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 }