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avfilter/vf_nnedi: small improvements

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Paul B Mahol 2021-01-20 13:05:52 +01:00
parent 2021dbe1d6
commit 1dc71cf64e
1 changed files with 13 additions and 8 deletions
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21
libavfilter/vf_nnedi.c
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@ -21,6 +21,7 @@
#include <float.h>
#include "libavutil/avassert.h"
#include "libavutil/common.h"
#include "libavutil/float_dsp.h"
#include "libavutil/imgutils.h"
@ -217,11 +218,13 @@ static int query_formats(AVFilterContext *ctx)
static float dot_dsp(const NNEDIContext *const s, const float *kernel, const float *input,
int n, float scale, float bias)
{
float sum;
float sum, y;
sum = s->fdsp->scalarproduct_float(kernel, input, n);
return sum * scale + bias;
y = sum * scale + bias + 1e-20f;
return y;
}
static float elliott(float x)
@ -334,6 +337,7 @@ static void gather_input(const float *src, ptrdiff_t src_stride,
float *buf, float mstd[4],
const PredictorCoefficients *const model)
{
const float scale = 1.f / model->nsize;
float sum = 0.f;
float sum_sq = 0.f;
float tmp;
@ -352,10 +356,10 @@ static void gather_input(const float *src, ptrdiff_t src_stride,
buf += model->xdim;
}
mstd[0] = sum / model->nsize;
mstd[0] = sum * scale;
mstd[3] = 0.f;
tmp = sum_sq / model->nsize - mstd[0] * mstd[0];
tmp = sum_sq * scale - mstd[0] * mstd[0];
if (tmp < FLT_EPSILON) {
mstd[1] = 0.0f;
mstd[2] = 0.0f;
@ -945,8 +949,9 @@ static void subtract_mean_new(PrescreenerCoefficients *coeffs, float half)
static void subtract_mean_predictor(PredictorCoefficients *model)
{
int filter_size = model->nsize;
int nns = model->nns;
const int filter_size = model->nsize;
const int nns = model->nns;
const float scale = 1.f / nns;
double softmax_means[256]; // Average of individual softmax filters.
double elliott_means[256]; // Average of individual elliott filters.
@ -963,7 +968,7 @@ static void subtract_mean_predictor(PredictorCoefficients *model)
}
for (int k = 0; k < filter_size; k++)
mean_filter[k] /= nns;
mean_filter[k] *= scale;
mean_bias = mean(model->softmax_bias_q1, nns);
@ -988,7 +993,7 @@ static void subtract_mean_predictor(PredictorCoefficients *model)
}
for (int k = 0; k < filter_size; k++)
mean_filter[k] /= nns;
mean_filter[k] *= scale;
mean_bias = mean(model->softmax_bias_q2, nns);