/* | |

* jquant2.c | |

* | |

* Copyright (C) 1991-1996, Thomas G. Lane. | |

* Copyright (C) 2009, D. R. Commander. | |

* This file is part of the Independent JPEG Group's software. | |

* For conditions of distribution and use, see the accompanying README file. | |

* | |

* This file contains 2-pass color quantization (color mapping) routines. | |

* These routines provide selection of a custom color map for an image, | |

* followed by mapping of the image to that color map, with optional | |

* Floyd-Steinberg dithering. | |

* It is also possible to use just the second pass to map to an arbitrary | |

* externally-given color map. | |

* | |

* Note: ordered dithering is not supported, since there isn't any fast | |

* way to compute intercolor distances; it's unclear that ordered dither's | |

* fundamental assumptions even hold with an irregularly spaced color map. | |

*/ | |

#define JPEG_INTERNALS | |

#include "jinclude.h" | |

#include "jpeglib.h" | |

#ifdef QUANT_2PASS_SUPPORTED | |

/* | |

* This module implements the well-known Heckbert paradigm for color | |

* quantization. Most of the ideas used here can be traced back to | |

* Heckbert's seminal paper | |

* Heckbert, Paul. "Color Image Quantization for Frame Buffer Display", | |

* Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304. | |

* | |

* In the first pass over the image, we accumulate a histogram showing the | |

* usage count of each possible color. To keep the histogram to a reasonable | |

* size, we reduce the precision of the input; typical practice is to retain | |

* 5 or 6 bits per color, so that 8 or 4 different input values are counted | |

* in the same histogram cell. | |

* | |

* Next, the color-selection step begins with a box representing the whole | |

* color space, and repeatedly splits the "largest" remaining box until we | |

* have as many boxes as desired colors. Then the mean color in each | |

* remaining box becomes one of the possible output colors. | |

* | |

* The second pass over the image maps each input pixel to the closest output | |

* color (optionally after applying a Floyd-Steinberg dithering correction). | |

* This mapping is logically trivial, but making it go fast enough requires | |

* considerable care. | |

* | |

* Heckbert-style quantizers vary a good deal in their policies for choosing | |

* the "largest" box and deciding where to cut it. The particular policies | |

* used here have proved out well in experimental comparisons, but better ones | |

* may yet be found. | |

* | |

* In earlier versions of the IJG code, this module quantized in YCbCr color | |

* space, processing the raw upsampled data without a color conversion step. | |

* This allowed the color conversion math to be done only once per colormap | |

* entry, not once per pixel. However, that optimization precluded other | |

* useful optimizations (such as merging color conversion with upsampling) | |

* and it also interfered with desired capabilities such as quantizing to an | |

* externally-supplied colormap. We have therefore abandoned that approach. | |

* The present code works in the post-conversion color space, typically RGB. | |

* | |

* To improve the visual quality of the results, we actually work in scaled | |

* RGB space, giving G distances more weight than R, and R in turn more than | |

* B. To do everything in integer math, we must use integer scale factors. | |

* The 2/3/1 scale factors used here correspond loosely to the relative | |

* weights of the colors in the NTSC grayscale equation. | |

* If you want to use this code to quantize a non-RGB color space, you'll | |

* probably need to change these scale factors. | |

*/ | |

#define R_SCALE 2 /* scale R distances by this much */ | |

#define G_SCALE 3 /* scale G distances by this much */ | |

#define B_SCALE 1 /* and B by this much */ | |

static const int c_scales[3]={R_SCALE, G_SCALE, B_SCALE}; | |

#define C0_SCALE c_scales[rgb_red[cinfo->out_color_space]] | |

#define C1_SCALE c_scales[rgb_green[cinfo->out_color_space]] | |

#define C2_SCALE c_scales[rgb_blue[cinfo->out_color_space]] | |

/* | |

* First we have the histogram data structure and routines for creating it. | |

* | |

* The number of bits of precision can be adjusted by changing these symbols. | |

* We recommend keeping 6 bits for G and 5 each for R and B. | |

* If you have plenty of memory and cycles, 6 bits all around gives marginally | |

* better results; if you are short of memory, 5 bits all around will save | |

* some space but degrade the results. | |

* To maintain a fully accurate histogram, we'd need to allocate a "long" | |

* (preferably unsigned long) for each cell. In practice this is overkill; | |

* we can get by with 16 bits per cell. Few of the cell counts will overflow, | |

* and clamping those that do overflow to the maximum value will give close- | |

* enough results. This reduces the recommended histogram size from 256Kb | |

* to 128Kb, which is a useful savings on PC-class machines. | |

* (In the second pass the histogram space is re-used for pixel mapping data; | |

* in that capacity, each cell must be able to store zero to the number of | |

* desired colors. 16 bits/cell is plenty for that too.) | |

* Since the JPEG code is intended to run in small memory model on 80x86 | |

* machines, we can't just allocate the histogram in one chunk. Instead | |

* of a true 3-D array, we use a row of pointers to 2-D arrays. Each | |

* pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and | |

* each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries. Note that | |

* on 80x86 machines, the pointer row is in near memory but the actual | |

* arrays are in far memory (same arrangement as we use for image arrays). | |

*/ | |

#define MAXNUMCOLORS (MAXJSAMPLE+1) /* maximum size of colormap */ | |

/* These will do the right thing for either R,G,B or B,G,R color order, | |

* but you may not like the results for other color orders. | |

*/ | |

#define HIST_C0_BITS 5 /* bits of precision in R/B histogram */ | |

#define HIST_C1_BITS 6 /* bits of precision in G histogram */ | |

#define HIST_C2_BITS 5 /* bits of precision in B/R histogram */ | |

/* Number of elements along histogram axes. */ | |

#define HIST_C0_ELEMS (1<<HIST_C0_BITS) | |

#define HIST_C1_ELEMS (1<<HIST_C1_BITS) | |

#define HIST_C2_ELEMS (1<<HIST_C2_BITS) | |

/* These are the amounts to shift an input value to get a histogram index. */ | |

#define C0_SHIFT (BITS_IN_JSAMPLE-HIST_C0_BITS) | |

#define C1_SHIFT (BITS_IN_JSAMPLE-HIST_C1_BITS) | |

#define C2_SHIFT (BITS_IN_JSAMPLE-HIST_C2_BITS) | |

typedef UINT16 histcell; /* histogram cell; prefer an unsigned type */ | |

typedef histcell FAR * histptr; /* for pointers to histogram cells */ | |

typedef histcell hist1d[HIST_C2_ELEMS]; /* typedefs for the array */ | |

typedef hist1d FAR * hist2d; /* type for the 2nd-level pointers */ | |

typedef hist2d * hist3d; /* type for top-level pointer */ | |

/* Declarations for Floyd-Steinberg dithering. | |

* | |

* Errors are accumulated into the array fserrors[], at a resolution of | |

* 1/16th of a pixel count. The error at a given pixel is propagated | |

* to its not-yet-processed neighbors using the standard F-S fractions, | |

* ... (here) 7/16 | |

* 3/16 5/16 1/16 | |

* We work left-to-right on even rows, right-to-left on odd rows. | |

* | |

* We can get away with a single array (holding one row's worth of errors) | |

* by using it to store the current row's errors at pixel columns not yet | |

* processed, but the next row's errors at columns already processed. We | |

* need only a few extra variables to hold the errors immediately around the | |

* current column. (If we are lucky, those variables are in registers, but | |

* even if not, they're probably cheaper to access than array elements are.) | |

* | |

* The fserrors[] array has (#columns + 2) entries; the extra entry at | |

* each end saves us from special-casing the first and last pixels. | |

* Each entry is three values long, one value for each color component. | |

* | |

* Note: on a wide image, we might not have enough room in a PC's near data | |

* segment to hold the error array; so it is allocated with alloc_large. | |

*/ | |

#if BITS_IN_JSAMPLE == 8 | |

typedef INT16 FSERROR; /* 16 bits should be enough */ | |

typedef int LOCFSERROR; /* use 'int' for calculation temps */ | |

#else | |

typedef INT32 FSERROR; /* may need more than 16 bits */ | |

typedef INT32 LOCFSERROR; /* be sure calculation temps are big enough */ | |

#endif | |

typedef FSERROR FAR *FSERRPTR; /* pointer to error array (in FAR storage!) */ | |

/* Private subobject */ | |

typedef struct { | |

struct jpeg_color_quantizer pub; /* public fields */ | |

/* Space for the eventually created colormap is stashed here */ | |

JSAMPARRAY sv_colormap; /* colormap allocated at init time */ | |

int desired; /* desired # of colors = size of colormap */ | |

/* Variables for accumulating image statistics */ | |

hist3d histogram; /* pointer to the histogram */ | |

boolean needs_zeroed; /* TRUE if next pass must zero histogram */ | |

/* Variables for Floyd-Steinberg dithering */ | |

FSERRPTR fserrors; /* accumulated errors */ | |

boolean on_odd_row; /* flag to remember which row we are on */ | |

int * error_limiter; /* table for clamping the applied error */ | |

} my_cquantizer; | |

typedef my_cquantizer * my_cquantize_ptr; | |

/* | |

* Prescan some rows of pixels. | |

* In this module the prescan simply updates the histogram, which has been | |

* initialized to zeroes by start_pass. | |

* An output_buf parameter is required by the method signature, but no data | |

* is actually output (in fact the buffer controller is probably passing a | |

* NULL pointer). | |

*/ | |

METHODDEF(void) | |

prescan_quantize (j_decompress_ptr cinfo, JSAMPARRAY input_buf, | |

JSAMPARRAY output_buf, int num_rows) | |

{ | |

my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; | |

register JSAMPROW ptr; | |

register histptr histp; | |

register hist3d histogram = cquantize->histogram; | |

int row; | |

JDIMENSION col; | |

JDIMENSION width = cinfo->output_width; | |

for (row = 0; row < num_rows; row++) { | |

ptr = input_buf[row]; | |

for (col = width; col > 0; col--) { | |

/* get pixel value and index into the histogram */ | |

histp = & histogram[GETJSAMPLE(ptr[0]) >> C0_SHIFT] | |

[GETJSAMPLE(ptr[1]) >> C1_SHIFT] | |

[GETJSAMPLE(ptr[2]) >> C2_SHIFT]; | |

/* increment, check for overflow and undo increment if so. */ | |

if (++(*histp) <= 0) | |

(*histp)--; | |

ptr += 3; | |

} | |

} | |

} | |

/* | |

* Next we have the really interesting routines: selection of a colormap | |

* given the completed histogram. | |

* These routines work with a list of "boxes", each representing a rectangular | |

* subset of the input color space (to histogram precision). | |

*/ | |

typedef struct { | |

/* The bounds of the box (inclusive); expressed as histogram indexes */ | |

int c0min, c0max; | |

int c1min, c1max; | |

int c2min, c2max; | |

/* The volume (actually 2-norm) of the box */ | |

INT32 volume; | |

/* The number of nonzero histogram cells within this box */ | |

long colorcount; | |

} box; | |

typedef box * boxptr; | |

LOCAL(boxptr) | |

find_biggest_color_pop (boxptr boxlist, int numboxes) | |

/* Find the splittable box with the largest color population */ | |

/* Returns NULL if no splittable boxes remain */ | |

{ | |

register boxptr boxp; | |

register int i; | |

register long maxc = 0; | |

boxptr which = NULL; | |

for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) { | |

if (boxp->colorcount > maxc && boxp->volume > 0) { | |

which = boxp; | |

maxc = boxp->colorcount; | |

} | |

} | |

return which; | |

} | |

LOCAL(boxptr) | |

find_biggest_volume (boxptr boxlist, int numboxes) | |

/* Find the splittable box with the largest (scaled) volume */ | |

/* Returns NULL if no splittable boxes remain */ | |

{ | |

register boxptr boxp; | |

register int i; | |

register INT32 maxv = 0; | |

boxptr which = NULL; | |

for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) { | |

if (boxp->volume > maxv) { | |

which = boxp; | |

maxv = boxp->volume; | |

} | |

} | |

return which; | |

} | |

LOCAL(void) | |

update_box (j_decompress_ptr cinfo, boxptr boxp) | |

/* Shrink the min/max bounds of a box to enclose only nonzero elements, */ | |

/* and recompute its volume and population */ | |

{ | |

my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; | |

hist3d histogram = cquantize->histogram; | |

histptr histp; | |

int c0,c1,c2; | |

int c0min,c0max,c1min,c1max,c2min,c2max; | |

INT32 dist0,dist1,dist2; | |

long ccount; | |

c0min = boxp->c0min; c0max = boxp->c0max; | |

c1min = boxp->c1min; c1max = boxp->c1max; | |

c2min = boxp->c2min; c2max = boxp->c2max; | |

if (c0max > c0min) | |

for (c0 = c0min; c0 <= c0max; c0++) | |

for (c1 = c1min; c1 <= c1max; c1++) { | |

histp = & histogram[c0][c1][c2min]; | |

for (c2 = c2min; c2 <= c2max; c2++) | |

if (*histp++ != 0) { | |

boxp->c0min = c0min = c0; | |

goto have_c0min; | |

} | |

} | |

have_c0min: | |

if (c0max > c0min) | |

for (c0 = c0max; c0 >= c0min; c0--) | |

for (c1 = c1min; c1 <= c1max; c1++) { | |

histp = & histogram[c0][c1][c2min]; | |

for (c2 = c2min; c2 <= c2max; c2++) | |

if (*histp++ != 0) { | |

boxp->c0max = c0max = c0; | |

goto have_c0max; | |

} | |

} | |

have_c0max: | |

if (c1max > c1min) | |

for (c1 = c1min; c1 <= c1max; c1++) | |

for (c0 = c0min; c0 <= c0max; c0++) { | |

histp = & histogram[c0][c1][c2min]; | |

for (c2 = c2min; c2 <= c2max; c2++) | |

if (*histp++ != 0) { | |

boxp->c1min = c1min = c1; | |

goto have_c1min; | |

} | |

} | |

have_c1min: | |

if (c1max > c1min) | |

for (c1 = c1max; c1 >= c1min; c1--) | |

for (c0 = c0min; c0 <= c0max; c0++) { | |

histp = & histogram[c0][c1][c2min]; | |

for (c2 = c2min; c2 <= c2max; c2++) | |

if (*histp++ != 0) { | |

boxp->c1max = c1max = c1; | |

goto have_c1max; | |

} | |

} | |

have_c1max: | |

if (c2max > c2min) | |

for (c2 = c2min; c2 <= c2max; c2++) | |

for (c0 = c0min; c0 <= c0max; c0++) { | |

histp = & histogram[c0][c1min][c2]; | |

for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS) | |

if (*histp != 0) { | |

boxp->c2min = c2min = c2; | |

goto have_c2min; | |

} | |

} | |

have_c2min: | |

if (c2max > c2min) | |

for (c2 = c2max; c2 >= c2min; c2--) | |

for (c0 = c0min; c0 <= c0max; c0++) { | |

histp = & histogram[c0][c1min][c2]; | |

for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS) | |

if (*histp != 0) { | |

boxp->c2max = c2max = c2; | |

goto have_c2max; | |

} | |

} | |

have_c2max: | |

/* Update box volume. | |

* We use 2-norm rather than real volume here; this biases the method | |

* against making long narrow boxes, and it has the side benefit that | |

* a box is splittable iff norm > 0. | |

* Since the differences are expressed in histogram-cell units, | |

* we have to shift back to JSAMPLE units to get consistent distances; | |

* after which, we scale according to the selected distance scale factors. | |

*/ | |

dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE; | |

dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE; | |

dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE; | |

boxp->volume = dist0*dist0 + dist1*dist1 + dist2*dist2; | |

/* Now scan remaining volume of box and compute population */ | |

ccount = 0; | |

for (c0 = c0min; c0 <= c0max; c0++) | |

for (c1 = c1min; c1 <= c1max; c1++) { | |

histp = & histogram[c0][c1][c2min]; | |

for (c2 = c2min; c2 <= c2max; c2++, histp++) | |

if (*histp != 0) { | |

ccount++; | |

} | |

} | |

boxp->colorcount = ccount; | |

} | |

LOCAL(int) | |

median_cut (j_decompress_ptr cinfo, boxptr boxlist, int numboxes, | |

int desired_colors) | |

/* Repeatedly select and split the largest box until we have enough boxes */ | |

{ | |

int n,lb; | |

int c0,c1,c2,cmax; | |

register boxptr b1,b2; | |

while (numboxes < desired_colors) { | |

/* Select box to split. | |

* Current algorithm: by population for first half, then by volume. | |

*/ | |

if (numboxes*2 <= desired_colors) { | |

b1 = find_biggest_color_pop(boxlist, numboxes); | |

} else { | |

b1 = find_biggest_volume(boxlist, numboxes); | |

} | |

if (b1 == NULL) /* no splittable boxes left! */ | |

break; | |

b2 = &boxlist[numboxes]; /* where new box will go */ | |

/* Copy the color bounds to the new box. */ | |

b2->c0max = b1->c0max; b2->c1max = b1->c1max; b2->c2max = b1->c2max; | |

b2->c0min = b1->c0min; b2->c1min = b1->c1min; b2->c2min = b1->c2min; | |

/* Choose which axis to split the box on. | |

* Current algorithm: longest scaled axis. | |

* See notes in update_box about scaling distances. | |

*/ | |

c0 = ((b1->c0max - b1->c0min) << C0_SHIFT) * C0_SCALE; | |

c1 = ((b1->c1max - b1->c1min) << C1_SHIFT) * C1_SCALE; | |

c2 = ((b1->c2max - b1->c2min) << C2_SHIFT) * C2_SCALE; | |

/* We want to break any ties in favor of green, then red, blue last. | |

* This code does the right thing for R,G,B or B,G,R color orders only. | |

*/ | |

if (rgb_red[cinfo->out_color_space] == 0) { | |

cmax = c1; n = 1; | |

if (c0 > cmax) { cmax = c0; n = 0; } | |

if (c2 > cmax) { n = 2; } | |

} | |

else { | |

cmax = c1; n = 1; | |

if (c2 > cmax) { cmax = c2; n = 2; } | |

if (c0 > cmax) { n = 0; } | |

} | |

/* Choose split point along selected axis, and update box bounds. | |

* Current algorithm: split at halfway point. | |

* (Since the box has been shrunk to minimum volume, | |

* any split will produce two nonempty subboxes.) | |

* Note that lb value is max for lower box, so must be < old max. | |

*/ | |

switch (n) { | |

case 0: | |

lb = (b1->c0max + b1->c0min) / 2; | |

b1->c0max = lb; | |

b2->c0min = lb+1; | |

break; | |

case 1: | |

lb = (b1->c1max + b1->c1min) / 2; | |

b1->c1max = lb; | |

b2->c1min = lb+1; | |

break; | |

case 2: | |

lb = (b1->c2max + b1->c2min) / 2; | |

b1->c2max = lb; | |

b2->c2min = lb+1; | |

break; | |

} | |

/* Update stats for boxes */ | |

update_box(cinfo, b1); | |

update_box(cinfo, b2); | |

numboxes++; | |

} | |

return numboxes; | |

} | |

LOCAL(void) | |

compute_color (j_decompress_ptr cinfo, boxptr boxp, int icolor) | |

/* Compute representative color for a box, put it in colormap[icolor] */ | |

{ | |

/* Current algorithm: mean weighted by pixels (not colors) */ | |

/* Note it is important to get the rounding correct! */ | |

my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; | |

hist3d histogram = cquantize->histogram; | |

histptr histp; | |

int c0,c1,c2; | |

int c0min,c0max,c1min,c1max,c2min,c2max; | |

long count; | |

long total = 0; | |

long c0total = 0; | |

long c1total = 0; | |

long c2total = 0; | |

c0min = boxp->c0min; c0max = boxp->c0max; | |

c1min = boxp->c1min; c1max = boxp->c1max; | |

c2min = boxp->c2min; c2max = boxp->c2max; | |

for (c0 = c0min; c0 <= c0max; c0++) | |

for (c1 = c1min; c1 <= c1max; c1++) { | |

histp = & histogram[c0][c1][c2min]; | |

for (c2 = c2min; c2 <= c2max; c2++) { | |

if ((count = *histp++) != 0) { | |

total += count; | |

c0total += ((c0 << C0_SHIFT) + ((1<<C0_SHIFT)>>1)) * count; | |

c1total += ((c1 << C1_SHIFT) + ((1<<C1_SHIFT)>>1)) * count; | |

c2total += ((c2 << C2_SHIFT) + ((1<<C2_SHIFT)>>1)) * count; | |

} | |

} | |

} | |

cinfo->colormap[0][icolor] = (JSAMPLE) ((c0total + (total>>1)) / total); | |

cinfo->colormap[1][icolor] = (JSAMPLE) ((c1total + (total>>1)) / total); | |

cinfo->colormap[2][icolor] = (JSAMPLE) ((c2total + (total>>1)) / total); | |

} | |

LOCAL(void) | |

select_colors (j_decompress_ptr cinfo, int desired_colors) | |

/* Master routine for color selection */ | |

{ | |

boxptr boxlist; | |

int numboxes; | |

int i; | |

/* Allocate workspace for box list */ | |

boxlist = (boxptr) (*cinfo->mem->alloc_small) | |

((j_common_ptr) cinfo, JPOOL_IMAGE, desired_colors * SIZEOF(box)); | |

/* Initialize one box containing whole space */ | |

numboxes = 1; | |

boxlist[0].c0min = 0; | |

boxlist[0].c0max = MAXJSAMPLE >> C0_SHIFT; | |

boxlist[0].c1min = 0; | |

boxlist[0].c1max = MAXJSAMPLE >> C1_SHIFT; | |

boxlist[0].c2min = 0; | |

boxlist[0].c2max = MAXJSAMPLE >> C2_SHIFT; | |

/* Shrink it to actually-used volume and set its statistics */ | |

update_box(cinfo, & boxlist[0]); | |

/* Perform median-cut to produce final box list */ | |

numboxes = median_cut(cinfo, boxlist, numboxes, desired_colors); | |

/* Compute the representative color for each box, fill colormap */ | |

for (i = 0; i < numboxes; i++) | |

compute_color(cinfo, & boxlist[i], i); | |

cinfo->actual_number_of_colors = numboxes; | |

TRACEMS1(cinfo, 1, JTRC_QUANT_SELECTED, numboxes); | |

} | |

/* | |

* These routines are concerned with the time-critical task of mapping input | |

* colors to the nearest color in the selected colormap. | |

* | |

* We re-use the histogram space as an "inverse color map", essentially a | |

* cache for the results of nearest-color searches. All colors within a | |

* histogram cell will be mapped to the same colormap entry, namely the one | |

* closest to the cell's center. This may not be quite the closest entry to | |

* the actual input color, but it's almost as good. A zero in the cache | |

* indicates we haven't found the nearest color for that cell yet; the array | |

* is cleared to zeroes before starting the mapping pass. When we find the | |

* nearest color for a cell, its colormap index plus one is recorded in the | |

* cache for future use. The pass2 scanning routines call fill_inverse_cmap | |

* when they need to use an unfilled entry in the cache. | |

* | |

* Our method of efficiently finding nearest colors is based on the "locally | |

* sorted search" idea described by Heckbert and on the incremental distance | |

* calculation described by Spencer W. Thomas in chapter III.1 of Graphics | |

* Gems II (James Arvo, ed. Academic Press, 1991). Thomas points out that | |

* the distances from a given colormap entry to each cell of the histogram can | |

* be computed quickly using an incremental method: the differences between | |

* distances to adjacent cells themselves differ by a constant. This allows a | |

* fairly fast implementation of the "brute force" approach of computing the | |

* distance from every colormap entry to every histogram cell. Unfortunately, | |

* it needs a work array to hold the best-distance-so-far for each histogram | |

* cell (because the inner loop has to be over cells, not colormap entries). | |

* The work array elements have to be INT32s, so the work array would need | |

* 256Kb at our recommended precision. This is not feasible in DOS machines. | |

* | |

* To get around these problems, we apply Thomas' method to compute the | |

* nearest colors for only the cells within a small subbox of the histogram. | |

* The work array need be only as big as the subbox, so the memory usage | |

* problem is solved. Furthermore, we need not fill subboxes that are never | |

* referenced in pass2; many images use only part of the color gamut, so a | |

* fair amount of work is saved. An additional advantage of this | |

* approach is that we can apply Heckbert's locality criterion to quickly | |

* eliminate colormap entries that are far away from the subbox; typically | |

* three-fourths of the colormap entries are rejected by Heckbert's criterion, | |

* and we need not compute their distances to individual cells in the subbox. | |

* The speed of this approach is heavily influenced by the subbox size: too | |

* small means too much overhead, too big loses because Heckbert's criterion | |

* can't eliminate as many colormap entries. Empirically the best subbox | |

* size seems to be about 1/512th of the histogram (1/8th in each direction). | |

* | |

* Thomas' article also describes a refined method which is asymptotically | |

* faster than the brute-force method, but it is also far more complex and | |

* cannot efficiently be applied to small subboxes. It is therefore not | |

* useful for programs intended to be portable to DOS machines. On machines | |

* with plenty of memory, filling the whole histogram in one shot with Thomas' | |

* refined method might be faster than the present code --- but then again, | |

* it might not be any faster, and it's certainly more complicated. | |

*/ | |

/* log2(histogram cells in update box) for each axis; this can be adjusted */ | |

#define BOX_C0_LOG (HIST_C0_BITS-3) | |

#define BOX_C1_LOG (HIST_C1_BITS-3) | |

#define BOX_C2_LOG (HIST_C2_BITS-3) | |

#define BOX_C0_ELEMS (1<<BOX_C0_LOG) /* # of hist cells in update box */ | |

#define BOX_C1_ELEMS (1<<BOX_C1_LOG) | |

#define BOX_C2_ELEMS (1<<BOX_C2_LOG) | |

#define BOX_C0_SHIFT (C0_SHIFT + BOX_C0_LOG) | |

#define BOX_C1_SHIFT (C1_SHIFT + BOX_C1_LOG) | |

#define BOX_C2_SHIFT (C2_SHIFT + BOX_C2_LOG) | |

/* | |

* The next three routines implement inverse colormap filling. They could | |

* all be folded into one big routine, but splitting them up this way saves | |

* some stack space (the mindist[] and bestdist[] arrays need not coexist) | |

* and may allow some compilers to produce better code by registerizing more | |

* inner-loop variables. | |

*/ | |

LOCAL(int) | |

find_nearby_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2, | |

JSAMPLE colorlist[]) | |

/* Locate the colormap entries close enough to an update box to be candidates | |

* for the nearest entry to some cell(s) in the update box. The update box | |

* is specified by the center coordinates of its first cell. The number of | |

* candidate colormap entries is returned, and their colormap indexes are | |

* placed in colorlist[]. | |

* This routine uses Heckbert's "locally sorted search" criterion to select | |

* the colors that need further consideration. | |

*/ | |

{ | |

int numcolors = cinfo->actual_number_of_colors; | |

int maxc0, maxc1, maxc2; | |

int centerc0, centerc1, centerc2; | |

int i, x, ncolors; | |

INT32 minmaxdist, min_dist, max_dist, tdist; | |

INT32 mindist[MAXNUMCOLORS]; /* min distance to colormap entry i */ | |

/* Compute true coordinates of update box's upper corner and center. | |

* Actually we compute the coordinates of the center of the upper-corner | |

* histogram cell, which are the upper bounds of the volume we care about. | |

* Note that since ">>" rounds down, the "center" values may be closer to | |

* min than to max; hence comparisons to them must be "<=", not "<". | |

*/ | |

maxc0 = minc0 + ((1 << BOX_C0_SHIFT) - (1 << C0_SHIFT)); | |

centerc0 = (minc0 + maxc0) >> 1; | |

maxc1 = minc1 + ((1 << BOX_C1_SHIFT) - (1 << C1_SHIFT)); | |

centerc1 = (minc1 + maxc1) >> 1; | |

maxc2 = minc2 + ((1 << BOX_C2_SHIFT) - (1 << C2_SHIFT)); | |

centerc2 = (minc2 + maxc2) >> 1; | |

/* For each color in colormap, find: | |

* 1. its minimum squared-distance to any point in the update box | |

* (zero if color is within update box); | |

* 2. its maximum squared-distance to any point in the update box. | |

* Both of these can be found by considering only the corners of the box. | |

* We save the minimum distance for each color in mindist[]; | |

* only the smallest maximum distance is of interest. | |

*/ | |

minmaxdist = 0x7FFFFFFFL; | |

for (i = 0; i < numcolors; i++) { | |

/* We compute the squared-c0-distance term, then add in the other two. */ | |

x = GETJSAMPLE(cinfo->colormap[0][i]); | |

if (x < minc0) { | |

tdist = (x - minc0) * C0_SCALE; | |

min_dist = tdist*tdist; | |

tdist = (x - maxc0) * C0_SCALE; | |

max_dist = tdist*tdist; | |

} else if (x > maxc0) { | |

tdist = (x - maxc0) * C0_SCALE; | |

min_dist = tdist*tdist; | |

tdist = (x - minc0) * C0_SCALE; | |

max_dist = tdist*tdist; | |

} else { | |

/* within cell range so no contribution to min_dist */ | |

min_dist = 0; | |

if (x <= centerc0) { | |

tdist = (x - maxc0) * C0_SCALE; | |

max_dist = tdist*tdist; | |

} else { | |

tdist = (x - minc0) * C0_SCALE; | |

max_dist = tdist*tdist; | |

} | |

} | |

x = GETJSAMPLE(cinfo->colormap[1][i]); | |

if (x < minc1) { | |

tdist = (x - minc1) * C1_SCALE; | |

min_dist += tdist*tdist; | |

tdist = (x - maxc1) * C1_SCALE; | |

max_dist += tdist*tdist; | |

} else if (x > maxc1) { | |

tdist = (x - maxc1) * C1_SCALE; | |

min_dist += tdist*tdist; | |

tdist = (x - minc1) * C1_SCALE; | |

max_dist += tdist*tdist; | |

} else { | |

/* within cell range so no contribution to min_dist */ | |

if (x <= centerc1) { | |

tdist = (x - maxc1) * C1_SCALE; | |

max_dist += tdist*tdist; | |

} else { | |

tdist = (x - minc1) * C1_SCALE; | |

max_dist += tdist*tdist; | |

} | |

} | |

x = GETJSAMPLE(cinfo->colormap[2][i]); | |

if (x < minc2) { | |

tdist = (x - minc2) * C2_SCALE; | |

min_dist += tdist*tdist; | |

tdist = (x - maxc2) * C2_SCALE; | |

max_dist += tdist*tdist; | |

} else if (x > maxc2) { | |

tdist = (x - maxc2) * C2_SCALE; | |

min_dist += tdist*tdist; | |

tdist = (x - minc2) * C2_SCALE; | |

max_dist += tdist*tdist; | |

} else { | |

/* within cell range so no contribution to min_dist */ | |

if (x <= centerc2) { | |

tdist = (x - maxc2) * C2_SCALE; | |

max_dist += tdist*tdist; | |

} else { | |

tdist = (x - minc2) * C2_SCALE; | |

max_dist += tdist*tdist; | |

} | |

} | |

mindist[i] = min_dist; /* save away the results */ | |

if (max_dist < minmaxdist) | |

minmaxdist = max_dist; | |

} | |

/* Now we know that no cell in the update box is more than minmaxdist | |

* away from some colormap entry. Therefore, only colors that are | |

* within minmaxdist of some part of the box need be considered. | |

*/ | |

ncolors = 0; | |

for (i = 0; i < numcolors; i++) { | |

if (mindist[i] <= minmaxdist) | |

colorlist[ncolors++] = (JSAMPLE) i; | |

} | |

return ncolors; | |

} | |

LOCAL(void) | |

find_best_colors (j_decompress_ptr cinfo, int minc0, int minc1, int minc2, | |

int numcolors, JSAMPLE colorlist[], JSAMPLE bestcolor[]) | |

/* Find the closest colormap entry for each cell in the update box, | |

* given the list of candidate colors prepared by find_nearby_colors. | |

* Return the indexes of the closest entries in the bestcolor[] array. | |

* This routine uses Thomas' incremental distance calculation method to | |

* find the distance from a colormap entry to successive cells in the box. | |

*/ | |

{ | |

int ic0, ic1, ic2; | |

int i, icolor; | |

register INT32 * bptr; /* pointer into bestdist[] array */ | |

JSAMPLE * cptr; /* pointer into bestcolor[] array */ | |

INT32 dist0, dist1; /* initial distance values */ | |

register INT32 dist2; /* current distance in inner loop */ | |

INT32 xx0, xx1; /* distance increments */ | |

register INT32 xx2; | |

INT32 inc0, inc1, inc2; /* initial values for increments */ | |

/* This array holds the distance to the nearest-so-far color for each cell */ | |

INT32 bestdist[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS]; | |

/* Initialize best-distance for each cell of the update box */ | |

bptr = bestdist; | |

for (i = BOX_C0_ELEMS*BOX_C1_ELEMS*BOX_C2_ELEMS-1; i >= 0; i--) | |

*bptr++ = 0x7FFFFFFFL; | |

/* For each color selected by find_nearby_colors, | |

* compute its distance to the center of each cell in the box. | |

* If that's less than best-so-far, update best distance and color number. | |

*/ | |

/* Nominal steps between cell centers ("x" in Thomas article) */ | |

#define STEP_C0 ((1 << C0_SHIFT) * C0_SCALE) | |

#define STEP_C1 ((1 << C1_SHIFT) * C1_SCALE) | |

#define STEP_C2 ((1 << C2_SHIFT) * C2_SCALE) | |

for (i = 0; i < numcolors; i++) { | |

icolor = GETJSAMPLE(colorlist[i]); | |

/* Compute (square of) distance from minc0/c1/c2 to this color */ | |

inc0 = (minc0 - GETJSAMPLE(cinfo->colormap[0][icolor])) * C0_SCALE; | |

dist0 = inc0*inc0; | |

inc1 = (minc1 - GETJSAMPLE(cinfo->colormap[1][icolor])) * C1_SCALE; | |

dist0 += inc1*inc1; | |

inc2 = (minc2 - GETJSAMPLE(cinfo->colormap[2][icolor])) * C2_SCALE; | |

dist0 += inc2*inc2; | |

/* Form the initial difference increments */ | |

inc0 = inc0 * (2 * STEP_C0) + STEP_C0 * STEP_C0; | |

inc1 = inc1 * (2 * STEP_C1) + STEP_C1 * STEP_C1; | |

inc2 = inc2 * (2 * STEP_C2) + STEP_C2 * STEP_C2; | |

/* Now loop over all cells in box, updating distance per Thomas method */ | |

bptr = bestdist; | |

cptr = bestcolor; | |

xx0 = inc0; | |

for (ic0 = BOX_C0_ELEMS-1; ic0 >= 0; ic0--) { | |

dist1 = dist0; | |

xx1 = inc1; | |

for (ic1 = BOX_C1_ELEMS-1; ic1 >= 0; ic1--) { | |

dist2 = dist1; | |

xx2 = inc2; | |

for (ic2 = BOX_C2_ELEMS-1; ic2 >= 0; ic2--) { | |

if (dist2 < *bptr) { | |

*bptr = dist2; | |

*cptr = (JSAMPLE) icolor; | |

} | |

dist2 += xx2; | |

xx2 += 2 * STEP_C2 * STEP_C2; | |

bptr++; | |

cptr++; | |

} | |

dist1 += xx1; | |

xx1 += 2 * STEP_C1 * STEP_C1; | |

} | |

dist0 += xx0; | |

xx0 += 2 * STEP_C0 * STEP_C0; | |

} | |

} | |

} | |

LOCAL(void) | |

fill_inverse_cmap (j_decompress_ptr cinfo, int c0, int c1, int c2) | |

/* Fill the inverse-colormap entries in the update box that contains */ | |

/* histogram cell c0/c1/c2. (Only that one cell MUST be filled, but */ | |

/* we can fill as many others as we wish.) */ | |

{ | |

my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; | |

hist3d histogram = cquantize->histogram; | |

int minc0, minc1, minc2; /* lower left corner of update box */ | |

int ic0, ic1, ic2; | |

register JSAMPLE * cptr; /* pointer into bestcolor[] array */ | |

register histptr cachep; /* pointer into main cache array */ | |

/* This array lists the candidate colormap indexes. */ | |

JSAMPLE colorlist[MAXNUMCOLORS]; | |

int numcolors; /* number of candidate colors */ | |

/* This array holds the actually closest colormap index for each cell. */ | |

JSAMPLE bestcolor[BOX_C0_ELEMS * BOX_C1_ELEMS * BOX_C2_ELEMS]; | |

/* Convert cell coordinates to update box ID */ | |

c0 >>= BOX_C0_LOG; | |

c1 >>= BOX_C1_LOG; | |

c2 >>= BOX_C2_LOG; | |

/* Compute true coordinates of update box's origin corner. | |

* Actually we compute the coordinates of the center of the corner | |

* histogram cell, which are the lower bounds of the volume we care about. | |

*/ | |

minc0 = (c0 << BOX_C0_SHIFT) + ((1 << C0_SHIFT) >> 1); | |

minc1 = (c1 << BOX_C1_SHIFT) + ((1 << C1_SHIFT) >> 1); | |

minc2 = (c2 << BOX_C2_SHIFT) + ((1 << C2_SHIFT) >> 1); | |

/* Determine which colormap entries are close enough to be candidates | |

* for the nearest entry to some cell in the update box. | |

*/ | |

numcolors = find_nearby_colors(cinfo, minc0, minc1, minc2, colorlist); | |

/* Determine the actually nearest colors. */ | |

find_best_colors(cinfo, minc0, minc1, minc2, numcolors, colorlist, | |

bestcolor); | |

/* Save the best color numbers (plus 1) in the main cache array */ | |

c0 <<= BOX_C0_LOG; /* convert ID back to base cell indexes */ | |

c1 <<= BOX_C1_LOG; | |

c2 <<= BOX_C2_LOG; | |

cptr = bestcolor; | |

for (ic0 = 0; ic0 < BOX_C0_ELEMS; ic0++) { | |

for (ic1 = 0; ic1 < BOX_C1_ELEMS; ic1++) { | |

cachep = & histogram[c0+ic0][c1+ic1][c2]; | |

for (ic2 = 0; ic2 < BOX_C2_ELEMS; ic2++) { | |

*cachep++ = (histcell) (GETJSAMPLE(*cptr++) + 1); | |

} | |

} | |

} | |

} | |

/* | |

* Map some rows of pixels to the output colormapped representation. | |

*/ | |

METHODDEF(void) | |

pass2_no_dither (j_decompress_ptr cinfo, | |

JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows) | |

/* This version performs no dithering */ | |

{ | |

my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; | |

hist3d histogram = cquantize->histogram; | |

register JSAMPROW inptr, outptr; | |

register histptr cachep; | |

register int c0, c1, c2; | |

int row; | |

JDIMENSION col; | |

JDIMENSION width = cinfo->output_width; | |

for (row = 0; row < num_rows; row++) { | |

inptr = input_buf[row]; | |

outptr = output_buf[row]; | |

for (col = width; col > 0; col--) { | |

/* get pixel value and index into the cache */ | |

c0 = GETJSAMPLE(*inptr++) >> C0_SHIFT; | |

c1 = GETJSAMPLE(*inptr++) >> C1_SHIFT; | |

c2 = GETJSAMPLE(*inptr++) >> C2_SHIFT; | |

cachep = & histogram[c0][c1][c2]; | |

/* If we have not seen this color before, find nearest colormap entry */ | |

/* and update the cache */ | |

if (*cachep == 0) | |

fill_inverse_cmap(cinfo, c0,c1,c2); | |

/* Now emit the colormap index for this cell */ | |

*outptr++ = (JSAMPLE) (*cachep - 1); | |

} | |

} | |

} | |

METHODDEF(void) | |

pass2_fs_dither (j_decompress_ptr cinfo, | |

JSAMPARRAY input_buf, JSAMPARRAY output_buf, int num_rows) | |

/* This version performs Floyd-Steinberg dithering */ | |

{ | |

my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; | |

hist3d histogram = cquantize->histogram; | |

register LOCFSERROR cur0, cur1, cur2; /* current error or pixel value */ | |

LOCFSERROR belowerr0, belowerr1, belowerr2; /* error for pixel below cur */ | |

LOCFSERROR bpreverr0, bpreverr1, bpreverr2; /* error for below/prev col */ | |

register FSERRPTR errorptr; /* => fserrors[] at column before current */ | |

JSAMPROW inptr; /* => current input pixel */ | |

JSAMPROW outptr; /* => current output pixel */ | |

histptr cachep; | |

int dir; /* +1 or -1 depending on direction */ | |

int dir3; /* 3*dir, for advancing inptr & errorptr */ | |

int row; | |

JDIMENSION col; | |

JDIMENSION width = cinfo->output_width; | |

JSAMPLE *range_limit = cinfo->sample_range_limit; | |

int *error_limit = cquantize->error_limiter; | |

JSAMPROW colormap0 = cinfo->colormap[0]; | |

JSAMPROW colormap1 = cinfo->colormap[1]; | |

JSAMPROW colormap2 = cinfo->colormap[2]; | |

SHIFT_TEMPS | |

for (row = 0; row < num_rows; row++) { | |

inptr = input_buf[row]; | |

outptr = output_buf[row]; | |

if (cquantize->on_odd_row) { | |

/* work right to left in this row */ | |

inptr += (width-1) * 3; /* so point to rightmost pixel */ | |

outptr += width-1; | |

dir = -1; | |

dir3 = -3; | |

errorptr = cquantize->fserrors + (width+1)*3; /* => entry after last column */ | |

cquantize->on_odd_row = FALSE; /* flip for next time */ | |

} else { | |

/* work left to right in this row */ | |

dir = 1; | |

dir3 = 3; | |

errorptr = cquantize->fserrors; /* => entry before first real column */ | |

cquantize->on_odd_row = TRUE; /* flip for next time */ | |

} | |

/* Preset error values: no error propagated to first pixel from left */ | |

cur0 = cur1 = cur2 = 0; | |

/* and no error propagated to row below yet */ | |

belowerr0 = belowerr1 = belowerr2 = 0; | |

bpreverr0 = bpreverr1 = bpreverr2 = 0; | |

for (col = width; col > 0; col--) { | |

/* curN holds the error propagated from the previous pixel on the | |

* current line. Add the error propagated from the previous line | |

* to form the complete error correction term for this pixel, and | |

* round the error term (which is expressed * 16) to an integer. | |

* RIGHT_SHIFT rounds towards minus infinity, so adding 8 is correct | |

* for either sign of the error value. | |

* Note: errorptr points to *previous* column's array entry. | |

*/ | |

cur0 = RIGHT_SHIFT(cur0 + errorptr[dir3+0] + 8, 4); | |

cur1 = RIGHT_SHIFT(cur1 + errorptr[dir3+1] + 8, 4); | |

cur2 = RIGHT_SHIFT(cur2 + errorptr[dir3+2] + 8, 4); | |

/* Limit the error using transfer function set by init_error_limit. | |

* See comments with init_error_limit for rationale. | |

*/ | |

cur0 = error_limit[cur0]; | |

cur1 = error_limit[cur1]; | |

cur2 = error_limit[cur2]; | |

/* Form pixel value + error, and range-limit to 0..MAXJSAMPLE. | |

* The maximum error is +- MAXJSAMPLE (or less with error limiting); | |

* this sets the required size of the range_limit array. | |

*/ | |

cur0 += GETJSAMPLE(inptr[0]); | |

cur1 += GETJSAMPLE(inptr[1]); | |

cur2 += GETJSAMPLE(inptr[2]); | |

cur0 = GETJSAMPLE(range_limit[cur0]); | |

cur1 = GETJSAMPLE(range_limit[cur1]); | |

cur2 = GETJSAMPLE(range_limit[cur2]); | |

/* Index into the cache with adjusted pixel value */ | |

cachep = & histogram[cur0>>C0_SHIFT][cur1>>C1_SHIFT][cur2>>C2_SHIFT]; | |

/* If we have not seen this color before, find nearest colormap */ | |

/* entry and update the cache */ | |

if (*cachep == 0) | |

fill_inverse_cmap(cinfo, cur0>>C0_SHIFT,cur1>>C1_SHIFT,cur2>>C2_SHIFT); | |

/* Now emit the colormap index for this cell */ | |

{ register int pixcode = *cachep - 1; | |

*outptr = (JSAMPLE) pixcode; | |

/* Compute representation error for this pixel */ | |

cur0 -= GETJSAMPLE(colormap0[pixcode]); | |

cur1 -= GETJSAMPLE(colormap1[pixcode]); | |

cur2 -= GETJSAMPLE(colormap2[pixcode]); | |

} | |

/* Compute error fractions to be propagated to adjacent pixels. | |

* Add these into the running sums, and simultaneously shift the | |

* next-line error sums left by 1 column. | |

*/ | |

{ register LOCFSERROR bnexterr, delta; | |

bnexterr = cur0; /* Process component 0 */ | |

delta = cur0 * 2; | |

cur0 += delta; /* form error * 3 */ | |

errorptr[0] = (FSERROR) (bpreverr0 + cur0); | |

cur0 += delta; /* form error * 5 */ | |

bpreverr0 = belowerr0 + cur0; | |

belowerr0 = bnexterr; | |

cur0 += delta; /* form error * 7 */ | |

bnexterr = cur1; /* Process component 1 */ | |

delta = cur1 * 2; | |

cur1 += delta; /* form error * 3 */ | |

errorptr[1] = (FSERROR) (bpreverr1 + cur1); | |

cur1 += delta; /* form error * 5 */ | |

bpreverr1 = belowerr1 + cur1; | |

belowerr1 = bnexterr; | |

cur1 += delta; /* form error * 7 */ | |

bnexterr = cur2; /* Process component 2 */ | |

delta = cur2 * 2; | |

cur2 += delta; /* form error * 3 */ | |

errorptr[2] = (FSERROR) (bpreverr2 + cur2); | |

cur2 += delta; /* form error * 5 */ | |

bpreverr2 = belowerr2 + cur2; | |

belowerr2 = bnexterr; | |

cur2 += delta; /* form error * 7 */ | |

} | |

/* At this point curN contains the 7/16 error value to be propagated | |

* to the next pixel on the current line, and all the errors for the | |

* next line have been shifted over. We are therefore ready to move on. | |

*/ | |

inptr += dir3; /* Advance pixel pointers to next column */ | |

outptr += dir; | |

errorptr += dir3; /* advance errorptr to current column */ | |

} | |

/* Post-loop cleanup: we must unload the final error values into the | |

* final fserrors[] entry. Note we need not unload belowerrN because | |

* it is for the dummy column before or after the actual array. | |

*/ | |

errorptr[0] = (FSERROR) bpreverr0; /* unload prev errs into array */ | |

errorptr[1] = (FSERROR) bpreverr1; | |

errorptr[2] = (FSERROR) bpreverr2; | |

} | |

} | |

/* | |

* Initialize the error-limiting transfer function (lookup table). | |

* The raw F-S error computation can potentially compute error values of up to | |

* +- MAXJSAMPLE. But we want the maximum correction applied to a pixel to be | |

* much less, otherwise obviously wrong pixels will be created. (Typical | |

* effects include weird fringes at color-area boundaries, isolated bright | |

* pixels in a dark area, etc.) The standard advice for avoiding this problem | |

* is to ensure that the "corners" of the color cube are allocated as output | |

* colors; then repeated errors in the same direction cannot cause cascading | |

* error buildup. However, that only prevents the error from getting | |

* completely out of hand; Aaron Giles reports that error limiting improves | |

* the results even with corner colors allocated. | |

* A simple clamping of the error values to about +- MAXJSAMPLE/8 works pretty | |

* well, but the smoother transfer function used below is even better. Thanks | |

* to Aaron Giles for this idea. | |

*/ | |

LOCAL(void) | |

init_error_limit (j_decompress_ptr cinfo) | |

/* Allocate and fill in the error_limiter table */ | |

{ | |

my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; | |

int * table; | |

int in, out; | |

table = (int *) (*cinfo->mem->alloc_small) | |

((j_common_ptr) cinfo, JPOOL_IMAGE, (MAXJSAMPLE*2+1) * SIZEOF(int)); | |

table += MAXJSAMPLE; /* so can index -MAXJSAMPLE .. +MAXJSAMPLE */ | |

cquantize->error_limiter = table; | |

#define STEPSIZE ((MAXJSAMPLE+1)/16) | |

/* Map errors 1:1 up to +- MAXJSAMPLE/16 */ | |

out = 0; | |

for (in = 0; in < STEPSIZE; in++, out++) { | |

table[in] = out; table[-in] = -out; | |

} | |

/* Map errors 1:2 up to +- 3*MAXJSAMPLE/16 */ | |

for (; in < STEPSIZE*3; in++, out += (in&1) ? 0 : 1) { | |

table[in] = out; table[-in] = -out; | |

} | |

/* Clamp the rest to final out value (which is (MAXJSAMPLE+1)/8) */ | |

for (; in <= MAXJSAMPLE; in++) { | |

table[in] = out; table[-in] = -out; | |

} | |

#undef STEPSIZE | |

} | |

/* | |

* Finish up at the end of each pass. | |

*/ | |

METHODDEF(void) | |

finish_pass1 (j_decompress_ptr cinfo) | |

{ | |

my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; | |

/* Select the representative colors and fill in cinfo->colormap */ | |

cinfo->colormap = cquantize->sv_colormap; | |

select_colors(cinfo, cquantize->desired); | |

/* Force next pass to zero the color index table */ | |

cquantize->needs_zeroed = TRUE; | |

} | |

METHODDEF(void) | |

finish_pass2 (j_decompress_ptr cinfo) | |

{ | |

/* no work */ | |

} | |

/* | |

* Initialize for each processing pass. | |

*/ | |

METHODDEF(void) | |

start_pass_2_quant (j_decompress_ptr cinfo, boolean is_pre_scan) | |

{ | |

my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; | |

hist3d histogram = cquantize->histogram; | |

int i; | |

/* Only F-S dithering or no dithering is supported. */ | |

/* If user asks for ordered dither, give him F-S. */ | |

if (cinfo->dither_mode != JDITHER_NONE) | |

cinfo->dither_mode = JDITHER_FS; | |

if (is_pre_scan) { | |

/* Set up method pointers */ | |

cquantize->pub.color_quantize = prescan_quantize; | |

cquantize->pub.finish_pass = finish_pass1; | |

cquantize->needs_zeroed = TRUE; /* Always zero histogram */ | |

} else { | |

/* Set up method pointers */ | |

if (cinfo->dither_mode == JDITHER_FS) | |

cquantize->pub.color_quantize = pass2_fs_dither; | |

else | |

cquantize->pub.color_quantize = pass2_no_dither; | |

cquantize->pub.finish_pass = finish_pass2; | |

/* Make sure color count is acceptable */ | |

i = cinfo->actual_number_of_colors; | |

if (i < 1) | |

ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 1); | |

if (i > MAXNUMCOLORS) | |

ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS); | |

if (cinfo->dither_mode == JDITHER_FS) { | |

size_t arraysize = (size_t) ((cinfo->output_width + 2) * | |

(3 * SIZEOF(FSERROR))); | |

/* Allocate Floyd-Steinberg workspace if we didn't already. */ | |

if (cquantize->fserrors == NULL) | |

cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large) | |

((j_common_ptr) cinfo, JPOOL_IMAGE, arraysize); | |

/* Initialize the propagated errors to zero. */ | |

jzero_far((void FAR *) cquantize->fserrors, arraysize); | |

/* Make the error-limit table if we didn't already. */ | |

if (cquantize->error_limiter == NULL) | |

init_error_limit(cinfo); | |

cquantize->on_odd_row = FALSE; | |

} | |

} | |

/* Zero the histogram or inverse color map, if necessary */ | |

if (cquantize->needs_zeroed) { | |

for (i = 0; i < HIST_C0_ELEMS; i++) { | |

jzero_far((void FAR *) histogram[i], | |

HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell)); | |

} | |

cquantize->needs_zeroed = FALSE; | |

} | |

} | |

/* | |

* Switch to a new external colormap between output passes. | |

*/ | |

METHODDEF(void) | |

new_color_map_2_quant (j_decompress_ptr cinfo) | |

{ | |

my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize; | |

/* Reset the inverse color map */ | |

cquantize->needs_zeroed = TRUE; | |

} | |

/* | |

* Module initialization routine for 2-pass color quantization. | |

*/ | |

GLOBAL(void) | |

jinit_2pass_quantizer (j_decompress_ptr cinfo) | |

{ | |

my_cquantize_ptr cquantize; | |

int i; | |

cquantize = (my_cquantize_ptr) | |

(*cinfo->mem->alloc_small) ((j_common_ptr) cinfo, JPOOL_IMAGE, | |

SIZEOF(my_cquantizer)); | |

cinfo->cquantize = (struct jpeg_color_quantizer *) cquantize; | |

cquantize->pub.start_pass = start_pass_2_quant; | |

cquantize->pub.new_color_map = new_color_map_2_quant; | |

cquantize->fserrors = NULL; /* flag optional arrays not allocated */ | |

cquantize->error_limiter = NULL; | |

/* Make sure jdmaster didn't give me a case I can't handle */ | |

if (cinfo->out_color_components != 3) | |

ERREXIT(cinfo, JERR_NOTIMPL); | |

/* Allocate the histogram/inverse colormap storage */ | |

cquantize->histogram = (hist3d) (*cinfo->mem->alloc_small) | |

((j_common_ptr) cinfo, JPOOL_IMAGE, HIST_C0_ELEMS * SIZEOF(hist2d)); | |

for (i = 0; i < HIST_C0_ELEMS; i++) { | |

cquantize->histogram[i] = (hist2d) (*cinfo->mem->alloc_large) | |

((j_common_ptr) cinfo, JPOOL_IMAGE, | |

HIST_C1_ELEMS*HIST_C2_ELEMS * SIZEOF(histcell)); | |

} | |

cquantize->needs_zeroed = TRUE; /* histogram is garbage now */ | |

/* Allocate storage for the completed colormap, if required. | |

* We do this now since it is FAR storage and may affect | |

* the memory manager's space calculations. | |

*/ | |

if (cinfo->enable_2pass_quant) { | |

/* Make sure color count is acceptable */ | |

int desired = cinfo->desired_number_of_colors; | |

/* Lower bound on # of colors ... somewhat arbitrary as long as > 0 */ | |

if (desired < 8) | |

ERREXIT1(cinfo, JERR_QUANT_FEW_COLORS, 8); | |

/* Make sure colormap indexes can be represented by JSAMPLEs */ | |

if (desired > MAXNUMCOLORS) | |

ERREXIT1(cinfo, JERR_QUANT_MANY_COLORS, MAXNUMCOLORS); | |

cquantize->sv_colormap = (*cinfo->mem->alloc_sarray) | |

((j_common_ptr) cinfo,JPOOL_IMAGE, (JDIMENSION) desired, (JDIMENSION) 3); | |

cquantize->desired = desired; | |

} else | |

cquantize->sv_colormap = NULL; | |

/* Only F-S dithering or no dithering is supported. */ | |

/* If user asks for ordered dither, give him F-S. */ | |

if (cinfo->dither_mode != JDITHER_NONE) | |

cinfo->dither_mode = JDITHER_FS; | |

/* Allocate Floyd-Steinberg workspace if necessary. | |

* This isn't really needed until pass 2, but again it is FAR storage. | |

* Although we will cope with a later change in dither_mode, | |

* we do not promise to honor max_memory_to_use if dither_mode changes. | |

*/ | |

if (cinfo->dither_mode == JDITHER_FS) { | |

cquantize->fserrors = (FSERRPTR) (*cinfo->mem->alloc_large) | |

((j_common_ptr) cinfo, JPOOL_IMAGE, | |

(size_t) ((cinfo->output_width + 2) * (3 * SIZEOF(FSERROR)))); | |

/* Might as well create the error-limiting table too. */ | |

init_error_limit(cinfo); | |

} | |

} | |

#endif /* QUANT_2PASS_SUPPORTED */ |