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greg |
2.1 |
/* Copyright (c) 1994 Regents of the University of California */
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#ifndef lint
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static char SCCSid[] = "$SunId$ LBL";
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#endif
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/*
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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 "color.h"
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#include "random.h"
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#ifdef COMPAT_MODE
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#define neu_init new_histo
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#define neu_pixel cnt_pixel
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#define neu_colrs cnt_colrs
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#define neu_clrtab new_clrtab
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#define neu_map_pixel map_pixel
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#define neu_map_colrs map_colrs
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#define neu_dith_colrs dith_colrs
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#endif
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/* our color table (global) */
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extern BYTE clrtab[256][3];
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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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#endif
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#endif
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int samplefac = DEFSMPFAC; /* sampling factor */
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/* Samples array starts off holding spacing between adjacent
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* samples, and ends up holding actual BGR sample values.
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*/
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static BYTE *thesamples;
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static int nsamples;
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static BYTE *cursamp;
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static long skipcount;
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#define MAXSKIP (1<<24-1)
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#define nskip(sp) ((long)(sp)[0]<<16|(long)(sp)[1]<<8|(long)(sp)[2])
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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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neu_init(npixels) /* initialize our sample array */
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long npixels;
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{
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register int nsleft;
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register long sv;
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double rval, cumprob;
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long npleft;
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nsamples = npixels/samplefac;
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if (nsamples < 600)
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return(-1);
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greg |
2.2 |
thesamples = (BYTE *)malloc(nsamples*3);
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greg |
2.1 |
if (thesamples == NULL)
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return(-1);
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cursamp = thesamples;
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npleft = npixels;
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nsleft = nsamples;
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while (nsleft) {
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rval = frandom(); /* random distance to next sample */
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sv = 0;
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cumprob = 0.;
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while ((cumprob += (1.-cumprob)*nsleft/(npleft-sv)) < rval)
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sv++;
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greg |
2.2 |
if (nsleft == nsamples)
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skipcount = sv;
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else {
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setskip(cursamp, sv);
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cursamp += 3;
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}
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npleft -= sv+1;
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greg |
2.1 |
nsleft--;
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}
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greg |
2.2 |
setskip(cursamp, npleft); /* tag on end to skip the rest */
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greg |
2.1 |
cursamp = thesamples;
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return(0);
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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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{
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if (!skipcount--) {
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greg |
2.2 |
skipcount = nskip(cursamp);
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greg |
2.1 |
cursamp[0] = col[BLU];
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cursamp[1] = col[GRN];
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cursamp[2] = col[RED];
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cursamp += 3;
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}
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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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{
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while (n > skipcount) {
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cs += skipcount;
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greg |
2.2 |
n -= skipcount+1;
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skipcount = nskip(cursamp);
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greg |
2.1 |
cursamp[0] = cs[0][BLU];
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cursamp[1] = cs[0][GRN];
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cursamp[2] = cs[0][RED];
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cs++;
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cursamp += 3;
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}
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skipcount -= n;
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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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{
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clrtabsiz = ncolors;
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if (clrtabsiz > 256) clrtabsiz = 256;
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initnet();
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learn();
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unbiasnet();
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cpyclrtab();
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inxbuild();
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/* we're done with our samples */
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free((char *)thesamples);
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/* reset dithering function */
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neu_dith_colrs((BYTE *)NULL, (COLR *)NULL, 0);
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/* return new color table size */
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return(clrtabsiz);
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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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{
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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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{
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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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cs++;
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}
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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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{
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static short (*cerr)[3] = NULL;
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static int N = 0;
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int err[3], errp[3];
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register int x, i;
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if (n != N) { /* get error propogation array */
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if (N) {
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free((char *)cerr);
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cerr = NULL;
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}
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if (n)
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cerr = (short (*)[3])malloc(3*n*sizeof(short));
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if (cerr == NULL) {
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N = 0;
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map_colrs(bs, cs, n);
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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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}
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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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for (i = 0; i < 3; i++) { /* dither value */
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errp[i] = err[i];
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err[i] += cerr[x][i];
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#ifdef MAXERR
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if (err[i] > MAXERR) err[i] = MAXERR;
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else if (err[i] < -MAXERR) err[i] = -MAXERR;
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#endif
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err[i] += cs[x][i];
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if (err[i] < 0) err[i] = 0;
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else if (err[i] > 255) err[i] = 255;
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}
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bs[x] = inxsearch(err[BLU],err[GRN],err[RED]);
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for (i = 0; i < 3; i++) { /* propagate error */
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err[i] -= clrtab[bs[x]][i];
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err[i] /= 3;
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cerr[x][i] = err[i] + errp[i];
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}
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}
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}
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/* The following was adapted and modified from the original (GW) */
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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, June 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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#define bool int
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#define false 0
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#define true 1
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#define initrad 32
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#define radiusdec 30
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#define alphadec; 30
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/* defs for freq and bias */
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#define gammashift 10
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#define betashift gammashift
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#define intbiasshift 16
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#define intbias (1<<intbiasshift)
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#define gamma (1<<gammashift)
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#define beta (intbias>>betashift)
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#define betagamma (intbias<<(gammashift-betashift))
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#define gammaphi (intbias<<(gammashift-8))
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/* defs for rad and alpha */
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#define maxrad (initrad+1)
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#define radiusbiasshift 6
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#define radiusbias (1<<radiusbiasshift)
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#define initradius ((int) (initrad*radiusbias))
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#define alphabiasshift 10
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#define initalpha (1<<alphabiasshift)
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#define radbiasshift 8
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#define radbias (1<<radbiasshift)
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#define alpharadbshift (alphabiasshift+radbiasshift)
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#define alpharadbias (1<<alpharadbshift)
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/* other defs */
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#define netbiasshift 4
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#define funnyshift (intbiasshift-netbiasshift)
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#define maxnetval ((256<<netbiasshift)-1)
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#define ncycles 100
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#define jump1 499 /* prime */
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#define jump2 491 /* prime */
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#define jump3 487 /* any pic whose size was divisible by all */
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#define jump4 503 /* four primes would be simply enormous */
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/* cheater definitions (GW) */
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#define thepicture thesamples
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#define lengthcount (nsamples*3)
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#define samplefac 1
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typedef int pixel[4]; /* BGRc */
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static pixel network[256];
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static int netindex[256];
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static int bias [256];
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static int freq [256];
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static int radpower[256]; /* actually need only go up to maxrad */
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/* fixed space overhead 256*4+256+256+256+256 words = 256*8 = 8kB */
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static
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initnet()
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{
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register int i;
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register int *p;
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for (i=0; i<clrtabsiz; i++) {
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p = network[i];
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greg |
2.3 |
p[0] =
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p[1] =
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p[2] = (i<<8) / clrtabsiz;
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freq[i] = intbias/clrtabsiz; /* 1/256 */
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greg |
2.1 |
bias[i] = 0;
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}
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}
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static
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inxbuild()
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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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int start,previous;
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previous = 0;
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start = 0;
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for (i=0; i<clrtabsiz; i++) {
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p = network[i];
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smallpos = i;
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smallval = p[1]; /* index on g */
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/* find smallest in i+1..clrtabsiz-1 */
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for (j=i+1; j<clrtabsiz; j++) {
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q = network[j];
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if (q[1] < smallval) { /* index on g */
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smallpos = j;
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smallval = q[1]; /* index on g */
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}
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}
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q = network[smallpos];
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if (i != smallpos) {
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j = q[0]; q[0] = p[0]; p[0] = j;
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j = q[1]; q[1] = p[1]; p[1] = j;
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j = q[2]; q[2] = p[2]; p[2] = j;
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j = q[3]; q[3] = p[3]; p[3] = j;
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}
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|
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/* smallval entry is now in position i */
|
| 353 |
|
|
if (smallval != previous) {
|
| 354 |
|
|
netindex[previous] = (start+i)>>1;
|
| 355 |
|
|
for (j=previous+1; j<smallval; j++) netindex[j] = i;
|
| 356 |
|
|
previous = smallval;
|
| 357 |
|
|
start = i;
|
| 358 |
|
|
}
|
| 359 |
|
|
}
|
| 360 |
|
|
netindex[previous] = (start+clrtabsiz-1)>>1;
|
| 361 |
|
|
for (j=previous+1; j<clrtabsiz; j++) netindex[j] = clrtabsiz-1;
|
| 362 |
|
|
}
|
| 363 |
|
|
|
| 364 |
|
|
|
| 365 |
|
|
static int
|
| 366 |
|
|
inxsearch(b,g,r) /* accepts real BGR values after net is unbiased */
|
| 367 |
|
|
register int b,g,r;
|
| 368 |
|
|
{
|
| 369 |
|
|
register int i,j,best,x,y,bestd;
|
| 370 |
|
|
register int *p;
|
| 371 |
|
|
|
| 372 |
|
|
bestd = 1000; /* biggest possible dist is 256*3 */
|
| 373 |
|
|
best = -1;
|
| 374 |
|
|
i = netindex[g]; /* index on g */
|
| 375 |
|
|
j = i-1;
|
| 376 |
|
|
|
| 377 |
|
|
while ((i<clrtabsiz) || (j>=0)) {
|
| 378 |
|
|
if (i<clrtabsiz) {
|
| 379 |
|
|
p = network[i];
|
| 380 |
|
|
x = p[1] - g; /* inx key */
|
| 381 |
|
|
if (x >= bestd) i = clrtabsiz; /* stop iter */
|
| 382 |
|
|
else {
|
| 383 |
|
|
i++;
|
| 384 |
|
|
if (x<0) x = -x;
|
| 385 |
|
|
y = p[0] - b;
|
| 386 |
|
|
if (y<0) y = -y;
|
| 387 |
|
|
x += y;
|
| 388 |
|
|
if (x<bestd) {
|
| 389 |
|
|
y = p[2] - r;
|
| 390 |
|
|
if (y<0) y = -y;
|
| 391 |
|
|
x += y; /* x holds distance */
|
| 392 |
|
|
if (x<bestd) {bestd=x; best=p[3];}
|
| 393 |
|
|
}
|
| 394 |
|
|
}
|
| 395 |
|
|
}
|
| 396 |
|
|
if (j>=0) {
|
| 397 |
|
|
p = network[j];
|
| 398 |
|
|
x = g - p[1]; /* inx key - reverse dif */
|
| 399 |
|
|
if (x >= bestd) j = -1; /* stop iter */
|
| 400 |
|
|
else {
|
| 401 |
|
|
j--;
|
| 402 |
|
|
if (x<0) x = -x;
|
| 403 |
|
|
y = p[0] - b;
|
| 404 |
|
|
if (y<0) y = -y;
|
| 405 |
|
|
x += y;
|
| 406 |
|
|
if (x<bestd) {
|
| 407 |
|
|
y = p[2] - r;
|
| 408 |
|
|
if (y<0) y = -y;
|
| 409 |
|
|
x += y; /* x holds distance */
|
| 410 |
|
|
if (x<bestd) {bestd=x; best=p[3];}
|
| 411 |
|
|
}
|
| 412 |
|
|
}
|
| 413 |
|
|
}
|
| 414 |
|
|
}
|
| 415 |
|
|
return(best);
|
| 416 |
|
|
}
|
| 417 |
|
|
|
| 418 |
|
|
|
| 419 |
|
|
static int
|
| 420 |
|
|
contest(b,g,r) /* accepts biased BGR values */
|
| 421 |
|
|
register int b,g,r;
|
| 422 |
|
|
{
|
| 423 |
|
|
register int i,best,bestbias,x,y,bestd,bestbiasd;
|
| 424 |
|
|
register int *p,*q, *pp;
|
| 425 |
|
|
|
| 426 |
|
|
bestd = ~(1<<31);
|
| 427 |
|
|
bestbiasd = bestd;
|
| 428 |
|
|
best = -1;
|
| 429 |
|
|
bestbias = best;
|
| 430 |
|
|
q = bias;
|
| 431 |
|
|
p = freq;
|
| 432 |
|
|
for (i=0; i<clrtabsiz; i++) {
|
| 433 |
|
|
pp = network[i];
|
| 434 |
|
|
x = pp[0] - b;
|
| 435 |
|
|
if (x<0) x = -x;
|
| 436 |
|
|
y = pp[1] - g;
|
| 437 |
|
|
if (y<0) y = -y;
|
| 438 |
|
|
x += y;
|
| 439 |
|
|
y = pp[2] - r;
|
| 440 |
|
|
if (y<0) y = -y;
|
| 441 |
|
|
x += y; /* x holds distance */
|
| 442 |
|
|
/* >> netbiasshift not needed if funnyshift used */
|
| 443 |
|
|
if (x<bestd) {bestd=x; best=i;}
|
| 444 |
|
|
y = x - ((*q)>>funnyshift); /* y holds biasd */
|
| 445 |
|
|
if (y<bestbiasd) {bestbiasd=y; bestbias=i;}
|
| 446 |
|
|
y = (*p >> betashift); /* y holds beta*freq */
|
| 447 |
|
|
*p -= y;
|
| 448 |
|
|
*q += (y<<gammashift);
|
| 449 |
|
|
p++;
|
| 450 |
|
|
q++;
|
| 451 |
|
|
}
|
| 452 |
|
|
freq[best] += beta;
|
| 453 |
|
|
bias[best] -= betagamma;
|
| 454 |
|
|
return(bestbias);
|
| 455 |
|
|
}
|
| 456 |
|
|
|
| 457 |
|
|
|
| 458 |
|
|
static
|
| 459 |
|
|
alterneigh(rad,i,b,g,r) /* accepts biased BGR values */
|
| 460 |
|
|
int rad,i;
|
| 461 |
|
|
register int b,g,r;
|
| 462 |
|
|
{
|
| 463 |
|
|
register int j,k,lo,hi,a;
|
| 464 |
|
|
register int *p, *q;
|
| 465 |
|
|
|
| 466 |
|
|
lo = i-rad;
|
| 467 |
|
|
if (lo<-1) lo= -1;
|
| 468 |
|
|
hi = i+rad;
|
| 469 |
|
|
if (hi>clrtabsiz) hi=clrtabsiz;
|
| 470 |
|
|
|
| 471 |
|
|
j = i+1;
|
| 472 |
|
|
k = i-1;
|
| 473 |
|
|
q = radpower;
|
| 474 |
|
|
while ((j<hi) || (k>lo)) {
|
| 475 |
|
|
a = (*(++q));
|
| 476 |
|
|
if (j<hi) {
|
| 477 |
|
|
p = network[j];
|
| 478 |
|
|
*p -= (a*(*p - b)) / alpharadbias;
|
| 479 |
|
|
p++;
|
| 480 |
|
|
*p -= (a*(*p - g)) / alpharadbias;
|
| 481 |
|
|
p++;
|
| 482 |
|
|
*p -= (a*(*p - r)) / alpharadbias;
|
| 483 |
|
|
j++;
|
| 484 |
|
|
}
|
| 485 |
|
|
if (k>lo) {
|
| 486 |
|
|
p = network[k];
|
| 487 |
|
|
*p -= (a*(*p - b)) / alpharadbias;
|
| 488 |
|
|
p++;
|
| 489 |
|
|
*p -= (a*(*p - g)) / alpharadbias;
|
| 490 |
|
|
p++;
|
| 491 |
|
|
*p -= (a*(*p - r)) / alpharadbias;
|
| 492 |
|
|
k--;
|
| 493 |
|
|
}
|
| 494 |
|
|
}
|
| 495 |
|
|
}
|
| 496 |
|
|
|
| 497 |
|
|
|
| 498 |
|
|
static
|
| 499 |
|
|
altersingle(alpha,j,b,g,r) /* accepts biased BGR values */
|
| 500 |
|
|
register int alpha,j,b,g,r;
|
| 501 |
|
|
{
|
| 502 |
|
|
register int *q;
|
| 503 |
|
|
|
| 504 |
|
|
q = network[j]; /* alter hit neuron */
|
| 505 |
|
|
*q -= (alpha*(*q - b)) / initalpha;
|
| 506 |
|
|
q++;
|
| 507 |
|
|
*q -= (alpha*(*q - g)) / initalpha;
|
| 508 |
|
|
q++;
|
| 509 |
|
|
*q -= (alpha*(*q - r)) / initalpha;
|
| 510 |
|
|
}
|
| 511 |
|
|
|
| 512 |
|
|
|
| 513 |
|
|
static
|
| 514 |
|
|
learn()
|
| 515 |
|
|
{
|
| 516 |
|
|
register int i,j,b,g,r;
|
| 517 |
|
|
int radius,rad,alpha,step,delta,upto;
|
| 518 |
|
|
register unsigned char *p;
|
| 519 |
|
|
unsigned char *lim;
|
| 520 |
|
|
|
| 521 |
|
|
upto = lengthcount/(3*samplefac);
|
| 522 |
|
|
delta = upto/ncycles;
|
| 523 |
|
|
lim = thepicture + lengthcount;
|
| 524 |
|
|
p = thepicture;
|
| 525 |
|
|
alpha = initalpha;
|
| 526 |
|
|
radius = initradius;
|
| 527 |
|
|
rad = radius >> radiusbiasshift;
|
| 528 |
|
|
if (rad <= 1) rad = 0;
|
| 529 |
|
|
for (i=0; i<rad; i++)
|
| 530 |
|
|
radpower[i] = alpha*(((rad*rad - i*i)*radbias)/(rad*rad));
|
| 531 |
|
|
|
| 532 |
|
|
if ((lengthcount%jump1) != 0) step = 3*jump1;
|
| 533 |
|
|
else {
|
| 534 |
|
|
if ((lengthcount%jump2) !=0) step = 3*jump2;
|
| 535 |
|
|
else {
|
| 536 |
|
|
if ((lengthcount%jump3) !=0) step = 3*jump3;
|
| 537 |
|
|
else step = 3*jump4;
|
| 538 |
|
|
}
|
| 539 |
|
|
}
|
| 540 |
|
|
i = 0;
|
| 541 |
|
|
while (i < upto) {
|
| 542 |
|
|
b = p[0] << netbiasshift;
|
| 543 |
|
|
g = p[1] << netbiasshift;
|
| 544 |
|
|
r = p[2] << netbiasshift;
|
| 545 |
|
|
j = contest(b,g,r);
|
| 546 |
|
|
|
| 547 |
|
|
altersingle(alpha,j,b,g,r);
|
| 548 |
|
|
if (rad) alterneigh(rad,j,b,g,r);
|
| 549 |
|
|
/* alter neighbours */
|
| 550 |
|
|
|
| 551 |
|
|
p += step;
|
| 552 |
|
|
if (p >= lim) p -= lengthcount;
|
| 553 |
|
|
|
| 554 |
|
|
i++;
|
| 555 |
|
|
if (i%delta == 0) {
|
| 556 |
|
|
alpha -= alpha / alphadec;
|
| 557 |
|
|
radius -= radius / radiusdec;
|
| 558 |
|
|
rad = radius >> radiusbiasshift;
|
| 559 |
|
|
if (rad <= 1) rad = 0;
|
| 560 |
|
|
for (j=0; j<rad; j++)
|
| 561 |
|
|
radpower[j] = alpha*(((rad*rad - j*j)*radbias)/(rad*rad));
|
| 562 |
|
|
}
|
| 563 |
|
|
}
|
| 564 |
|
|
}
|
| 565 |
|
|
|
| 566 |
|
|
static
|
| 567 |
|
|
unbiasnet()
|
| 568 |
|
|
{
|
| 569 |
|
|
int i,j;
|
| 570 |
|
|
|
| 571 |
|
|
for (i=0; i<clrtabsiz; i++) {
|
| 572 |
|
|
for (j=0; j<3; j++)
|
| 573 |
|
|
network[i][j] >>= netbiasshift;
|
| 574 |
|
|
network[i][3] = i; /* record colour no */
|
| 575 |
|
|
}
|
| 576 |
|
|
}
|
| 577 |
|
|
|
| 578 |
|
|
/* Don't do this until the network has been unbiased */
|
| 579 |
|
|
|
| 580 |
|
|
static
|
| 581 |
|
|
cpyclrtab()
|
| 582 |
|
|
{
|
| 583 |
|
|
register int i,j,k;
|
| 584 |
|
|
|
| 585 |
|
|
for (j=0; j<clrtabsiz; j++) {
|
| 586 |
|
|
k = network[j][3];
|
| 587 |
|
|
for (i = 0; i < 3; i++)
|
| 588 |
|
|
clrtab[k][i] = network[j][2-i];
|
| 589 |
|
|
}
|
| 590 |
|
|
}
|
