ref: f698ea61a72695f9433ad4e4eb0b31ae8f65700a
parent: 39ea3d72f5751ff09f7ac0e7b9dfe61b247ddeae
author: Michael Horowitz <mhoro@google.com>
date: Wed Apr 3 09:41:37 EDT 2019
Histogram-based noise estimation algorithm Histogram-based noise estimation algorithm leveraged that low-noise sequences tend to populate lower-valued histogram bins and high-noise sequences tend to populate higher-valued histogram bins in a predictable/repeatable manner. The algorithm compensates for histogram flattening and skewing toward zero as the scene darkens. Change-Id: Ia5acb611f0cc6d726280bd5ea5f45d42ff0dc2dd
--- a/vp9/encoder/vp9_noise_estimate.c
+++ b/vp9/encoder/vp9_noise_estimate.c
@@ -112,10 +112,6 @@
// Estimate of noise level every frame_period frames.
int frame_period = 8;
int thresh_consec_zeromv = 6;
- unsigned int thresh_sum_diff = 100;
- unsigned int thresh_sum_spatial = (200 * 200) << 8;
- unsigned int thresh_spatial_var = (32 * 32) << 8;
- int min_blocks_estimate = cm->mi_rows * cm->mi_cols >> 7;
int frame_counter = cm->current_video_frame;
// Estimate is between current source and last source.
YV12_BUFFER_CONFIG *last_source = cpi->Last_Source;
@@ -126,9 +122,6 @@
// enabled.
if (cm->width > 640 && cm->width <= 1920) {thresh_consec_zeromv = 2;
- thresh_sum_diff = 200;
- thresh_sum_spatial = (120 * 120) << 8;
- thresh_spatial_var = (48 * 48) << 8;
}
}
#endif
@@ -165,11 +158,13 @@
#endif
return;
} else {- int num_samples = 0;
- uint64_t avg_est = 0;
+ unsigned int bin_size = 100;
+ unsigned int hist[MAX_VAR_HIST_BINS] = { 0 };+ unsigned int hist_avg[MAX_VAR_HIST_BINS];
+ unsigned int max_bin = 0;
+ unsigned int max_bin_count = 0;
+ unsigned int bin_cnt;
int bsize = BLOCK_16X16;
- static const unsigned char const_source[16] = { 0, 0, 0, 0, 0, 0, 0, 0,- 0, 0, 0, 0, 0, 0, 0, 0 };
// Loop over sub-sample of 16x16 blocks of frame, and for blocks that have
// been encoded as zero/small mv at least x consecutive frames, compute
// the variance to update estimate of noise in the source.
@@ -222,25 +217,15 @@
}
if (!is_skin) {unsigned int sse;
- // Compute variance.
+ // Compute variance between co-located blocks from current and
+ // last input frames.
unsigned int variance = cpi->fn_ptr[bsize].vf(
src_y, src_ystride, last_src_y, last_src_ystride, &sse);
- // Only consider this block as valid for noise measurement if the
- // average term (sse - variance = N * avg^{2}, N = 16X16) of the- // temporal residual is small (avoid effects from lighting
- // change).
- if ((sse - variance) < thresh_sum_diff) {- unsigned int sse2;
- const unsigned int spatial_variance = cpi->fn_ptr[bsize].vf(
- src_y, src_ystride, const_source, 0, &sse2);
- // Avoid blocks with high brightness and high spatial variance.
- if ((sse2 - spatial_variance) < thresh_sum_spatial &&
- spatial_variance < thresh_spatial_var) {- avg_est += low_res ? variance >> 4
- : variance / ((spatial_variance >> 9) + 1);
- num_samples++;
- }
- }
+ unsigned int hist_index = variance / bin_size;
+ if (hist_index < MAX_VAR_HIST_BINS)
+ hist[hist_index]++;
+ else if (hist_index < 3 * (MAX_VAR_HIST_BINS >> 1))
+ hist[MAX_VAR_HIST_BINS - 1]++; // Account for the tail
}
}
}
@@ -256,25 +241,52 @@
}
ne->last_w = cm->width;
ne->last_h = cm->height;
- // Update noise estimate if we have at a minimum number of block samples,
- // and avg_est > 0 (avg_est == 0 can happen if the application inputs
- // duplicate frames).
- if (num_samples > min_blocks_estimate && avg_est > 0) {- // Normalize.
- avg_est = avg_est / num_samples;
- // Update noise estimate.
- ne->value = (int)((3 * ne->value + avg_est) >> 2);
- ne->count++;
- if (ne->count == ne->num_frames_estimate) {- // Reset counter and check noise level condition.
- ne->num_frames_estimate = 30;
- ne->count = 0;
- ne->level = vp9_noise_estimate_extract_level(ne);
+ // Adjust histogram to account for effect that histogram flattens
+ // and shifts to zero as scene darkens.
+ if (hist[0] > 10 && (hist[MAX_VAR_HIST_BINS - 1] > hist[0] >> 2)) {+ hist[0] = 0;
+ hist[1] >>= 2;
+ hist[2] >>= 2;
+ hist[3] >>= 2;
+ hist[4] >>= 1;
+ hist[5] >>= 1;
+ hist[6] = 3 * hist[6] >> 1;
+ hist[MAX_VAR_HIST_BINS - 1] >>= 1;
+ }
+
+ // Average hist[] and find largest bin
+ for (bin_cnt = 0; bin_cnt < MAX_VAR_HIST_BINS; bin_cnt++) {+ if (bin_cnt == 0)
+ hist_avg[bin_cnt] = (hist[0] + hist[1] + hist[2]) / 3;
+ else if (bin_cnt == MAX_VAR_HIST_BINS - 1)
+ hist_avg[bin_cnt] = hist[MAX_VAR_HIST_BINS - 1] >> 2;
+ else if (bin_cnt == MAX_VAR_HIST_BINS - 2)
+ hist_avg[bin_cnt] = (hist[bin_cnt - 1] + 2 * hist[bin_cnt] +
+ (hist[bin_cnt + 1] >> 1) + 2) >>
+ 2;
+ else
+ hist_avg[bin_cnt] =
+ (hist[bin_cnt - 1] + 2 * hist[bin_cnt] + hist[bin_cnt + 1] + 2) >>
+ 2;
+
+ if (hist_avg[bin_cnt] > max_bin_count) {+ max_bin_count = hist_avg[bin_cnt];
+ max_bin = bin_cnt;
+ }
+ }
+
+ // Scale by 40 to work with existing thresholds
+ ne->value = (int)((3 * ne->value + max_bin * 40) >> 2);
+ ne->count++;
+ if (ne->count == ne->num_frames_estimate) {+ // Reset counter and check noise level condition.
+ ne->num_frames_estimate = 30;
+ ne->count = 0;
+ ne->level = vp9_noise_estimate_extract_level(ne);
#if CONFIG_VP9_TEMPORAL_DENOISING
- if (cpi->oxcf.noise_sensitivity > 0 && noise_est_svc(cpi))
- vp9_denoiser_set_noise_level(cpi, ne->level);
+ if (cpi->oxcf.noise_sensitivity > 0 && noise_est_svc(cpi))
+ vp9_denoiser_set_noise_level(cpi, ne->level);
#endif
- }
}
}
#if CONFIG_VP9_TEMPORAL_DENOISING
--- a/vp9/encoder/vp9_noise_estimate.h
+++ b/vp9/encoder/vp9_noise_estimate.h
@@ -23,6 +23,8 @@
extern "C" {#endif
+#define MAX_VAR_HIST_BINS 20
+
typedef enum noise_level { kLowLow, kLow, kMedium, kHigh } NOISE_LEVEL; typedef struct noise_estimate {