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* Neural-Net quantization algorithm based on work of Anthony Dekker |
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*/ |
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< |
#include "standard.h" |
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#include "copyright.h" |
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#include "color.h" |
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#include <string.h> |
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#include "standard.h" |
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#include "color.h" |
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#include "random.h" |
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#include "clrtab.h" |
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#ifdef COMPAT_MODE |
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#define neu_init new_histo |
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static int clrtabsiz; |
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#ifndef DEFSMPFAC |
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#ifdef SPEED |
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#define DEFSMPFAC (240/SPEED+3) |
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#else |
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#define DEFSMPFAC 30 |
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#define DEFSMPFAC 3 |
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#endif |
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#endif |
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int samplefac = DEFSMPFAC; /* sampling factor */ |
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#define setskip(sp,n) ((sp)[0]=(n)>>16,(sp)[1]=((n)>>8)&255,(sp)[2]=(n)&255) |
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static cpyclrtab(); |
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static void initnet(void); |
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static void inxbuild(void); |
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static int inxsearch(int b, int g, int r); |
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static int contest(int b, int g, int r); |
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static void altersingle(int alpha, int i, int b, int g, int r); |
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static void alterneigh(int rad, int i, int b, int g, int r); |
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static void learn(void); |
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static void unbiasnet(void); |
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static void cpyclrtab(void); |
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neu_init(npixels) /* initialize our sample array */ |
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long npixels; |
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extern int |
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neu_init( /* initialize our sample array */ |
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long npixels |
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) |
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{ |
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register int nsleft; |
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register long sv; |
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} |
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neu_pixel(col) /* add pixel to our samples */ |
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register BYTE col[]; |
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extern void |
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neu_pixel( /* add pixel to our samples */ |
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register BYTE col[] |
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) |
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{ |
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if (!skipcount--) { |
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skipcount = nskip(cursamp); |
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} |
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neu_colrs(cs, n) /* add a scanline to our samples */ |
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register COLR *cs; |
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register int n; |
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extern void |
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neu_colrs( /* add a scanline to our samples */ |
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register COLR *cs, |
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register int n |
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) |
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{ |
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while (n > skipcount) { |
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cs += skipcount; |
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} |
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neu_clrtab(ncolors) /* make new color table using ncolors */ |
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int ncolors; |
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extern int |
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neu_clrtab( /* make new color table using ncolors */ |
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int ncolors |
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) |
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{ |
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clrtabsiz = ncolors; |
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if (clrtabsiz > 256) clrtabsiz = 256; |
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} |
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int |
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neu_map_pixel(col) /* get pixel for color */ |
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register BYTE col[]; |
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extern int |
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neu_map_pixel( /* get pixel for color */ |
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register BYTE col[] |
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) |
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{ |
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return(inxsearch(col[BLU],col[GRN],col[RED])); |
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} |
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neu_map_colrs(bs, cs, n) /* convert a scanline to color index values */ |
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register BYTE *bs; |
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register COLR *cs; |
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register int n; |
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extern void |
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neu_map_colrs( /* convert a scanline to color index values */ |
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register BYTE *bs, |
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register COLR *cs, |
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register int n |
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) |
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{ |
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while (n-- > 0) { |
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*bs++ = inxsearch(cs[0][BLU],cs[0][GRN],cs[0][RED]); |
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} |
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neu_dith_colrs(bs, cs, n) /* convert scanline to dithered index values */ |
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register BYTE *bs; |
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register COLR *cs; |
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int n; |
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extern void |
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neu_dith_colrs( /* convert scanline to dithered index values */ |
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register BYTE *bs, |
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register COLR *cs, |
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int n |
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) |
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{ |
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static short (*cerr)[3] = NULL; |
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static int N = 0; |
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return; |
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} |
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N = n; |
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bzero((char *)cerr, 3*N*sizeof(short)); |
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memset((char *)cerr, '\0', 3*N*sizeof(short)); |
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} |
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err[0] = err[1] = err[2] = 0; |
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for (x = 0; x < n; x++) { |
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#define lengthcount (nsamples*3) |
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#define samplefac 1 |
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/*----------------------------------------------------------------------*/ |
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/* */ |
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/* NeuQuant */ |
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/* -------- */ |
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/* */ |
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/* Copyright: Anthony Dekker, November 1994 */ |
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/* */ |
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/* This program performs colour quantization of graphics images (SUN */ |
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/* raster files). It uses a Kohonen Neural Network. It produces */ |
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/* better results than existing methods and runs faster, using minimal */ |
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/* space (8kB plus the image itself). The algorithm is described in */ |
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/* the paper "Kohonen Neural Networks for Optimal Colour Quantization" */ |
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/* to appear in the journal "Network: Computation in Neural Systems". */ |
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/* It is a significant improvement of an earlier algorithm. */ |
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/* */ |
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/* This program is distributed free for academic use or for evaluation */ |
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/* by commercial organizations. */ |
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/* */ |
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/* Usage: NeuQuant -n inputfile > outputfile */ |
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/* */ |
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/* where n is a sampling factor for neural learning. */ |
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/* */ |
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/* Program performance compared with other methods is as follows: */ |
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/* */ |
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/* Algorithm | Av. CPU Time | Quantization Error */ |
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/* ------------------------------------------------------------- */ |
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/* NeuQuant -3 | 314 | 5.55 */ |
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/* NeuQuant -10 | 119 | 5.97 */ |
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/* NeuQuant -30 | 65 | 6.53 */ |
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/* Oct-Trees | 141 | 8.96 */ |
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/* Median Cut (XV -best) | 420 | 9.28 */ |
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/* Median Cut (XV -slow) | 72 | 12.15 */ |
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/* */ |
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/* Author's address: Dept of ISCS, National University of Singapore */ |
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/* Kent Ridge, Singapore 0511 */ |
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/* Email: [email protected] */ |
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/*----------------------------------------------------------------------*/ |
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/* NeuQuant Neural-Net Quantization Algorithm Interface |
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* ---------------------------------------------------- |
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* |
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* Copyright (c) 1994 Anthony Dekker |
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* |
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* NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994. |
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* See "Kohonen neural networks for optimal colour quantization" |
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* in "Network: Computation in Neural Systems" Vol. 5 (1994) pp 351-367. |
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* for a discussion of the algorithm. |
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* See also http://members.ozemail.com.au/~dekker/NEUQUANT.HTML |
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* |
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* Any party obtaining a copy of these files from the author, directly or |
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* indirectly, is granted, free of charge, a full and unrestricted irrevocable, |
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* world-wide, paid up, royalty-free, nonexclusive right and license to deal |
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* in this software and documentation files (the "Software"), including without |
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* limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, |
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* and/or sell copies of the Software, and to permit persons who receive |
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* copies from any such party to do so, with the only requirement being |
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* that this copyright notice remain intact. |
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*/ |
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#define bool int |
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#define false 0 |
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/* initialise network in range (0,0,0) to (255,255,255) */ |
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initnet() |
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static void |
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initnet(void) |
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{ |
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register int i; |
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register int *p; |
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/* do after unbias - insertion sort of network and build netindex[0..255] */ |
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inxbuild() |
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static void |
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inxbuild(void) |
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{ |
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register int i,j,smallpos,smallval; |
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register int *p,*q; |
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} |
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int inxsearch(b,g,r) /* accepts real BGR values after net is unbiased */ |
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register int b,g,r; |
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static int |
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inxsearch( /* accepts real BGR values after net is unbiased */ |
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register int b, |
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register int g, |
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register int r |
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) |
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{ |
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register int i,j,dist,a,bestd; |
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register int *p; |
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/* for frequently chosen neurons, freq[i] is high and bias[i] is negative */ |
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/* bias[i] = gamma*((1/netsize)-freq[i]) */ |
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int contest(b,g,r) /* accepts biased BGR values */ |
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register int b,g,r; |
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static int |
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contest( /* accepts biased BGR values */ |
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register int b, |
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register int g, |
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register int r |
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) |
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{ |
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register int i,dist,a,biasdist,betafreq; |
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int bestpos,bestbiaspos,bestd,bestbiasd; |
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/* move neuron i towards (b,g,r) by factor alpha */ |
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altersingle(alpha,i,b,g,r) /* accepts biased BGR values */ |
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register int alpha,i,b,g,r; |
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static void |
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altersingle( /* accepts biased BGR values */ |
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register int alpha, |
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register int i, |
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register int b, |
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register int g, |
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register int r |
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) |
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{ |
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register int *n; |
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/* move neurons adjacent to i towards (b,g,r) by factor */ |
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/* alpha*(1-((i-j)^2/[r]^2)) precomputed as radpower[|i-j|]*/ |
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alterneigh(rad,i,b,g,r) /* accents biased BGR values */ |
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int rad,i; |
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register int b,g,r; |
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static void |
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alterneigh( /* accents biased BGR values */ |
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int rad, |
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int i, |
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register int b, |
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register int g, |
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register int r |
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) |
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{ |
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register int j,k,lo,hi,a; |
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register int *p, *q; |
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} |
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learn() |
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static void |
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learn(void) |
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{ |
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register int i,j,b,g,r; |
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int radius,rad,alpha,step,delta,samplepixels; |
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/* which can then be used for colour map */ |
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/* and record position i to prepare for sort */ |
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unbiasnet() |
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static void |
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unbiasnet(void) |
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{ |
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int i,j; |
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/* Don't do this until the network has been unbiased (GW) */ |
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static |
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cpyclrtab() |
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static void |
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cpyclrtab(void) |
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{ |
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register int i,j,k; |
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|
