 * 2. its maximum squared-distance to any point in the update box. 661 * Both of these can be found by considering only the corners of the box. 662 * We save the minimum distance for each color in mindist[]; 663 * only the smallest maximum distance is of interest. 664 */ 665 minmaxdist = 0x7FFFFFFFL; 666 667 for (i = 0; i < numcolors; i++) { 668 /* We compute the squared-c0-distance term, then add in the other two. */ 669 x = GETJSAMPLE(cinfo->colormap[0][i]); 670 if (x < minc0) { 671 tdist = (x - minc0) * C0_SCALE; 672 min_dist = tdist*tdist; 673 tdist = (x - maxc0) * C0_SCALE; 674 max_dist = tdist*tdist; 675 } else if (x > maxc0) { 676 tdist = (x - maxc0) * C0_SCALE; 677 min_dist = tdist*tdist; 678 tdist = (x - minc0) * C0_SCALE; 679 max_dist = tdist*tdist; 680 } else { 681 /* within cell range so no contribution to min_dist */ 682 min_dist = 0; 683 if (x <= centerc0) { 684 tdist = (x - maxc0) * C0_SCALE; 685 max_dist = tdist*tdist; 686 } else { 687 tdist = (x - minc0) * C0_SCALE; 688 max_dist = tdist*tdist; 689 } 690 } 691 692 x = GETJSAMPLE(cinfo->colormap[1][i]); 693 if (x < minc1) { 694 tdist = (x - minc1) * C1_SCALE; 695 min_dist += tdist*tdist; 696 tdist = (x - maxc1) * C1_SCALE; 697 max_dist += tdist*tdist; 698 } else if (x > maxc1) { 699 tdist = (x - maxc1) * C1_SCALE; 700 min_dist += tdist*tdist; 701 tdist = (x - minc1) * C1_SCALE; 702 max_dist += tdist*tdist; 703 } else { 704 /* within cell range so no contribution to min_dist */ 705 if (x <= centerc1) { 706 tdist = (x - maxc1) * C1_SCALE; 707 max_dist += tdist*tdist; 708 } else { 709 tdist = (x - minc1) * C1_SCALE; 710 max_dist += tdist*tdist; 711 } 712 } 713 714 x = GETJSAMPLE(cinfo->colormap[2][i]); 715 if (x < minc2) { 716 tdist = (x - minc2) * C2_SCALE; 717 min_dist += tdist*tdist; 718 tdist = (x - maxc2) * C2_SCALE; 719 max_dist += tdist*tdist; 720 } else if (x > maxc2) { 721 tdist = (x - maxc2) * C2_SCALE; 722 min_dist += tdist*tdist; 723 tdist = (x - minc2) * C2_SCALE; 724 max_dist += tdist*tdist; 725 } else { 726 /* within cell range so no contribution to min_dist */ 727 if (x <= centerc2) { 728 tdist = (x - maxc2) * C2_SCALE; 729 max_dist += tdist*tdist; 730 } else { 731 tdist = (x - minc2) * C2_SCALE; 732 max_dist += tdist*tdist; 733 } 734 } 735 736 mindist[i] = min_dist; /* save away the results */ 737 if (max_dist < minmaxdist) 738 minmaxdist = max_dist; 739 } 740 741 /* Now we know that no cell in the update box is more than minmaxdist 742 * away from some colormap entry. Therefore, only colors that are 743 * within minmaxdist of some part of the box need be considered. 744 */ 745 ncolors = 0; 746 for (i = 0; i < numcolors; i++) { 747 if (mindist[i] <= minmaxdist) 748 colorlist[ncolors++] = (JSAMPLE) i; 749 } 750 return ncolors; 751 } 752 753 754 LOCAL(void) 755 find_best_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2, 756 int numcolors, JSAMPLE colorlist[], JSAMPLE bestcolor[]) 757 /* Find the closest colormap entry for each cell in the update box, 758 * given the list of candidate colors prepared by find_nearby_colors. 759 * Return the indexes of the closest entries in the bestcolor[] array. 760 * This routine uses Thomas' incremental distance calculation method to 761 * find the distance from a colormap entry to successive cells in the box. 762 */ 763 { 764 int ic0, ic1, ic2; 765 int i, icolor; 766 register JLONG *bptr; /* pointer into bestdist[] array */ 767 JSAMPLE *cptr; /* pointer into bestcolor[] array */ 768 JLONG dist0, dist1; /* initial distance values */ 769 register JLONG dist2; /* current distance in inner loop */ 770 JLONG xx0, xx1; /* distance increments */ 771 register JLONG xx2; 772 JLONG inc0, inc1, inc2; /* initial values for increments */ 773 /* This array holds the distance to the nearest-so-far color for each cell */ 774 JLONG bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS]; 775 776 /* Initialize best-distance for each cell of the update box */ 777 bptr = bestdist; 778 for (i = BOX_C0_ELEMS*BOX_C1_ELEMS*BOX_C2_ELEMS-1; i >= 0; i--) 779 *bptr++ = 0x7FFFFFFFL; 780 781 /* For each color selected by find_nearby_colors, 782 * compute its distance to the center of each cell in the box. 783 * If that's less than best-so-far, update best distance and color number. 784 */ 785 786 /* Nominal steps between cell centers ("x" in Thomas article) */ 787 #define STEP_C0 ((1 << C0_SHIFT) * C0_SCALE) 788 #define STEP_C1 ((1 << C1_SHIFT) * C1_SCALE) 789 #define STEP_C2 ((1 << C2_SHIFT) * C2_SCALE) 790 791 for (i = 0; i < numcolors; i++) { 792 icolor = GETJSAMPLE(colorlist[i]); 793 /* Compute (square of) distance from minc0/c1/c2 to this color */ 794 inc0 = (minc0 - GETJSAMPLE(cinfo->colormap[0][icolor])) * C0_SCALE; 795 dist0 = inc0*inc0; 796 inc1 = (minc1 - GETJSAMPLE(cinfo->colormap[1][icolor])) * C1_SCALE; 797 dist0 += inc1*inc1; 798 inc2 = (minc2 - GETJSAMPLE(cinfo->colormap[2][icolor])) * C2_SCALE; 799 dist0 += inc2*inc2; 800 /* Form the initial difference increments */ 801 inc0 = inc0 * (2 * STEP_C0) + STEP_C0 * STEP_C0; 802 inc1 = inc1 * (2 * STEP_C1) + STEP_C1 * STEP_C1; 803 inc2 = inc2 * (2 * STEP_C2) + STEP_C2 * STEP_C2; 804 /* Now loop over all cells in box, updating distance per Thomas method */ 805 bptr = bestdist; 806 cptr = bestcolor; 807 xx0 = inc0; 808 for (ic0 = BOX_C0_ELEMS-1; ic0 >= 0; ic0--) { 809 dist1 = dist0; 810 xx1 = inc1; 811 for (ic1 = BOX_C1_ELEMS-1; ic1 >= 0; ic1--) { 812 dist2 = dist1; 813 xx2 = inc2; 814 for (ic2 = BOX_C2_ELEMS-1; ic2 >= 0; ic2--) { 815 if (dist2 < *bptr) { 816 *bptr = dist2; 817 *cptr = (JSAMPLE) icolor; 818 } 819 dist2 += xx2; 820 xx2 += 2 * STEP_C2 * STEP_C2; 821 bptr++; 822 cptr++; 823 } 824 dist1 += xx1; 825 xx1 += 2 * STEP_C1 * STEP_C1; 826 } 827 dist0 += xx0; 828 xx0 += 2 * STEP_C0 * STEP_C0; 829 } 830 } 831 } 832 833 834 LOCAL(void) 835 fill_inverse_cmap (j_decompress_ptr cinfo, int c0, int c1, int c2) 836 /* Fill the inverse-colormap entries in the update box that contains */ 837 /* histogram cell c0/c1/c2. (Only that one cell MUST be filled, but */ 838 /* we can fill as many others as we wish.) */ 839 { 840 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; 841 hist3d histogram = cquantize->histogram; 842 int minc0, minc1, minc2; /* lower left corner of update box */ 843 int ic0, ic1, ic2; 844 register JSAMPLE *cptr; /* pointer into bestcolor[] array */ 845 register histptr cachep; /* pointer into main cache array */ 846 /* This array lists the candidate colormap indexes. */ 847 JSAMPLE colorlist[MAXNUMCOLORS]; 848 int numcolors; /* number of candidate colors */ 849 /* This array holds the actually closest colormap index for each cell. */ 850 JSAMPLE bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS]; 851 852 /* Convert cell coordinates to update box ID */ 853 c0 >>= BOX_C0_LOG; 854 c1 >>= BOX_C1_LOG; 855 c2 >>= BOX_C2_LOG; 856 857 /* Compute true coordinates of update box's origin corner. 858 * Actually we compute the coordinates of the center of the corner 859 * histogram cell, which are the lower bounds of the volume we care about. 860 */ 861 minc0 = (c0 << BOX_C0_SHIFT) + ((1 << C0_SHIFT) >> 1); 862 minc1 = (c1 << BOX_C1_SHIFT) + ((1 << C1_SHIFT) >> 1); 863 minc2 = (c2 << BOX_C2_SHIFT) + ((1 << C2_SHIFT) >> 1); 864 865 /* Determine which colormap entries are close enough to be candidates 866 * for the nearest entry to some cell in the update box. 867 */ 868 numcolors = find_nearby_colors(cinfo, minc0, minc1, minc2, colorlist); 869 870 /* Determine the actually nearest colors. */ 871 find_best_colors(cinfo, minc0, minc1, minc2, numcolors, colorlist, 872 bestcolor); 873 874 /* Save the best color numbers (plus 1) in the main cache array */ 875 c0 <<= BOX_C0_LOG; /* convert ID back to base cell indexes */ 876 c1 <<= BOX_C1_LOG; 877 c2 <<= BOX_C2_LOG; 878 cptr = bestcolor; 879 for (ic0 = 0; ic0 < BOX_C0_ELEMS; ic0++) { 880 for (ic1 = 0; ic1 < BOX_C1_ELEMS; ic1++) { 881 cachep = & histogram[c0+ic0][c1+ic1][c2]; 882 for (ic2 = 0; ic2 < BOX_C2_ELEMS; ic2++) { 883 *cachep++ = (histcell) (GETJSAMPLE(*cptr++) + 1); 884 } 885 } 886 } 887 } 888 889 890 /* 891 * Map some rows of pixels to the output colormapped representation. 892 */ 893 894 METHODDEF(void) 895 pass2_no_dither (j_decompress_ptr cinfo, 896 JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows) 897 /* This version performs no dithering */ 898 { 899 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; 900 hist3d histogram = cquantize->histogram; 901 register JSAMPROW inptr, outptr; 902 register histptr cachep; 903 register int c0, c1, c2; 904 int row; 905 JDIMENSION col; 906 JDIMENSION width = cinfo->output_width; 907 908 for (row = 0; row < num_rows; row++) { 909 inptr = input_buf[row]; 910 outptr = output_buf[row]; 911 for (col = width; col > 0; col--) { 912 /* get pixel value and index into the cache */ 913 c0 = GETJSAMPLE(*inptr++) >> C0_SHIFT; 914 c1 = GETJSAMPLE(*inptr++) >> C1_SHIFT; 915 c2 = GETJSAMPLE(*inptr++) >> C2_SHIFT; 916 cachep = & histogram[c0][c1][c2]; 917 /* If we have not seen this color before, find nearest colormap entry */ 918 /* and update the cache */ 919 if (*cachep == 0) 920 fill_inverse_cmap(cinfo, c0,c1,c2); 921 /* Now emit the colormap index for this cell */ 922 *outptr++ = (JSAMPLE) (*cachep - 1); 923 } 924 } 925 } 926 927 928 METHODDEF(void) 929 pass2_fs_dither (j_decompress_ptr cinfo, 930 JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows) 931 /* This version performs Floyd-Steinberg dithering */ 932 { 933 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; 934 hist3d histogram = cquantize->histogram; 935 register LOCFSERROR cur0, cur1, cur2; /* current error or pixel value */ 936 LOCFSERROR belowerr0, belowerr1, belowerr2; /* error for pixel below cur */ 937 LOCFSERROR bpreverr0, bpreverr1, bpreverr2; /* error for below/prev col */ 938 register FSERRPTR errorptr; /* => fserrors[] at column before current */ 939 JSAMPROW inptr; /* => current input pixel */ 940 JSAMPROW outptr; /* => current output pixel */ 941 histptr cachep; 942 int dir; /* +1 or -1 depending on direction */ 943 int dir3; /* 3*dir, for advancing inptr & errorptr */ 944 int row; 945 JDIMENSION col; 946 JDIMENSION width = cinfo->output_width; 947 JSAMPLE *range_limit = cinfo->sample_range_limit; 948 int *error_limit = cquantize->error_limiter; 949 JSAMPROW colormap0 = cinfo->colormap[0]; 950 JSAMPROW colormap1 = cinfo->colormap[1]; 951 JSAMPROW colormap2 = cinfo->colormap[2]; 952 SHIFT_TEMPS 953 954 for (row = 0; row < num_rows; row++) { 955 inptr = input_buf[row]; 956 outptr = output_buf[row]; 957 if (cquantize->on_odd_row) { 958 /* work right to left in this row */ 959 inptr += (width-1) * 3; /* so point to rightmost pixel */ 960 outptr += width-1; 961 dir = -1; 962 dir3 = -3; 963 errorptr = cquantize->fserrors + (width+1)*3; /* => entry after last column */ 964 cquantize->on_odd_row = FALSE; /* flip for next time */ 965 } else { 966 /* work left to right in this row */ 967 dir = 1; 968 dir3 = 3; 969 errorptr = cquantize->fserrors; /* => entry before first real column */ 970 cquantize->on_odd_row = TRUE; /* flip for next time */ 971 } 972 /* Preset error values: no error propagated to first pixel from left */ 973 cur0 = cur1 = cur2 = 0; 974 /* and no error propagated to row below yet */ 975 belowerr0 = belowerr1 = belowerr2 = 0; 976 bpreverr0 = bpreverr1 = bpreverr2 = 0; 977 978 for (col = width; col > 0; col--) { 979 /* curN holds the error propagated from the previous pixel on the 980 * current line. Add the error propagated from the previous line 981 * to form the complete error correction term for this pixel, and 982 * round the error term (which is expressed * 16) to an integer. 983 * RIGHT_SHIFT rounds towards minus infinity, so adding 8 is correct 984 * for either sign of the error value. 985 * Note: errorptr points to *previous* column's array entry. 986 */ 987 cur0 = RIGHT_SHIFT(cur0 + errorptr[dir3+0] + 8, 4); 988 cur1 = RIGHT_SHIFT(cur1 + errorptr[dir3+1] + 8, 4); 989 cur2 = RIGHT_SHIFT(cur2 + errorptr[dir3+2] + 8, 4); 990 /* Limit the error using transfer function set by init_error_limit. 991 * See comments with init_error_limit for rationale. 992 */ 993 cur0 = error_limit[cur0]; 994 cur1 = error_limit[cur1]; 995 cur2 = error_limit[cur2]; 996 /* Form pixel value + error, and range-limit to 0..MAXJSAMPLE. 997 * The maximum error is +- MAXJSAMPLE (or less with error limiting); 998 * this sets the required size of the range_limit array. 999 */ 1000 cur0 += GETJSAMPLE(inptr[0]); 1001 cur1 += GETJSAMPLE(inptr[1]); 1002 cur2 += GETJSAMPLE(inptr[2]); 1003 cur0 = GETJSAMPLE(range_limit[cur0]); 1004 cur1 = GETJSAMPLE(range_limit[cur1]); 1005 cur2 = GETJSAMPLE(range_limit[cur2]); 1006 /* Index into the cache with adjusted pixel value */ 1007 cachep = & histogram[cur0>>C0_SHIFT][cur1>>C1_SHIFT][cur2>>C2_SHIFT]; 1008 /* If we have not seen this color before, find nearest colormap */ 1009 /* entry and update the cache */ 1010 if (*cachep == 0) 1011 fill_inverse_cmap(cinfo, cur0>>C0_SHIFT,cur1>>C1_SHIFT,cur2>>C2_SHIFT); 1012 /* Now emit the colormap index for this cell */ 1013 { register int pixcode = *cachep - 1; 1014 *outptr = (JSAMPLE) pixcode; 1015 /* Compute representation error for this pixel */ 1016 cur0 -= GETJSAMPLE(colormap0[pixcode]); 1017 cur1 -= GETJSAMPLE(colormap1[pixcode]); 1018 cur2 -= GETJSAMPLE(colormap2[pixcode]); 1019 } 1020 /* Compute error fractions to be propagated to adjacent pixels. 1021 * Add these into the running sums, and simultaneously shift the 1022 * next-line error sums left by 1 column. 1023 */ 1024 { register LOCFSERROR bnexterr; 1025 1026 bnexterr = cur0; /* Process component 0 */ 1027 errorptr[0] = (FSERROR) (bpreverr0 + cur0 * 3); 1028 bpreverr0 = belowerr0 + cur0 * 5; 1029 belowerr0 = bnexterr; 1030 cur0 *= 7; 1031 bnexterr = cur1; /* Process component 1 */ 1032 errorptr[1] = (FSERROR) (bpreverr1 + cur1 * 3); 1033 bpreverr1 = belowerr1 + cur1 * 5; 1034 belowerr1 = bnexterr; 1035 cur1 *= 7; 1036 bnexterr = cur2; /* Process component 2 */ 1037 errorptr[2] = (FSERROR) (bpreverr2 + cur2 * 3); 1038 bpreverr2 = belowerr2 + cur2 * 5; 1039 belowerr2 = bnexterr; 1040 cur2 *= 7; 1041 } 1042 /* At this point curN contains the 7/16 error value to be propagated 1043 * to the next pixel on the current line, and all the errors for the 1044 * next line have been shifted over. We are therefore ready to move on. 1045 */ 1046 inptr += dir3; /* Advance pixel pointers to next column */ 1047 outptr += dir; 1048 errorptr += dir3; /* advance errorptr to current column */ 1049 } 1050 /* Post-loop cleanup: we must unload the final error values into the 1051 * final fserrors[] entry. Note we need not unload belowerrN because 1052 * it is for the dummy column before or after the actual array. 1053 */ 1054 errorptr[0] = (FSERROR) bpreverr0; /* unload prev errs into array */ 1055 errorptr[1] = (FSERROR) bpreverr1; 1056 errorptr[2] = (FSERROR) bpreverr2; 1057 } 1058 } 1059 1060 1061 /* 1062 * Initialize the error-limiting transfer function (lookup table). 1063 * The raw F-S error computation can potentially compute error values of up to 1064 * +- MAXJSAMPLE. But we want the maximum correction applied to a pixel to be 1065 * much less, otherwise obviously wrong pixels will be created. (Typical 1066 * effects include weird fringes at color-area boundaries, isolated bright 1067 * pixels in a dark area, etc.) The standard advice for avoiding this problem 1068 * is to ensure that the "corners" of the color cube are allocated as output 1069 * colors; then repeated errors in the same direction cannot cause cascading 1070 * error buildup. However, that only prevents the error from getting 1071 * completely out of hand; Aaron Giles reports that error limiting improves 1072 * the results even with corner colors allocated. 1073 * A simple clamping of the error values to about +- MAXJSAMPLE/8 works pretty 1074 * well, but the smoother transfer function used below is even better. Thanks 1075 * to Aaron Giles for this idea. 1076 */ 1077 1078 LOCAL(void) 1079 init_error_limit (j_decompress_ptr cinfo) 1080 /* Allocate and fill in the error_limiter table */ 1081 { 1082 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; 1083 int *table; 1084 int in, out; 1085 1086 table = (int *) (*cinfo->mem->alloc_small) 1087 ((j_common_ptr) cinfo, JPOOL_IMAGE, (MAXJSAMPLE*2+1) * sizeof(int)); 1088 table += MAXJSAMPLE; /* so can index -MAXJSAMPLE .. +MAXJSAMPLE */ 1089 cquantize->error_limiter = table; 1090 1091 #define STEPSIZE ((MAXJSAMPLE+1)/16) 1092 /* Map errors 1:1 up to +- MAXJSAMPLE/16 */ 1093 out = 0; 1094 for (in = 0; in < STEPSIZE; in++, out++) { 1095 table[in] = out; table[-in] = -out; 1096 } 1097 /* Map errors 1:2 up to +- 3*MAXJSAMPLE/16 */ 1098 for (; in < STEPSIZE*3; in++, out += (in&1) ? 0 : 1) { 1099 table[in] = out; table[-in] = -out; 1100 } 1101 /* Clamp the rest to final out value (which is (MAXJSAMPLE+1)/8) */ 1102 for (; in <= MAXJSAMPLE; in++) { 1103 table[in] = out; table[-in] = -out; 1104 } 1105 #undef STEPSIZE 1106 } 1107 1108 1109 /* 1110 * Finish up at the end of each pass. 1111 */ 1112 1113 METHODDEF(void) 1114 finish_pass1 (j_decompress_ptr cinfo) 1115 { 1116 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; 1117 1118 /* Select the representative colors and fill in cinfo->colormap */ 1119 cinfo->colormap = cquantize->sv_colormap; 1120 select_colors(cinfo, cquantize->desired); 1121 /* Force next pass to zero the color index table */ 1122 cquantize->needs_zeroed = TRUE; 1123 } 1124 1125 1126 METHODDEF(void) 1127 finish_pass2 (j_decompress_ptr cinfo) 1128 { 1129 /* no work */ 1130 } 1131 1132 1133 /* 1134 * Initialize for each processing pass. 1135 */ 1136 1137 METHODDEF(void) 1138 start_pass_2_quant (j_decompress_ptr cinfo, boolean is_pre_scan) 1139 { 1140 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; 1141 hist3d histogram = cquantize->histogram; 1142 int i; 1143 1144 /* Only F-S dithering or no dithering is supported. */ 1145 /* If user asks for ordered dither, give him F-S. */ 1146 if (cinfo->dither_mode != JDITHER_NONE) 1147 cinfo->dither_mode = JDITHER_FS; 1148 1149 if (is_pre_scan) { 1150 /* Set up method pointers */ 1151 cquantize->pub.color_quantize = prescan_quantize; 1152 cquantize->pub.finish_pass = finish_pass1; 1153 cquantize->needs_zeroed = TRUE; /* Always zero histogram */ 1154 } else { 1155 /* Set up method pointers */ 1156 if (cinfo->dither_mode == JDITHER_FS) 1157 cquantize->pub.color_quantize = pass2_fs_dither; 1158 else 1159 cquantize->pub.color_quantize = pass2_no_dither; 1160 cquantize->pub.finish_pass = finish_pass2; 1161 1162 /* Make sure color count is acceptable */ 1163 i = cinfo->actual_number_of_colors; 1164 if (i < 1) 1165 ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 1); 1166 if (i > MAXNUMCOLORS) 1167 ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS); 1168 1169 if (cinfo->dither_mode == JDITHER_FS) { 1170 size_t arraysize = (size_t) ((cinfo->output_width + 2) * 1171 (3 * sizeof(FSERROR))); 1172 /* Allocate Floyd-Steinberg workspace if we didn't already. */ 1173 if (cquantize->fserrors == NULL) 1174 cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large) 1175 ((j_common_ptr) cinfo, JPOOL_IMAGE, arraysize); 1176 /* Initialize the propagated errors to zero. */ 1177 jzero_far((void *) cquantize->fserrors, arraysize); 1178 /* Make the error-limit table if we didn't already. */ 1179 if (cquantize->error_limiter == NULL) 1180 init_error_limit(cinfo); 1181 cquantize->on_odd_row = FALSE; 1182 } 1183 1184 } 1185 /* Zero the histogram or inverse color map, if necessary */ 1186 if (cquantize->needs_zeroed) { 1187 for (i = 0; i < HIST_C0_ELEMS; i++) { 1188 jzero_far((void *) histogram[i], 1189 HIST_C1_ELEMS*HIST_C2_ELEMS * sizeof(histcell)); 1190 } 1191 cquantize->needs_zeroed = FALSE; 1192 } 1193 } 1194 1195 1196 /* 1197 * Switch to a new external colormap between output passes. 1198 */ 1199 1200 METHODDEF(void) 1201 new_color_map_2_quant (j_decompress_ptr cinfo) 1202 { 1203 my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; 1204 1205 /* Reset the inverse color map */ 1206 cquantize->needs_zeroed = TRUE; 1207 } 1208 1209 1210 /* 1211 * Module initialization routine for 2-pass color quantization. 1212 */ 1213 1214 GLOBAL(void) 1215 jinit_2pass_quantizer (j_decompress_ptr cinfo) 1216 { 1217 my_cquantize_ptr cquantize; 1218 int i; 1219 1220 cquantize = (my_cquantize_ptr) 1221 (*cinfo->mem->alloc_small) ((j_common_ptr) cinfo, JPOOL_IMAGE, 1222 sizeof(my_cquantizer)); 1223 cinfo->cquantize = (struct jpeg_color_quantizer *) cquantize; 1224 cquantize->pub.start_pass = start_pass_2_quant; 1225 cquantize->pub.new_color_map = new_color_map_2_quant; 1226 cquantize->fserrors = NULL; /* flag optional arrays not allocated */ 1227 cquantize->error_limiter = NULL; 1228 1229 /* Make sure jdmaster didn't give me a case I can't handle */ 1230 if (cinfo->out_color_components != 3) 1231 ERREXIT(cinfo, JERR_NOTIMPL); 1232 1233 /* Allocate the histogram/inverse colormap storage */ 1234 cquantize->histogram = (hist3d) (*cinfo->mem->alloc_small) 1235 ((j_common_ptr) cinfo, JPOOL_IMAGE, HIST_C0_ELEMS * sizeof(hist2d)); 1236 for (i = 0; i < HIST_C0_ELEMS; i++) { 1237 cquantize->histogram[i] = (hist2d) (*cinfo->mem->alloc_large) 1238 ((j_common_ptr) cinfo, JPOOL_IMAGE, 1239 HIST_C1_ELEMS*HIST_C2_ELEMS * sizeof(histcell)); 1240 } 1241 cquantize->needs_zeroed = TRUE; /* histogram is garbage now */ 1242 1243 /* Allocate storage for the completed colormap, if required. 1244 * We do this now since it may affect the memory manager's space 1245 * calculations. 1246 */ 1247 if (cinfo->enable_2pass_quant) { 1248 /* Make sure color count is acceptable */ 1249 int desired = cinfo->desired_number_of_colors; 1250 /* Lower bound on # of colors ... somewhat arbitrary as long as > 0 */ 1251 if (desired < 8) 1252 ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 8); 1253 /* Make sure colormap indexes can be represented by JSAMPLEs */ 1254 if (desired > MAXNUMCOLORS) 1255 ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS); 1256 cquantize->sv_colormap = (*cinfo->mem->alloc_sarray) 1257 ((j_common_ptr) cinfo,JPOOL_IMAGE, (JDIMENSION) desired, (JDIMENSION) 3); 1258 cquantize->desired = desired; 1259 } else 1260 cquantize->sv_colormap = NULL; 1261 1262 /* Only F-S dithering or no dithering is supported. */ 1263 /* If user asks for ordered dither, give him F-S. */ 1264 if (cinfo->dither_mode != JDITHER_NONE) 1265 cinfo->dither_mode = JDITHER_FS; 1266 1267 /* Allocate Floyd-Steinberg workspace if necessary. 1268 * This isn't really needed until pass 2, but again it may affect the memory 1269 * manager's space calculations. Although we will cope with a later change 1270 * in dither_mode, we do not promise to honor max_memory_to_use if 1271 * dither_mode changes. 1272 */ 1273 if (cinfo->dither_mode == JDITHER_FS) { 1274 cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large) 1275 ((j_common_ptr) cinfo, JPOOL_IMAGE, 1276 (size_t) ((cinfo->output_width + 2) * (3 * sizeof(FSERROR)))); 1277 /* Might as well create the error-limiting table too. */ 1278 init_error_limit(cinfo); 1279 } 1280 } 1281 1282 #endif /* QUANT_2PASS_SUPPORTED */ 1283