Chiebot-Mirror
/
FFmpeg
mirror of https://github.com/FFmpeg/FFmpeg.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

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

Browse Source 
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
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 24
      libavcodec/flacenc.c
  2. 64
      libavutil/lls.c
  3. 7
      libavutil/lls.h

24
libavcodec/flacenc.c
Unescape View File

@ -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]);

64
libavutil/lls.c
Unescape View File

@ -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
Unescape View File

@ -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

Write Preview
Loading…
Cancel
Save