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1310 lines
47 KiB
1310 lines
47 KiB
/*
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* jquant2.c
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*
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* Copyright (C) 1991-1996, Thomas G. Lane.
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* This file is part of the Independent JPEG Group's software.
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* For conditions of distribution and use, see the accompanying README file.
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*
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* This file contains 2-pass color quantization (color mapping) routines.
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* These routines provide selection of a custom color map for an image,
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* followed by mapping of the image to that color map, with optional
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* Floyd-Steinberg dithering.
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* It is also possible to use just the second pass to map to an arbitrary
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* externally-given color map.
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*
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* Note: ordered dithering is not supported, since there isn't any fast
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* way to compute intercolor distances; it's unclear that ordered dither's
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* fundamental assumptions even hold with an irregularly spaced color map.
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*/
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#define JPEG_INTERNALS
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#include "jinclude.h"
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#include "jpeglib.h"
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#ifdef QUANT_2PASS_SUPPORTED
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/*
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* This module implements the well-known Heckbert paradigm for color
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* quantization. Most of the ideas used here can be traced back to
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* Heckbert's seminal paper
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* Heckbert, Paul. "Color Image Quantization for Frame Buffer Display",
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* Proc. SIGGRAPH '82, Computer Graphics v.16 #3 (July 1982), pp 297-304.
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*
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* In the first pass over the image, we accumulate a histogram showing the
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* usage count of each possible color. To keep the histogram to a reasonable
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* size, we reduce the precision of the input; typical practice is to retain
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* 5 or 6 bits per color, so that 8 or 4 different input values are counted
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* in the same histogram cell.
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*
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* Next, the color-selection step begins with a box representing the whole
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* color space, and repeatedly splits the "largest" remaining box until we
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* have as many boxes as desired colors. Then the mean color in each
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* remaining box becomes one of the possible output colors.
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*
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* The second pass over the image maps each input pixel to the closest output
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* color (optionally after applying a Floyd-Steinberg dithering correction).
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* This mapping is logically trivial, but making it go fast enough requires
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* considerable care.
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*
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* Heckbert-style quantizers vary a good deal in their policies for choosing
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* the "largest" box and deciding where to cut it. The particular policies
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* used here have proved out well in experimental comparisons, but better ones
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* may yet be found.
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*
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* In earlier versions of the IJG code, this module quantized in YCbCr color
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* space, processing the raw upsampled data without a color conversion step.
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* This allowed the color conversion math to be done only once per colormap
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* entry, not once per pixel. However, that optimization precluded other
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* useful optimizations (such as merging color conversion with upsampling)
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* and it also interfered with desired capabilities such as quantizing to an
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* externally-supplied colormap. We have therefore abandoned that approach.
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* The present code works in the post-conversion color space, typically RGB.
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*
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* To improve the visual quality of the results, we actually work in scaled
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* RGB space, giving G distances more weight than R, and R in turn more than
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* B. To do everything in integer math, we must use integer scale factors.
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* The 2/3/1 scale factors used here correspond loosely to the relative
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* weights of the colors in the NTSC grayscale equation.
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* If you want to use this code to quantize a non-RGB color space, you'll
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* probably need to change these scale factors.
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*/
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#define R_SCALE 2 /* scale R distances by this much */
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#define G_SCALE 3 /* scale G distances by this much */
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#define B_SCALE 1 /* and B by this much */
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/* Relabel R/G/B as components 0/1/2, respecting the RGB ordering defined
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* in jmorecfg.h. As the code stands, it will do the right thing for R,G,B
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* and B,G,R orders. If you define some other weird order in jmorecfg.h,
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* you'll get compile errors until you extend this logic. In that case
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* you'll probably want to tweak the histogram sizes too.
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*/
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#if RGB_RED == 0
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#define C0_SCALE R_SCALE
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#endif
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#if RGB_BLUE == 0
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#define C0_SCALE B_SCALE
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#endif
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#if RGB_GREEN == 1
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#define C1_SCALE G_SCALE
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#endif
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#if RGB_RED == 2
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#define C2_SCALE R_SCALE
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#endif
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#if RGB_BLUE == 2
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#define C2_SCALE B_SCALE
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#endif
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/*
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* First we have the histogram data structure and routines for creating it.
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*
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* The number of bits of precision can be adjusted by changing these symbols.
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* We recommend keeping 6 bits for G and 5 each for R and B.
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* If you have plenty of memory and cycles, 6 bits all around gives marginally
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* better results; if you are short of memory, 5 bits all around will save
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* some space but degrade the results.
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* To maintain a fully accurate histogram, we'd need to allocate a "long"
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* (preferably unsigned long) for each cell. In practice this is overkill;
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* we can get by with 16 bits per cell. Few of the cell counts will overflow,
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* and clamping those that do overflow to the maximum value will give close-
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* enough results. This reduces the recommended histogram size from 256Kb
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* to 128Kb, which is a useful savings on PC-class machines.
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* (In the second pass the histogram space is re-used for pixel mapping data;
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* in that capacity, each cell must be able to store zero to the number of
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* desired colors. 16 bits/cell is plenty for that too.)
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* Since the JPEG code is intended to run in small memory model on 80x86
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* machines, we can't just allocate the histogram in one chunk. Instead
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* of a true 3-D array, we use a row of pointers to 2-D arrays. Each
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* pointer corresponds to a C0 value (typically 2^5 = 32 pointers) and
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* each 2-D array has 2^6*2^5 = 2048 or 2^6*2^6 = 4096 entries. Note that
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* on 80x86 machines, the pointer row is in near memory but the actual
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* arrays are in far memory (same arrangement as we use for image arrays).
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*/
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#define MAXNUMCOLORS (MAXJSAMPLE+1) /* maximum size of colormap */
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/* These will do the right thing for either R,G,B or B,G,R color order,
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* but you may not like the results for other color orders.
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*/
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#define HIST_C0_BITS 5 /* bits of precision in R/B histogram */
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#define HIST_C1_BITS 6 /* bits of precision in G histogram */
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#define HIST_C2_BITS 5 /* bits of precision in B/R histogram */
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/* Number of elements along histogram axes. */
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#define HIST_C0_ELEMS (1<<HIST_C0_BITS)
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#define HIST_C1_ELEMS (1<<HIST_C1_BITS)
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#define HIST_C2_ELEMS (1<<HIST_C2_BITS)
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/* These are the amounts to shift an input value to get a histogram index. */
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#define C0_SHIFT (BITS_IN_JSAMPLE-HIST_C0_BITS)
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#define C1_SHIFT (BITS_IN_JSAMPLE-HIST_C1_BITS)
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#define C2_SHIFT (BITS_IN_JSAMPLE-HIST_C2_BITS)
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typedef UINT16 histcell; /* histogram cell; prefer an unsigned type */
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typedef histcell FAR * histptr; /* for pointers to histogram cells */
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typedef histcell hist1d[HIST_C2_ELEMS]; /* typedefs for the array */
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typedef hist1d FAR * hist2d; /* type for the 2nd-level pointers */
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typedef hist2d * hist3d; /* type for top-level pointer */
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/* Declarations for Floyd-Steinberg dithering.
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*
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* Errors are accumulated into the array fserrors[], at a resolution of
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* 1/16th of a pixel count. The error at a given pixel is propagated
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* to its not-yet-processed neighbors using the standard F-S fractions,
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* ... (here) 7/16
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* 3/16 5/16 1/16
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* We work left-to-right on even rows, right-to-left on odd rows.
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*
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* We can get away with a single array (holding one row's worth of errors)
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* by using it to store the current row's errors at pixel columns not yet
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* processed, but the next row's errors at columns already processed. We
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* need only a few extra variables to hold the errors immediately around the
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* current column. (If we are lucky, those variables are in registers, but
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* even if not, they're probably cheaper to access than array elements are.)
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*
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* The fserrors[] array has (#columns + 2) entries; the extra entry at
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* each end saves us from special-casing the first and last pixels.
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* Each entry is three values long, one value for each color component.
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*
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* Note: on a wide image, we might not have enough room in a PC's near data
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* segment to hold the error array; so it is allocated with alloc_large.
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*/
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#if BITS_IN_JSAMPLE == 8
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typedef INT16 FSERROR; /* 16 bits should be enough */
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typedef int LOCFSERROR; /* use 'int' for calculation temps */
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#else
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typedef INT32 FSERROR; /* may need more than 16 bits */
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typedef INT32 LOCFSERROR; /* be sure calculation temps are big enough */
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#endif
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typedef FSERROR FAR *FSERRPTR; /* pointer to error array (in FAR storage!) */
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/* Private subobject */
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typedef struct {
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struct jpeg_color_quantizer pub; /* public fields */
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/* Space for the eventually created colormap is stashed here */
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JSAMPARRAY sv_colormap; /* colormap allocated at init time */
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int desired; /* desired # of colors = size of colormap */
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/* Variables for accumulating image statistics */
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hist3d histogram; /* pointer to the histogram */
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boolean needs_zeroed; /* TRUE if next pass must zero histogram */
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/* Variables for Floyd-Steinberg dithering */
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FSERRPTR fserrors; /* accumulated errors */
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boolean on_odd_row; /* flag to remember which row we are on */
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int * error_limiter; /* table for clamping the applied error */
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} my_cquantizer;
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typedef my_cquantizer * my_cquantize_ptr;
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/*
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* Prescan some rows of pixels.
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* In this module the prescan simply updates the histogram, which has been
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* initialized to zeroes by start_pass.
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* An output_buf parameter is required by the method signature, but no data
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* is actually output (in fact the buffer controller is probably passing a
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* NULL pointer).
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*/
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METHODDEF(void)
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prescan_quantize (j_decompress_ptr cinfo, JSAMPARRAY input_buf,
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JSAMPARRAY output_buf, int num_rows)
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{
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my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
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JSAMPROW ptr;
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histptr histp;
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hist3d histogram = cquantize->histogram;
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int row;
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JDIMENSION col;
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JDIMENSION width = cinfo->output_width;
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for (row = 0; row < num_rows; row++) {
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ptr = input_buf[row];
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for (col = width; col > 0; col--) {
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/* get pixel value and index into the histogram */
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histp = & histogram[GETJSAMPLE(ptr[0]) >> C0_SHIFT]
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[GETJSAMPLE(ptr[1]) >> C1_SHIFT]
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[GETJSAMPLE(ptr[2]) >> C2_SHIFT];
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/* increment, check for overflow and undo increment if so. */
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if (++(*histp) <= 0)
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(*histp)--;
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ptr += 3;
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}
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}
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}
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/*
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* Next we have the really interesting routines: selection of a colormap
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* given the completed histogram.
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* These routines work with a list of "boxes", each representing a rectangular
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* subset of the input color space (to histogram precision).
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*/
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typedef struct {
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/* The bounds of the box (inclusive); expressed as histogram indexes */
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int c0min, c0max;
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int c1min, c1max;
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int c2min, c2max;
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/* The volume (actually 2-norm) of the box */
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INT32 volume;
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/* The number of nonzero histogram cells within this box */
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long colorcount;
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} box;
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typedef box * boxptr;
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LOCAL(boxptr)
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find_biggest_color_pop (boxptr boxlist, int numboxes)
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/* Find the splittable box with the largest color population */
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/* Returns NULL if no splittable boxes remain */
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{
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boxptr boxp;
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int i;
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long maxc = 0;
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boxptr which = NULL;
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for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
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if (boxp->colorcount > maxc && boxp->volume > 0) {
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which = boxp;
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maxc = boxp->colorcount;
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}
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}
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return which;
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}
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LOCAL(boxptr)
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find_biggest_volume (boxptr boxlist, int numboxes)
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/* Find the splittable box with the largest (scaled) volume */
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/* Returns NULL if no splittable boxes remain */
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{
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boxptr boxp;
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int i;
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INT32 maxv = 0;
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boxptr which = NULL;
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for (i = 0, boxp = boxlist; i < numboxes; i++, boxp++) {
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if (boxp->volume > maxv) {
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which = boxp;
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maxv = boxp->volume;
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}
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}
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return which;
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}
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LOCAL(void)
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update_box (j_decompress_ptr cinfo, boxptr boxp)
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/* Shrink the min/max bounds of a box to enclose only nonzero elements, */
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/* and recompute its volume and population */
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{
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my_cquantize_ptr cquantize = (my_cquantize_ptr) cinfo->cquantize;
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hist3d histogram = cquantize->histogram;
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histptr histp;
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int c0,c1,c2;
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int c0min,c0max,c1min,c1max,c2min,c2max;
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INT32 dist0,dist1,dist2;
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long ccount;
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c0min = boxp->c0min; c0max = boxp->c0max;
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c1min = boxp->c1min; c1max = boxp->c1max;
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c2min = boxp->c2min; c2max = boxp->c2max;
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if (c0max > c0min)
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for (c0 = c0min; c0 <= c0max; c0++)
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for (c1 = c1min; c1 <= c1max; c1++) {
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histp = & histogram[c0][c1][c2min];
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for (c2 = c2min; c2 <= c2max; c2++)
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if (*histp++ != 0) {
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boxp->c0min = c0min = c0;
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goto have_c0min;
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}
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}
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have_c0min:
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if (c0max > c0min)
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for (c0 = c0max; c0 >= c0min; c0--)
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for (c1 = c1min; c1 <= c1max; c1++) {
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histp = & histogram[c0][c1][c2min];
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for (c2 = c2min; c2 <= c2max; c2++)
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if (*histp++ != 0) {
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boxp->c0max = c0max = c0;
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goto have_c0max;
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}
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}
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have_c0max:
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if (c1max > c1min)
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for (c1 = c1min; c1 <= c1max; c1++)
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for (c0 = c0min; c0 <= c0max; c0++) {
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histp = & histogram[c0][c1][c2min];
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for (c2 = c2min; c2 <= c2max; c2++)
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if (*histp++ != 0) {
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boxp->c1min = c1min = c1;
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goto have_c1min;
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}
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}
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have_c1min:
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if (c1max > c1min)
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for (c1 = c1max; c1 >= c1min; c1--)
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for (c0 = c0min; c0 <= c0max; c0++) {
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histp = & histogram[c0][c1][c2min];
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for (c2 = c2min; c2 <= c2max; c2++)
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if (*histp++ != 0) {
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boxp->c1max = c1max = c1;
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goto have_c1max;
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}
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}
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have_c1max:
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if (c2max > c2min)
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for (c2 = c2min; c2 <= c2max; c2++)
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for (c0 = c0min; c0 <= c0max; c0++) {
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histp = & histogram[c0][c1min][c2];
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for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
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if (*histp != 0) {
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boxp->c2min = c2min = c2;
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goto have_c2min;
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}
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}
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have_c2min:
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if (c2max > c2min)
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for (c2 = c2max; c2 >= c2min; c2--)
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for (c0 = c0min; c0 <= c0max; c0++) {
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histp = & histogram[c0][c1min][c2];
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for (c1 = c1min; c1 <= c1max; c1++, histp += HIST_C2_ELEMS)
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if (*histp != 0) {
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boxp->c2max = c2max = c2;
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goto have_c2max;
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}
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}
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have_c2max:
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|
|
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/* Update box volume.
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* We use 2-norm rather than real volume here; this biases the method
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* against making long narrow boxes, and it has the side benefit that
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* a box is splittable iff norm > 0.
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* Since the differences are expressed in histogram-cell units,
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* we have to shift back to JSAMPLE units to get consistent distances;
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* after which, we scale according to the selected distance scale factors.
|
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*/
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dist0 = ((c0max - c0min) << C0_SHIFT) * C0_SCALE;
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dist1 = ((c1max - c1min) << C1_SHIFT) * C1_SCALE;
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dist2 = ((c2max - c2min) << C2_SHIFT) * C2_SCALE;
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boxp->volume = dist0*dist0 + dist1*dist1 + dist2*dist2;
|
|
|
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/* Now scan remaining volume of box and compute population */
|
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ccount = 0;
|
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for (c0 = c0min; c0 <= c0max; c0++)
|
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for (c1 = c1min; c1 <= c1max; c1++) {
|
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histp = & histogram[c0][c1][c2min];
|
|
for (c2 = c2min; c2 <= c2max; c2++, histp++)
|
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if (*histp != 0) {
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ccount++;
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}
|
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}
|
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boxp->colorcount = ccount;
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}
|
|
|
|
|
|
LOCAL(int)
|
|
median_cut (j_decompress_ptr cinfo, boxptr boxlist, int numboxes,
|
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int desired_colors)
|
|
/* Repeatedly select and split the largest box until we have enough boxes */
|
|
{
|
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int n,lb;
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int c0,c1,c2,cmax;
|
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boxptr b1,b2;
|
|
|
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while (numboxes < desired_colors) {
|
|
/* Select box to split.
|
|
* Current algorithm: by population for first half, then by volume.
|
|
*/
|
|
if (numboxes*2 <= desired_colors) {
|
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b1 = find_biggest_color_pop(boxlist, numboxes);
|
|
} else {
|
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b1 = find_biggest_volume(boxlist, numboxes);
|
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}
|
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if (b1 == NULL) /* no splittable boxes left! */
|
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break;
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b2 = &boxlist[numboxes]; /* where new box will go */
|
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/* 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 == 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; }
|
|
#endif
|
|
/* 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;
|
|
INT32 * bptr; /* pointer into bestdist[] array */
|
|
JSAMPLE * cptr; /* pointer into bestcolor[] array */
|
|
INT32 dist0, dist1; /* initial distance values */
|
|
INT32 dist2; /* current distance in inner loop */
|
|
INT32 xx0, xx1; /* distance increments */
|
|
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;
|
|
JSAMPLE * cptr; /* pointer into bestcolor[] array */
|
|
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;
|
|
JSAMPROW inptr, outptr;
|
|
histptr cachep;
|
|
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;
|
|
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 */
|
|
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 */
|
|
{ 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.
|
|
*/
|
|
{ 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,
|
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(size_t) ((cinfo->output_width + 2) * (3 * SIZEOF(FSERROR))));
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/* Might as well create the error-limiting table too. */
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init_error_limit(cinfo);
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|
}
|
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}
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#endif /* QUANT_2PASS_SUPPORTED */
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