diff -urN pngnq-0.4/neuquant32.c pngnq-0.41/neuquant32.c
--- pngnq-0.4/neuquant32.c	2006-06-12 22:37:19.000000000 +1000
+++ pngnq-0.41/neuquant32.c	2006-06-17 09:25:04.000000000 +1000
@@ -381,7 +381,7 @@
 	register unsigned char *p;
 	unsigned char *lim;
 	
-	alphadec = 30 + ((samplefac-1)/3);
+	alphadec = 30 + ((samplefac-1)/4); /*Fixed sample factor bug 17/06/2006/ Stuart Coyle */
 	p = thepicture;
 	lim = thepicture + lengthcount;
 	samplepixels = lengthcount/(3*samplefac);
diff -urN pngnq-0.4/neuquant32.c~ pngnq-0.41/neuquant32.c~
--- pngnq-0.4/neuquant32.c~	1970-01-01 10:00:00.000000000 +1000
+++ pngnq-0.41/neuquant32.c~	2006-06-17 09:24:20.000000000 +1000
@@ -0,0 +1,434 @@
+/* NeuQuant Neural-Net Quantization Algorithm
+ * ------------------------------------------
+ *
+ * Copyright (c) 1994 Anthony Dekker
+ *
+ * NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994.
+ * See "Kohonen neural networks for optimal colour quantization"
+ * in "Network: Computation in Neural Systems" Vol. 5 (1994) pp 351-367.
+ * for a discussion of the algorithm.
+ * See also  http://members.ozemail.com.au/~dekker/NEUQUANT.HTML
+ *
+ * Any party obtaining a copy of these files from the author, directly or
+ * indirectly, is granted, free of charge, a full and unrestricted irrevocable,
+ * world-wide, paid up, royalty-free, nonexclusive right and license to deal
+ * in this software and documentation files (the "Software"), including without
+ * limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
+ * and/or sell copies of the Software, and to permit persons who receive
+ * copies from any such party to do so, with the only requirement being
+ * that this copyright notice remain intact.
+ * 
+ *
+ * Modified to process 32bit RGBA images.
+ * Stuart Coyle 2004-2006
+ */
+
+
+#include "neuquant32.h"
+
+
+/* Network Definitions
+   ------------------- */
+   
+#define maxnetpos	(MAXNETSIZE-1)
+#define netbiasshift	4			/* bias for colour values */
+#define ncycles		100			/* no. of learning cycles */
+
+/* defs for freq and bias */
+#define intbiasshift    16			/* bias for fractions */
+#define intbias		(((int) 1)<<intbiasshift)
+#define gammashift  	10			/* gamma = 1024 */
+#define gamma   	(((int) 1)<<gammashift)
+#define betashift  	10
+#define beta		(intbias>>betashift)	/* beta = 1/1024 */
+#define betagamma	(intbias<<(gammashift-betashift))
+
+/* defs for decreasing radius factor */
+#define initrad		(MAXNETSIZE>>3)		/* for 256 cols, radius starts */
+#define radiusbiasshift	6			/* at 32.0 biased by 6 bits */
+#define radiusbias	(((int) 1)<<radiusbiasshift)
+#define initradius	(initrad*radiusbias)	/* and decreases by a */
+#define radiusdec	30			/* factor of 1/30 each cycle */ 
+
+/* defs for decreasing alpha factor */
+#define alphabiasshift	10			/* alpha starts at 1.0 */
+#define initalpha	(((int) 1)<<alphabiasshift)
+int alphadec;					/* biased by 10 bits */
+
+/* radbias and alpharadbias used for radpower calculation */
+#define radbiasshift	8
+#define radbias		(((int) 1)<<radbiasshift)
+#define alpharadbshift  (alphabiasshift+radbiasshift)
+#define alpharadbias    (((int) 1)<<alpharadbshift)
+
+
+/* Types and Global Variables
+   -------------------------- */
+   
+static unsigned char *thepicture;		/* the input image itself */
+static int lengthcount;				/* lengthcount = H*W*3 */
+
+static int samplefac;				/* sampling factor 1..30 */
+
+
+typedef int nq_pixel[5];				/* ABGRc */
+static nq_pixel network[MAXNETSIZE];			/* the network itself */
+
+static int netindex[256];			/* for network lookup - really 256 */
+
+static int bias [MAXNETSIZE];			/* bias and freq arrays for learning */
+static int freq [MAXNETSIZE];
+static int radpower[initrad];			/* radpower for precomputation */
+
+static int netsize;                             /* Number of colours to use. Made a global instead of #define */
+
+/* Initialise network in range (0,0,0,0) to (255,255,255,255) and set parameters
+   ----------------------------------------------------------------------- */
+
+void initnet(thepic,len,sample,colours)	
+unsigned char *thepic;
+int len;
+int sample;
+{
+	register int i;
+	register int *p;
+	
+	thepicture = thepic;
+	lengthcount = len;
+	samplefac = sample;
+	netsize = colours; 
+	
+	for (i=0; i<netsize; i++) {
+		p = network[i];
+		p[0] = p[1] = p[2] = p[3] = (i << (netbiasshift+8))/netsize;
+		freq[i] = intbias/netsize;	/* 1/netsize */
+		bias[i] = 0;
+	}
+}
+
+	
+/* Unbias network to give byte values 0..255 and record position i to prepare for sort
+   ----------------------------------------------------------------------------------- */
+
+void unbiasnet()
+{
+	int i,j,temp;
+
+	for (i=0; i<netsize; i++) {
+		for (j=0; j<4; j++) {
+			/* OLD CODE: network[i][j] >>= netbiasshift; */
+			/* Fix based on bug report by Juergen Weigert jw@suse.de */
+			temp = (network[i][j] + (1 << (netbiasshift - 1))) >> netbiasshift;
+			if (temp > 255) temp = 255;
+			network[i][j] = temp;
+		}
+		network[i][4] = i;			/* record colour no */
+	}
+}
+
+
+/* Output colour map
+   ----------------- */
+
+void writecolourmap(f)
+FILE *f;
+{
+	int i,j;
+
+	for (i=3; i>=0; i--) 
+		for (j=0; j<netsize; j++) 
+			putc(network[j][i], f);
+}	          
+
+
+/* Output colormap to unsigned char ptr in RGBA format */
+void getcolormap(map)
+     unsigned char *map;
+{
+	int i,j;
+	for(j=0; j<netsize; j++){
+		for (i=3; i>=0; i--){
+			*map = network[j][i];
+			map++;
+		}
+	}
+}
+
+
+/* Insertion sort of network and building of netindex[0..255] (to do after unbias)
+   ------------------------------------------------------------------------------- */
+
+void inxbuild()
+{
+	register int i,j,smallpos,smallval;
+	register int *p,*q;
+	int previouscol,startpos;
+
+	previouscol = 0;
+	startpos = 0;
+	for (i=0; i<netsize; i++) {
+		p = network[i];
+		smallpos = i;
+		smallval = p[2];			/* index on g */
+		/* find smallest in i..netsize-1 */
+		for (j=i+1; j<netsize; j++) {
+			q = network[j];
+			if (q[2] < smallval) {		/* index on g */
+				smallpos = j;
+				smallval = q[2];	/* index on g */
+			}
+		}
+		q = network[smallpos];
+		/* swap p (i) and q (smallpos) entries */
+		if (i != smallpos) {
+			j = q[0];   q[0] = p[0];   p[0] = j;
+			j = q[1];   q[1] = p[1];   p[1] = j;
+			j = q[2];   q[2] = p[2];   p[2] = j;
+			j = q[3];   q[3] = p[3];   p[3] = j;
+			j = q[4];   q[4] = p[4];   p[4] = j;
+		}
+		/* smallval entry is now in position i */
+		if (smallval != previouscol) {
+			netindex[previouscol] = (startpos+i)>>1;
+			for (j=previouscol+1; j<smallval; j++) netindex[j] = i;
+			previouscol = smallval;
+			startpos = i;
+		}
+	}
+	netindex[previouscol] = (startpos+maxnetpos)>>1;
+	for (j=previouscol+1; j<256; j++) netindex[j] = maxnetpos; /* really 256 */
+}
+
+
+/* Search for ABGR values 0..255 (after net is unbiased) and return colour index
+   ---------------------------------------------------------------------------- */
+int inxsearch(al,b,g,r)
+     register int al,b,g,r;
+{
+	register int i,j,dist,a,bestd;
+	register int *p;
+	int best;
+
+	bestd = 1000;		/* biggest possible dist is 256*3 */
+	best = -1;
+	i = netindex[g];	/* index on g */
+	j = i-1;		/* start at netindex[g] and work outwards */
+
+	while ((i<netsize) || (j>=0)) {
+		if (i<netsize) {
+			p = network[i];
+			dist = p[2] - g;		/* inx key */
+			if (dist >= bestd) i = netsize;	/* stop iter */
+			else {
+				i++;
+				if (dist<0) dist = -dist;
+				a = p[1] - b;   if (a<0) a = -a;
+				dist += a;
+				if (dist<bestd) {
+					a = p[3] - r;   if (a<0) a = -a;
+					dist += a;
+				}
+				if(dist<bestd) {
+					a = p[0] - al; if (a<0) a = -a;
+					dist += a;
+				}
+				if (dist<bestd) {bestd=dist; best=p[4];}
+			}
+		}
+	
+		if (j>=0) {
+			p = network[j];
+			dist = g - p[2]; /* inx key - reverse dif */
+			if (dist >= bestd) j = -1; /* stop iter */
+			else {
+				j--;
+				if (dist<0) dist = -dist;
+				a = p[1] - b;   if (a<0) a = -a;
+				dist += a;
+				if (dist<bestd) {
+					a = p[3] - r;   if (a<0) a = -a;
+					dist += a;
+				}
+				if(dist<bestd) {
+					a = p[0] - al; if (a<0) a = -a;
+					dist += a;
+				}			
+				if (dist<bestd) {bestd=dist; best=p[4];}
+			}
+		}
+	}
+
+return(best);
+}
+
+
+
+
+/* Search for biased ABGR values
+   ---------------------------- */
+
+int contest(al,b,g,r)
+register int al,b,g,r;
+{
+	/* finds closest neuron (min dist) and updates freq */
+	/* finds best neuron (min dist-bias) and returns position */
+	/* for frequently chosen neurons, freq[i] is high and bias[i] is negative */
+	/* bias[i] = gamma*((1/netsize)-freq[i]) */
+
+	register int i,dist,a,biasdist,betafreq;
+	int bestpos,bestbiaspos,bestd,bestbiasd;
+	register int *p,*f, *n;
+
+	bestd = ~(((int) 1)<<31);
+	bestbiasd = bestd;
+	bestpos = -1;
+	bestbiaspos = bestpos;
+	p = bias;
+	f = freq;
+
+	for (i=0; i<netsize; i++) {
+		n = network[i];
+		dist = n[0] - al;   if (dist<0) dist = -dist;
+		a = n[1] - b;   if (a<0) a = -a;
+		dist += a;
+		a = n[2] - g;   if (a<0) a = -a;
+		dist += a;
+		a = n[3] - r;   if (a<0) a = -a;
+		dist += a;
+		if (dist<bestd) {bestd=dist; bestpos=i;}
+		biasdist = dist - ((*p)>>(intbiasshift-netbiasshift));
+		if (biasdist<bestbiasd) {bestbiasd=biasdist; bestbiaspos=i;}
+		betafreq = (*f >> betashift);
+		*f++ -= betafreq;
+		*p++ += (betafreq<<gammashift);
+	}
+	freq[bestpos] += beta;
+	bias[bestpos] -= betagamma;
+	return(bestbiaspos);
+}
+
+
+/* Move neuron i towards biased (a,b,g,r) by factor alpha
+   ---------------------------------------------------- */
+
+void altersingle(alpha,i,al,b,g,r)
+register int alpha,i,b,g,r;
+{
+	register int *n;
+
+	n = network[i];	/* alter hit neuron */
+	*n -= (alpha*(*n - al)) / initalpha;
+	n++;
+	*n -= (alpha*(*n - b)) / initalpha;
+	n++;
+	*n -= (alpha*(*n - g)) / initalpha;
+	n++;
+	*n -= (alpha*(*n - r)) / initalpha;
+}
+
+
+/* Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in radpower[|i-j|]
+   --------------------------------------------------------------------------------- */
+
+void alterneigh(rad,i,al,b,g,r)
+int rad,i;
+register int al,b,g,r;
+{
+	register int j,k,lo,hi,a;
+	register int *p, *q;
+
+	lo = i-rad;   if (lo<-1) lo=-1;
+	hi = i+rad;   if (hi>netsize) hi=netsize;
+
+	j = i+1;
+	k = i-1;
+	q = radpower;
+	while ((j<hi) || (k>lo)) {
+		a = (*(++q));
+		if (j<hi) {
+			p = network[j];
+			*p -= (a*(*p - al)) / alpharadbias;
+			p++;
+			*p -= (a*(*p - b)) / alpharadbias;
+			p++;
+			*p -= (a*(*p - g)) / alpharadbias;
+			p++;
+			*p -= (a*(*p - r)) / alpharadbias;
+			j++;
+		}
+		if (k>lo) {
+			p = network[k];
+			*p -= (a*(*p - al)) / alpharadbias;
+			p++;
+			*p -= (a*(*p - b)) / alpharadbias;
+			p++;
+			*p -= (a*(*p - g)) / alpharadbias;
+			p++;
+			*p -= (a*(*p - r)) / alpharadbias;
+			k--;
+		}
+	}
+}
+
+
+/* Main Learning Loop
+   ------------------ */
+
+void learn(int verbose) /* Stu: N.B. added parameter so that main() could control verbosity. */
+{
+	register int i,j,al,b,g,r;
+	int radius,rad,alpha,step,delta,samplepixels;
+	register unsigned char *p;
+	unsigned char *lim;
+	
+	alphadec = 30 + ((samplefac-1)/3);
+	p = thepicture;
+	lim = thepicture + lengthcount;
+	samplepixels = lengthcount/(3*samplefac);
+	delta = samplepixels/ncycles;  /* here's a problem with small images: samplepixels < ncycles => delta = 0 */
+	if(delta==0) delta = 1;        /* kludge to fix */
+	alpha = initalpha;
+	radius = initradius;
+	
+	rad = radius >> radiusbiasshift;
+	if (rad <= 1) rad = 0;
+	for (i=0; i<rad; i++) 
+		radpower[i] = alpha*(((rad*rad - i*i)*radbias)/(rad*rad));
+	
+	if(verbose) fprintf(stderr,"beginning 1D learning: initial radius=%d\n", rad);
+
+	if ((lengthcount%prime1) != 0) step = 4*prime1;
+	else {
+		if ((lengthcount%prime2) !=0) step = 4*prime2;
+		else {
+			if ((lengthcount%prime3) !=0) step = 4*prime3;
+			else step = 4*prime4;
+		}
+	}
+	
+	i = 0;
+	while (i < samplepixels) {
+	       al = p[3] << netbiasshift;
+		b = p[2] << netbiasshift;
+		g = p[1] << netbiasshift;
+		r = p[0] << netbiasshift;
+		j = contest(al,b,g,r);
+
+		altersingle(alpha,j,al,b,g,r);
+		if (rad) alterneigh(rad,j,al,b,g,r);   /* alter neighbours */
+
+		p += step;
+		if (p >= lim) p -= lengthcount;
+	
+		i++;
+		if (i%delta == 0) {                    /* FPE here if delta=0*/	
+			alpha -= alpha / alphadec;
+			radius -= radius / radiusdec;
+			rad = radius >> radiusbiasshift;
+			if (rad <= 1) rad = 0;
+			for (j=0; j<rad; j++) 
+				radpower[j] = alpha*(((rad*rad - j*j)*radbias)/(rad*rad));
+		}
+	}
+	if(verbose) fprintf(stderr,"finished 1D learning: final alpha=%f !\n",((float)alpha)/initalpha);
+}