/******************************************************************** * * * THIS FILE IS PART OF THE OggVorbis SOFTWARE CODEC SOURCE CODE. * * USE, DISTRIBUTION AND REPRODUCTION OF THIS LIBRARY SOURCE IS * * GOVERNED BY A BSD-STYLE SOURCE LICENSE INCLUDED WITH THIS SOURCE * * IN 'COPYING'. PLEASE READ THESE TERMS BEFORE DISTRIBUTING. * * * * THE OggVorbis SOURCE CODE IS (C) COPYRIGHT 1994-2001 * * by the Xiph.Org Foundation http://www.xiph.org/ * * * ******************************************************************** function: train a VQ codebook last mod: $Id: vqgen.c 13293 2007-07-24 00:09:47Z xiphmont $ ********************************************************************/ /* This code is *not* part of libvorbis. It is used to generate trained codebooks offline and then spit the results into a pregenerated codebook that is compiled into libvorbis. It is an expensive (but good) algorithm. Run it on big iron. */ /* There are so many optimizations to explore in *both* stages that considering the undertaking is almost withering. For now, we brute force it all */ #include #include #include #include #include "vqgen.h" #include "bookutil.h" /* Codebook generation happens in two steps: 1) Train the codebook with data collected from the encoder: We use one of a few error metrics (which represent the distance between a given data point and a candidate point in the training set) to divide the training set up into cells representing roughly equal probability of occurring. 2) Generate the codebook and auxiliary data from the trained data set */ /* Codebook training **************************************************** * * The basic idea here is that a VQ codebook is like an m-dimensional * foam with n bubbles. The bubbles compete for space/volume and are * 'pressurized' [biased] according to some metric. The basic alg * iterates through allowing the bubbles to compete for space until * they converge (if the damping is dome properly) on a steady-state * solution. Individual input points, collected from libvorbis, are * used to train the algorithm monte-carlo style. */ /* internal helpers *****************************************************/ #define vN(data,i) (data+v->elements*i) /* default metric; squared 'distance' from desired value. */ float _dist(vqgen *v,float *a, float *b){ int i; int el=v->elements; float acc=0.f; for(i=0;ientries;i++) memcpy(_now(v,i),_point(v,i),sizeof(float)*v->elements); v->seeded=1; } int directdsort(const void *a, const void *b){ float av=*((float *)a); float bv=*((float *)b); return (avbv); } void vqgen_cellmetric(vqgen *v){ int j,k; float min=-1.f,max=-1.f,mean=0.f,acc=0.f; long dup=0,unused=0; #ifdef NOISY int i; char buff[80]; float spacings[v->entries]; int count=0; FILE *cells; sprintf(buff,"cellspace%d.m",v->it); cells=fopen(buff,"w"); #endif /* minimum, maximum, cell spacing */ for(j=0;jentries;j++){ float localmin=-1.; for(k=0;kentries;k++){ if(j!=k){ float this=_dist(v,_now(v,j),_now(v,k)); if(this>0){ if(v->assigned[k] && (localmin==-1 || thisentries)continue; if(v->assigned[j]==0){ unused++; continue; } localmin=v->max[j]+localmin/2; /* this gives us rough diameter */ if(min==-1 || localminmax)max=localmin; mean+=localmin; acc++; #ifdef NOISY spacings[count++]=localmin; #endif } fprintf(stderr,"cell diameter: %.03g::%.03g::%.03g (%ld unused/%ld dup)\n", min,mean/acc,max,unused,dup); #ifdef NOISY qsort(spacings,count,sizeof(float),directdsort); for(i=0;iquant)-1); int j,k; mindel=maxdel=_now(v,0)[0]; for(j=0;jentries;j++){ float last=0.f; for(k=0;kelements;k++){ if(mindel>_now(v,j)[k]-last)mindel=_now(v,j)[k]-last; if(maxdel<_now(v,j)[k]-last)maxdel=_now(v,j)[k]-last; if(q->sequencep)last=_now(v,j)[k]; } } /* first find the basic delta amount from the maximum span to be encoded. Loosen the delta slightly to allow for additional error during sequence quantization */ delta=(maxdel-mindel)/((1<quant)-1.5f); q->min=_float32_pack(mindel); q->delta=_float32_pack(delta); mindel=_float32_unpack(q->min); delta=_float32_unpack(q->delta); for(j=0;jentries;j++){ float last=0; for(k=0;kelements;k++){ float val=_now(v,j)[k]; float now=rint((val-last-mindel)/delta); _now(v,j)[k]=now; if(now<0){ /* be paranoid; this should be impossible */ fprintf(stderr,"fault; quantized value<0\n"); exit(1); } if(now>maxquant){ /* be paranoid; this should be impossible */ fprintf(stderr,"fault; quantized value>max\n"); exit(1); } if(q->sequencep)last=(now*delta)+mindel+last; } } } /* much easier :-). Unlike in the codebook, we don't un-log log scales; we just make sure they're properly offset. */ void vqgen_unquantize(vqgen *v,quant_meta *q){ long j,k; float mindel=_float32_unpack(q->min); float delta=_float32_unpack(q->delta); for(j=0;jentries;j++){ float last=0.f; for(k=0;kelements;k++){ float now=_now(v,j)[k]; now=fabs(now)*delta+last+mindel; if(q->sequencep)last=now; _now(v,j)[k]=now; } } } void vqgen_init(vqgen *v,int elements,int aux,int entries,float mindist, float (*metric)(vqgen *,float *, float *), float *(*weight)(vqgen *,float *),int centroid){ memset(v,0,sizeof(vqgen)); v->centroid=centroid; v->elements=elements; v->aux=aux; v->mindist=mindist; v->allocated=32768; v->pointlist=_ogg_malloc(v->allocated*(v->elements+v->aux)*sizeof(float)); v->entries=entries; v->entrylist=_ogg_malloc(v->entries*v->elements*sizeof(float)); v->assigned=_ogg_malloc(v->entries*sizeof(long)); v->bias=_ogg_calloc(v->entries,sizeof(float)); v->max=_ogg_calloc(v->entries,sizeof(float)); if(metric) v->metric_func=metric; else v->metric_func=_dist; if(weight) v->weight_func=weight; else v->weight_func=_weight_null; v->asciipoints=tmpfile(); } void vqgen_addpoint(vqgen *v, float *p,float *a){ int k; for(k=0;kelements;k++) fprintf(v->asciipoints,"%.12g\n",p[k]); for(k=0;kaux;k++) fprintf(v->asciipoints,"%.12g\n",a[k]); if(v->points>=v->allocated){ v->allocated*=2; v->pointlist=_ogg_realloc(v->pointlist,v->allocated*(v->elements+v->aux)* sizeof(float)); } memcpy(_point(v,v->points),p,sizeof(float)*v->elements); if(v->aux)memcpy(_point(v,v->points)+v->elements,a,sizeof(float)*v->aux); /* quantize to the density mesh if it's selected */ if(v->mindist>0.f){ /* quantize to the mesh */ for(k=0;kelements+v->aux;k++) _point(v,v->points)[k]= rint(_point(v,v->points)[k]/v->mindist)*v->mindist; } v->points++; if(!(v->points&0xff))spinnit("loading... ",v->points); } /* yes, not threadsafe. These utils aren't */ static int sortit=0; static int sortsize=0; static int meshcomp(const void *a,const void *b){ if(((sortit++)&0xfff)==0)spinnit("sorting mesh...",sortit); return(memcmp(a,b,sortsize)); } void vqgen_sortmesh(vqgen *v){ sortit=0; if(v->mindist>0.f){ long i,march=1; /* sort to make uniqueness detection trivial */ sortsize=(v->elements+v->aux)*sizeof(float); qsort(v->pointlist,v->points,sortsize,meshcomp); /* now march through and eliminate dupes */ for(i=1;ipoints;i++){ if(memcmp(_point(v,i),_point(v,i-1),sortsize)){ /* a new, unique entry. march it down */ if(i>march)memcpy(_point(v,march),_point(v,i),sortsize); march++; } spinnit("eliminating density... ",v->points-i); } /* we're done */ fprintf(stderr,"\r%ld training points remining out of %ld" " after density mesh (%ld%%)\n",march,v->points,march*100/v->points); v->points=march; } v->sorted=1; } float vqgen_iterate(vqgen *v,int biasp){ long i,j,k; float fdesired; long desired; long desired2; float asserror=0.f; float meterror=0.f; float *new; float *new2; long *nearcount; float *nearbias; #ifdef NOISY char buff[80]; FILE *assig; FILE *bias; FILE *cells; sprintf(buff,"cells%d.m",v->it); cells=fopen(buff,"w"); sprintf(buff,"assig%d.m",v->it); assig=fopen(buff,"w"); sprintf(buff,"bias%d.m",v->it); bias=fopen(buff,"w"); #endif if(v->entries<2){ fprintf(stderr,"generation requires at least two entries\n"); exit(1); } if(!v->sorted)vqgen_sortmesh(v); if(!v->seeded)_vqgen_seed(v); fdesired=(float)v->points/v->entries; desired=fdesired; desired2=desired*2; new=_ogg_malloc(sizeof(float)*v->entries*v->elements); 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;ipoints;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;jentries;j++){ float thismetric=v->metric_func(v,_now(v,j),ppt)+v->bias[j]; if(thismetricentries;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(kpoints+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;ientries;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;ipoints;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;jentries;j++){ float thismetric=v->metric_func(v,_now(v,j),ppt)+v->bias[j]; if(thismetricbias[j]; meterror+=firstmetric; if(v->centroid==0){ /* set up midpoints for next iter */ if(v->assigned[j]++){ for(k=0;kelements;k++) vN(new,j)[k]+=ppt[k]; if(firstmetric>v->max[j])v->max[j]=firstmetric; }else{ for(k=0;kelements;k++) vN(new,j)[k]=ppt[k]; v->max[j]=firstmetric; } }else{ /* centroid */ if(v->assigned[j]++){ for(k=0;kelements;k++){ if(vN(new,j)[k]>ppt[k])vN(new,j)[k]=ppt[k]; if(vN(new2,j)[k]v->max[firstentry])v->max[j]=firstmetric; }else{ for(k=0;kelements;k++){ vN(new,j)[k]=ppt[k]; vN(new2,j)[k]=ppt[k]; } v->max[firstentry]=firstmetric; } } } /* assign midpoints */ for(j=0;jentries;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;kelements;k++) _now(v,j)[k]=vN(new,j)[k]/v->assigned[j]; }else{ for(k=0;kelements;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); }