567 lines
16 KiB
C
567 lines
16 KiB
C
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/********************************************************************
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* *
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* THIS FILE IS PART OF THE OggVorbis SOFTWARE CODEC SOURCE CODE. *
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* USE, DISTRIBUTION AND REPRODUCTION OF THIS LIBRARY SOURCE IS *
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* GOVERNED BY A BSD-STYLE SOURCE LICENSE INCLUDED WITH THIS SOURCE *
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* IN 'COPYING'. PLEASE READ THESE TERMS BEFORE DISTRIBUTING. *
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* *
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* THE OggVorbis SOURCE CODE IS (C) COPYRIGHT 1994-2001 *
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* by the Xiph.Org Foundation http://www.xiph.org/ *
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* *
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********************************************************************
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function: train a VQ codebook
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********************************************************************/
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/* This code is *not* part of libvorbis. It is used to generate
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trained codebooks offline and then spit the results into a
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pregenerated codebook that is compiled into libvorbis. It is an
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expensive (but good) algorithm. Run it on big iron. */
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/* There are so many optimizations to explore in *both* stages that
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considering the undertaking is almost withering. For now, we brute
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force it all */
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#include <stdlib.h>
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#include <stdio.h>
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#include <math.h>
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#include <string.h>
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#include "vqgen.h"
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#include "bookutil.h"
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/* Codebook generation happens in two steps:
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1) Train the codebook with data collected from the encoder: We use
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one of a few error metrics (which represent the distance between a
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given data point and a candidate point in the training set) to
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divide the training set up into cells representing roughly equal
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probability of occurring.
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2) Generate the codebook and auxiliary data from the trained data set
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*/
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/* Codebook training ****************************************************
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*
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* The basic idea here is that a VQ codebook is like an m-dimensional
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* foam with n bubbles. The bubbles compete for space/volume and are
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* 'pressurized' [biased] according to some metric. The basic alg
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* iterates through allowing the bubbles to compete for space until
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* they converge (if the damping is dome properly) on a steady-state
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* solution. Individual input points, collected from libvorbis, are
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* used to train the algorithm monte-carlo style. */
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/* internal helpers *****************************************************/
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#define vN(data,i) (data+v->elements*i)
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/* default metric; squared 'distance' from desired value. */
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float _dist(vqgen *v,float *a, float *b){
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int i;
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int el=v->elements;
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float acc=0.f;
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for(i=0;i<el;i++){
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float val=(a[i]-b[i]);
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acc+=val*val;
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}
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return sqrt(acc);
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}
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float *_weight_null(vqgen *v,float *a){
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return a;
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}
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/* *must* be beefed up. */
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void _vqgen_seed(vqgen *v){
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long i;
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for(i=0;i<v->entries;i++)
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memcpy(_now(v,i),_point(v,i),sizeof(float)*v->elements);
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v->seeded=1;
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}
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int directdsort(const void *a, const void *b){
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float av=*((float *)a);
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float bv=*((float *)b);
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return (av<bv)-(av>bv);
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}
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void vqgen_cellmetric(vqgen *v){
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int j,k;
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float min=-1.f,max=-1.f,mean=0.f,acc=0.f;
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long dup=0,unused=0;
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#ifdef NOISY
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int i;
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char buff[80];
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float spacings[v->entries];
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int count=0;
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FILE *cells;
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sprintf(buff,"cellspace%d.m",v->it);
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cells=fopen(buff,"w");
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#endif
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/* minimum, maximum, cell spacing */
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for(j=0;j<v->entries;j++){
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float localmin=-1.;
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for(k=0;k<v->entries;k++){
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if(j!=k){
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float this=_dist(v,_now(v,j),_now(v,k));
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if(this>0){
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if(v->assigned[k] && (localmin==-1 || this<localmin))
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localmin=this;
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}else{
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if(k<j){
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dup++;
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break;
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}
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}
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}
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}
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if(k<v->entries)continue;
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if(v->assigned[j]==0){
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unused++;
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continue;
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}
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localmin=v->max[j]+localmin/2; /* this gives us rough diameter */
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if(min==-1 || localmin<min)min=localmin;
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if(max==-1 || localmin>max)max=localmin;
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mean+=localmin;
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acc++;
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#ifdef NOISY
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spacings[count++]=localmin;
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#endif
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}
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fprintf(stderr,"cell diameter: %.03g::%.03g::%.03g (%ld unused/%ld dup)\n",
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min,mean/acc,max,unused,dup);
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#ifdef NOISY
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qsort(spacings,count,sizeof(float),directdsort);
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for(i=0;i<count;i++)
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fprintf(cells,"%g\n",spacings[i]);
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fclose(cells);
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#endif
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}
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/* External calls *******************************************************/
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/* We have two forms of quantization; in the first, each vector
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element in the codebook entry is orthogonal. Residues would use this
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quantization for example.
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In the second, we have a sequence of monotonically increasing
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values that we wish to quantize as deltas (to save space). We
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still need to quantize so that absolute values are accurate. For
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example, LSP quantizes all absolute values, but the book encodes
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distance between values because each successive value is larger
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than the preceeding value. Thus the desired quantibits apply to
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the encoded (delta) values, not abs positions. This requires minor
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additional encode-side trickery. */
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void vqgen_quantize(vqgen *v,quant_meta *q){
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float maxdel;
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float mindel;
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float delta;
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float maxquant=((1<<q->quant)-1);
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int j,k;
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mindel=maxdel=_now(v,0)[0];
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for(j=0;j<v->entries;j++){
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float last=0.f;
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for(k=0;k<v->elements;k++){
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if(mindel>_now(v,j)[k]-last)mindel=_now(v,j)[k]-last;
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if(maxdel<_now(v,j)[k]-last)maxdel=_now(v,j)[k]-last;
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if(q->sequencep)last=_now(v,j)[k];
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}
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}
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/* first find the basic delta amount from the maximum span to be
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encoded. Loosen the delta slightly to allow for additional error
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during sequence quantization */
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delta=(maxdel-mindel)/((1<<q->quant)-1.5f);
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q->min=_float32_pack(mindel);
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q->delta=_float32_pack(delta);
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mindel=_float32_unpack(q->min);
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delta=_float32_unpack(q->delta);
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for(j=0;j<v->entries;j++){
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float last=0;
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for(k=0;k<v->elements;k++){
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float val=_now(v,j)[k];
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float now=rint((val-last-mindel)/delta);
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_now(v,j)[k]=now;
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if(now<0){
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/* be paranoid; this should be impossible */
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fprintf(stderr,"fault; quantized value<0\n");
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exit(1);
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}
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if(now>maxquant){
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/* be paranoid; this should be impossible */
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fprintf(stderr,"fault; quantized value>max\n");
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exit(1);
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}
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if(q->sequencep)last=(now*delta)+mindel+last;
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}
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}
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}
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/* much easier :-). Unlike in the codebook, we don't un-log log
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scales; we just make sure they're properly offset. */
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void vqgen_unquantize(vqgen *v,quant_meta *q){
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long j,k;
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float mindel=_float32_unpack(q->min);
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float delta=_float32_unpack(q->delta);
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for(j=0;j<v->entries;j++){
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float last=0.f;
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for(k=0;k<v->elements;k++){
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float now=_now(v,j)[k];
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now=fabs(now)*delta+last+mindel;
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if(q->sequencep)last=now;
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_now(v,j)[k]=now;
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}
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}
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}
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void vqgen_init(vqgen *v,int elements,int aux,int entries,float mindist,
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float (*metric)(vqgen *,float *, float *),
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float *(*weight)(vqgen *,float *),int centroid){
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memset(v,0,sizeof(vqgen));
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v->centroid=centroid;
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v->elements=elements;
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v->aux=aux;
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v->mindist=mindist;
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v->allocated=32768;
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v->pointlist=_ogg_malloc(v->allocated*(v->elements+v->aux)*sizeof(float));
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v->entries=entries;
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v->entrylist=_ogg_malloc(v->entries*v->elements*sizeof(float));
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v->assigned=_ogg_malloc(v->entries*sizeof(long));
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v->bias=_ogg_calloc(v->entries,sizeof(float));
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v->max=_ogg_calloc(v->entries,sizeof(float));
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if(metric)
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v->metric_func=metric;
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else
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v->metric_func=_dist;
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if(weight)
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v->weight_func=weight;
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else
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v->weight_func=_weight_null;
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v->asciipoints=tmpfile();
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}
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void vqgen_addpoint(vqgen *v, float *p,float *a){
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int k;
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for(k=0;k<v->elements;k++)
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fprintf(v->asciipoints,"%.12g\n",p[k]);
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for(k=0;k<v->aux;k++)
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fprintf(v->asciipoints,"%.12g\n",a[k]);
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if(v->points>=v->allocated){
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v->allocated*=2;
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v->pointlist=_ogg_realloc(v->pointlist,v->allocated*(v->elements+v->aux)*
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sizeof(float));
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}
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memcpy(_point(v,v->points),p,sizeof(float)*v->elements);
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if(v->aux)memcpy(_point(v,v->points)+v->elements,a,sizeof(float)*v->aux);
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/* quantize to the density mesh if it's selected */
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if(v->mindist>0.f){
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/* quantize to the mesh */
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for(k=0;k<v->elements+v->aux;k++)
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_point(v,v->points)[k]=
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rint(_point(v,v->points)[k]/v->mindist)*v->mindist;
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}
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v->points++;
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if(!(v->points&0xff))spinnit("loading... ",v->points);
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}
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/* yes, not threadsafe. These utils aren't */
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static int sortit=0;
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static int sortsize=0;
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static int meshcomp(const void *a,const void *b){
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if(((sortit++)&0xfff)==0)spinnit("sorting mesh...",sortit);
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return(memcmp(a,b,sortsize));
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}
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void vqgen_sortmesh(vqgen *v){
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sortit=0;
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if(v->mindist>0.f){
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long i,march=1;
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/* sort to make uniqueness detection trivial */
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sortsize=(v->elements+v->aux)*sizeof(float);
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qsort(v->pointlist,v->points,sortsize,meshcomp);
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/* now march through and eliminate dupes */
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for(i=1;i<v->points;i++){
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if(memcmp(_point(v,i),_point(v,i-1),sortsize)){
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/* a new, unique entry. march it down */
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if(i>march)memcpy(_point(v,march),_point(v,i),sortsize);
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march++;
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}
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spinnit("eliminating density... ",v->points-i);
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}
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/* we're done */
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fprintf(stderr,"\r%ld training points remining out of %ld"
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" after density mesh (%ld%%)\n",march,v->points,march*100/v->points);
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v->points=march;
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}
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v->sorted=1;
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}
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float vqgen_iterate(vqgen *v,int biasp){
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long i,j,k;
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float fdesired;
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long desired;
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long desired2;
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float asserror=0.f;
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float meterror=0.f;
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float *new;
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float *new2;
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long *nearcount;
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float *nearbias;
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#ifdef NOISY
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char buff[80];
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FILE *assig;
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FILE *bias;
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FILE *cells;
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sprintf(buff,"cells%d.m",v->it);
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cells=fopen(buff,"w");
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sprintf(buff,"assig%d.m",v->it);
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assig=fopen(buff,"w");
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sprintf(buff,"bias%d.m",v->it);
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bias=fopen(buff,"w");
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#endif
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if(v->entries<2){
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fprintf(stderr,"generation requires at least two entries\n");
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exit(1);
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}
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if(!v->sorted)vqgen_sortmesh(v);
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if(!v->seeded)_vqgen_seed(v);
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fdesired=(float)v->points/v->entries;
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desired=fdesired;
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desired2=desired*2;
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new=_ogg_malloc(sizeof(float)*v->entries*v->elements);
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new2=_ogg_malloc(sizeof(float)*v->entries*v->elements);
|
||
|
|
nearcount=_ogg_malloc(v->entries*sizeof(long));
|
||
|
|
nearbias=_ogg_malloc(v->entries*desired2*sizeof(float));
|
||
|
|
|
||
|
|
/* fill in nearest points for entry biasing */
|
||
|
|
/*memset(v->bias,0,sizeof(float)*v->entries);*/
|
||
|
|
memset(nearcount,0,sizeof(long)*v->entries);
|
||
|
|
memset(v->assigned,0,sizeof(long)*v->entries);
|
||
|
|
if(biasp){
|
||
|
|
for(i=0;i<v->points;i++){
|
||
|
|
float *ppt=v->weight_func(v,_point(v,i));
|
||
|
|
float firstmetric=v->metric_func(v,_now(v,0),ppt)+v->bias[0];
|
||
|
|
float secondmetric=v->metric_func(v,_now(v,1),ppt)+v->bias[1];
|
||
|
|
long firstentry=0;
|
||
|
|
long secondentry=1;
|
||
|
|
|
||
|
|
if(!(i&0xff))spinnit("biasing... ",v->points+v->points+v->entries-i);
|
||
|
|
|
||
|
|
if(firstmetric>secondmetric){
|
||
|
|
float temp=firstmetric;
|
||
|
|
firstmetric=secondmetric;
|
||
|
|
secondmetric=temp;
|
||
|
|
firstentry=1;
|
||
|
|
secondentry=0;
|
||
|
|
}
|
||
|
|
|
||
|
|
for(j=2;j<v->entries;j++){
|
||
|
|
float thismetric=v->metric_func(v,_now(v,j),ppt)+v->bias[j];
|
||
|
|
if(thismetric<secondmetric){
|
||
|
|
if(thismetric<firstmetric){
|
||
|
|
secondmetric=firstmetric;
|
||
|
|
secondentry=firstentry;
|
||
|
|
firstmetric=thismetric;
|
||
|
|
firstentry=j;
|
||
|
|
}else{
|
||
|
|
secondmetric=thismetric;
|
||
|
|
secondentry=j;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
j=firstentry;
|
||
|
|
for(j=0;j<v->entries;j++){
|
||
|
|
|
||
|
|
float thismetric,localmetric;
|
||
|
|
float *nearbiasptr=nearbias+desired2*j;
|
||
|
|
long k=nearcount[j];
|
||
|
|
|
||
|
|
localmetric=v->metric_func(v,_now(v,j),ppt);
|
||
|
|
/* 'thismetric' is to be the bias value necessary in the current
|
||
|
|
arrangement for entry j to capture point i */
|
||
|
|
if(firstentry==j){
|
||
|
|
/* use the secondary entry as the threshhold */
|
||
|
|
thismetric=secondmetric-localmetric;
|
||
|
|
}else{
|
||
|
|
/* use the primary entry as the threshhold */
|
||
|
|
thismetric=firstmetric-localmetric;
|
||
|
|
}
|
||
|
|
|
||
|
|
/* support the idea of 'minimum distance'... if we want the
|
||
|
|
cells in a codebook to be roughly some minimum size (as with
|
||
|
|
the low resolution residue books) */
|
||
|
|
|
||
|
|
/* a cute two-stage delayed sorting hack */
|
||
|
|
if(k<desired){
|
||
|
|
nearbiasptr[k]=thismetric;
|
||
|
|
k++;
|
||
|
|
if(k==desired){
|
||
|
|
spinnit("biasing... ",v->points+v->points+v->entries-i);
|
||
|
|
qsort(nearbiasptr,desired,sizeof(float),directdsort);
|
||
|
|
}
|
||
|
|
|
||
|
|
}else if(thismetric>nearbiasptr[desired-1]){
|
||
|
|
nearbiasptr[k]=thismetric;
|
||
|
|
k++;
|
||
|
|
if(k==desired2){
|
||
|
|
spinnit("biasing... ",v->points+v->points+v->entries-i);
|
||
|
|
qsort(nearbiasptr,desired2,sizeof(float),directdsort);
|
||
|
|
k=desired;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
nearcount[j]=k;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
/* inflate/deflate */
|
||
|
|
|
||
|
|
for(i=0;i<v->entries;i++){
|
||
|
|
float *nearbiasptr=nearbias+desired2*i;
|
||
|
|
|
||
|
|
spinnit("biasing... ",v->points+v->entries-i);
|
||
|
|
|
||
|
|
/* due to the delayed sorting, we likely need to finish it off....*/
|
||
|
|
if(nearcount[i]>desired)
|
||
|
|
qsort(nearbiasptr,nearcount[i],sizeof(float),directdsort);
|
||
|
|
|
||
|
|
v->bias[i]=nearbiasptr[desired-1];
|
||
|
|
|
||
|
|
}
|
||
|
|
}else{
|
||
|
|
memset(v->bias,0,v->entries*sizeof(float));
|
||
|
|
}
|
||
|
|
|
||
|
|
/* Now assign with new bias and find new midpoints */
|
||
|
|
for(i=0;i<v->points;i++){
|
||
|
|
float *ppt=v->weight_func(v,_point(v,i));
|
||
|
|
float firstmetric=v->metric_func(v,_now(v,0),ppt)+v->bias[0];
|
||
|
|
long firstentry=0;
|
||
|
|
|
||
|
|
if(!(i&0xff))spinnit("centering... ",v->points-i);
|
||
|
|
|
||
|
|
for(j=0;j<v->entries;j++){
|
||
|
|
float thismetric=v->metric_func(v,_now(v,j),ppt)+v->bias[j];
|
||
|
|
if(thismetric<firstmetric){
|
||
|
|
firstmetric=thismetric;
|
||
|
|
firstentry=j;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
j=firstentry;
|
||
|
|
|
||
|
|
#ifdef NOISY
|
||
|
|
fprintf(cells,"%g %g\n%g %g\n\n",
|
||
|
|
_now(v,j)[0],_now(v,j)[1],
|
||
|
|
ppt[0],ppt[1]);
|
||
|
|
#endif
|
||
|
|
|
||
|
|
firstmetric-=v->bias[j];
|
||
|
|
meterror+=firstmetric;
|
||
|
|
|
||
|
|
if(v->centroid==0){
|
||
|
|
/* set up midpoints for next iter */
|
||
|
|
if(v->assigned[j]++){
|
||
|
|
for(k=0;k<v->elements;k++)
|
||
|
|
vN(new,j)[k]+=ppt[k];
|
||
|
|
if(firstmetric>v->max[j])v->max[j]=firstmetric;
|
||
|
|
}else{
|
||
|
|
for(k=0;k<v->elements;k++)
|
||
|
|
vN(new,j)[k]=ppt[k];
|
||
|
|
v->max[j]=firstmetric;
|
||
|
|
}
|
||
|
|
}else{
|
||
|
|
/* centroid */
|
||
|
|
if(v->assigned[j]++){
|
||
|
|
for(k=0;k<v->elements;k++){
|
||
|
|
if(vN(new,j)[k]>ppt[k])vN(new,j)[k]=ppt[k];
|
||
|
|
if(vN(new2,j)[k]<ppt[k])vN(new2,j)[k]=ppt[k];
|
||
|
|
}
|
||
|
|
if(firstmetric>v->max[firstentry])v->max[j]=firstmetric;
|
||
|
|
}else{
|
||
|
|
for(k=0;k<v->elements;k++){
|
||
|
|
vN(new,j)[k]=ppt[k];
|
||
|
|
vN(new2,j)[k]=ppt[k];
|
||
|
|
}
|
||
|
|
v->max[firstentry]=firstmetric;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
/* assign midpoints */
|
||
|
|
|
||
|
|
for(j=0;j<v->entries;j++){
|
||
|
|
#ifdef NOISY
|
||
|
|
fprintf(assig,"%ld\n",v->assigned[j]);
|
||
|
|
fprintf(bias,"%g\n",v->bias[j]);
|
||
|
|
#endif
|
||
|
|
asserror+=fabs(v->assigned[j]-fdesired);
|
||
|
|
if(v->assigned[j]){
|
||
|
|
if(v->centroid==0){
|
||
|
|
for(k=0;k<v->elements;k++)
|
||
|
|
_now(v,j)[k]=vN(new,j)[k]/v->assigned[j];
|
||
|
|
}else{
|
||
|
|
for(k=0;k<v->elements;k++)
|
||
|
|
_now(v,j)[k]=(vN(new,j)[k]+vN(new2,j)[k])/2.f;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
asserror/=(v->entries*fdesired);
|
||
|
|
|
||
|
|
fprintf(stderr,"Pass #%d... ",v->it);
|
||
|
|
fprintf(stderr,": dist %g(%g) metric error=%g \n",
|
||
|
|
asserror,fdesired,meterror/v->points);
|
||
|
|
v->it++;
|
||
|
|
|
||
|
|
free(new);
|
||
|
|
free(nearcount);
|
||
|
|
free(nearbias);
|
||
|
|
#ifdef NOISY
|
||
|
|
fclose(assig);
|
||
|
|
fclose(bias);
|
||
|
|
fclose(cells);
|
||
|
|
#endif
|
||
|
|
return(asserror);
|
||
|
|
}
|
||
|
|
|