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calculate all coefficients for several orders during cholesky factorization, the resulting coefficients are not strictly optimal though as there is a small difference in the autocorrelation matrixes which is ignored for the smaller orders

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Originally committed as revision 5758 to svn://svn.ffmpeg.org/ffmpeg/trunk
pull/126/head
Michael Niedermayer 19 years ago
parent 6ce704bbed
commit 408ec4e2a6
3 changed files with 55 additions and 40 deletions
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  1. 24
      libavcodec/flacenc.c
  2. 62
      libavutil/lls.c
  3. 7
      libavutil/lls.h

24
libavcodec/flacenc.c
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@ -742,35 +742,41 @@ static int lpc_calc_coefs(const int32_t *samples, int blocksize, int max_order,
compute_autocorr(samples, blocksize, max_order+1, autoc);
compute_lpc_coefs(autoc, max_order, lpc, ref);
opt_order = estimate_best_order(ref, max_order);
}else{
LLSModel m[2];
double var[MAX_LPC_ORDER+1], eval;
double var[MAX_LPC_ORDER+1], eval, weight;
for(pass=0; pass<use_lpc-1; pass++){
av_init_lls(&m[pass&1], max_order);
weight=0;
for(i=max_order; i<blocksize; i++){
for(j=0; j<=max_order; j++)
var[j]= samples[i-j];
if(pass){
eval= av_evaluate_lls(&m[(pass-1)&1], var+1);
eval= av_evaluate_lls(&m[(pass-1)&1], var+1, max_order-1);
eval= (512>>pass) + fabs(eval - var[0]);
for(j=0; j<=max_order; j++)
var[j]/= sqrt(eval);
}
weight += 1/eval;
}else
weight++;
av_update_lls(&m[pass&1], var, 1.0);
}
av_solve_lls(&m[pass&1], 0.001);
opt_order= max_order; //FIXME
av_solve_lls(&m[pass&1], 0.001, 0);
}
for(i=0; i<opt_order; i++)
lpc[opt_order-1][i]= m[(pass-1)&1].coeff[i];
for(i=0; i<max_order; i++){
for(j=0; j<max_order; j++)
lpc[i][j]= m[(pass-1)&1].coeff[i][j];
ref[i]= sqrt(m[(pass-1)&1].variance[i] / weight) * (blocksize - max_order) / 4000;
}
for(i=max_order-1; i>0; i--)
ref[i] = ref[i-1] - ref[i];
}
opt_order = estimate_best_order(ref, max_order);
i = opt_order-1;
quantize_lpc_coefs(lpc[i], i+1, precision, coefs[i], &shift[i]);

62
libavutil/lls.c
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@ -49,12 +49,11 @@ void av_update_lls(LLSModel *m, double *var, double decay){
}
}
double av_solve_lls(LLSModel *m, double threshold){
void av_solve_lls(LLSModel *m, double threshold, int min_order){
int i,j,k;
double (*factor)[MAX_VARS+1]= &m->covariance[1][0];
double (*covar )[MAX_VARS+1]= &m->covariance[1][1];
double *covar_y = m->covariance[0];
double variance;
int count= m->indep_count;
for(i=0; i<count; i++){
@ -75,33 +74,34 @@ double av_solve_lls(LLSModel *m, double threshold){
for(i=0; i<count; i++){
double sum= covar_y[i+1];
for(k=i-1; k>=0; k--)
sum -= factor[i][k]*m->coeff[k];
m->coeff[i]= sum / factor[i][i];
sum -= factor[i][k]*m->coeff[0][k];
m->coeff[0][i]= sum / factor[i][i];
}
for(i=count-1; i>=0; i--){
double sum= m->coeff[i];
for(k=i+1; k<count; k++)
sum -= factor[k][i]*m->coeff[k];
m->coeff[i]= sum / factor[i][i];
for(j=count-1; j>=min_order; j--){
for(i=j; i>=0; i--){
double sum= m->coeff[0][i];
for(k=i+1; k<=j; k++)
sum -= factor[k][i]*m->coeff[j][k];
m->coeff[j][i]= sum / factor[i][i];
}
variance= covar_y[0];
for(i=0; i<count; i++){
double sum= m->coeff[i]*covar[i][i] - 2*covar_y[i+1];
for(j=0; j<i; j++)
sum += 2*m->coeff[j]*covar[j][i];
variance += m->coeff[i]*sum;
m->variance[j]= covar_y[0];
for(i=0; i<=j; i++){
double sum= m->coeff[j][i]*covar[i][i] - 2*covar_y[i+1];
for(k=0; k<i; k++)
sum += 2*m->coeff[j][k]*covar[k][i];
m->variance[j] += m->coeff[j][i]*sum;
}
}
return variance;
}
double av_evaluate_lls(LLSModel *m, double *param){
double av_evaluate_lls(LLSModel *m, double *param, int order){
int i;
double out= 0;
for(i=0; i<m->indep_count; i++)
out+= param[i]*m->coeff[i];
for(i=0; i<=order; i++)
out+= param[i]*m->coeff[order][i];
return out;
}
@ -113,27 +113,35 @@ double av_evaluate_lls(LLSModel *m, double *param){
int main(){
LLSModel m;
int i;
int i, order;
av_init_lls(&m, 3);
for(i=0; i<100; i++){
double var[4];
double eval, variance;
#if 0
var[1] = rand() / (double)RAND_MAX;
var[2] = rand() / (double)RAND_MAX;
var[3] = rand() / (double)RAND_MAX;
var[2]= var[1] + var[3];
var[2]= var[1] + var[3]/2;
var[0] = var[1] + var[2] + var[3] + var[1]*var[2]/100;
eval= av_evaluate_lls(&m, var+1);
#else
var[0] = (rand() / (double)RAND_MAX - 0.5)*2;
var[1] = var[0] + rand() / (double)RAND_MAX - 0.5;
var[2] = var[1] + rand() / (double)RAND_MAX - 0.5;
var[3] = var[2] + rand() / (double)RAND_MAX - 0.5;
#endif
av_update_lls(&m, var, 0.99);
variance= av_solve_lls(&m, 0.001);
av_log(NULL, AV_LOG_DEBUG, "real:%f pred:%f var:%f coeffs:%f %f %f\n",
var[0], eval, sqrt(variance / (i+1)),
m.coeff[0], m.coeff[1], m.coeff[2]);
av_solve_lls(&m, 0.001, 0);
for(order=0; order<3; order++){
eval= av_evaluate_lls(&m, var+1, order);
av_log(NULL, AV_LOG_DEBUG, "real:%f order:%d pred:%f var:%f coeffs:%f %f %f\n",
var[0], order, eval, sqrt(m.variance[order] / (i+1)),
m.coeff[order][0], m.coeff[order][1], m.coeff[order][2]);
}
}
return 0;
}

7
libavutil/lls.h
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@ -30,13 +30,14 @@
*/
typedef struct LLSModel{
double covariance[MAX_VARS+1][MAX_VARS+1];
double coeff[MAX_VARS];
double coeff[MAX_VARS][MAX_VARS];
double variance[MAX_VARS];
int indep_count;
}LLSModel;
void av_init_lls(LLSModel *m, int indep_count);
void av_update_lls(LLSModel *m, double *param, double decay);
double av_solve_lls(LLSModel *m, double threshold);
double av_evaluate_lls(LLSModel *m, double *param);
void av_solve_lls(LLSModel *m, double threshold, int min_order);
double av_evaluate_lls(LLSModel *m, double *param, int order);
#endif

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