fa0befe13b
Improved the existing external denoiser in WebRTC: the filter strength is adaptive based on the noise level of the whole frame and the moving object detection result. The adaptive filter effectively removes the artifacts in previous version, such as trailing and blockiness on moving objects. The external denoiser is off by default for now. BUG= Review URL: https://codereview.webrtc.org/1822333003 Cr-Commit-Position: refs/heads/master@{#12198}
312 lines
13 KiB
C++
312 lines
13 KiB
C++
/*
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* Copyright (c) 2015 The WebRTC project authors. All Rights Reserved.
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*
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* Use of this source code is governed by a BSD-style license
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* that can be found in the LICENSE file in the root of the source
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* tree. An additional intellectual property rights grant can be found
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* in the file PATENTS. All contributing project authors may
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* be found in the AUTHORS file in the root of the source tree.
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*/
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#include "webrtc/common_video/libyuv/include/scaler.h"
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#include "webrtc/common_video/libyuv/include/webrtc_libyuv.h"
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#include "webrtc/modules/video_processing/video_denoiser.h"
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namespace webrtc {
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VideoDenoiser::VideoDenoiser(bool runtime_cpu_detection)
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: width_(0),
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height_(0),
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filter_(DenoiserFilter::Create(runtime_cpu_detection, &cpu_type_)),
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ne_(new NoiseEstimation()) {}
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#if EXPERIMENTAL
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// Check the mb position(1: close to the center, 3: close to the border).
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static int PositionCheck(int mb_row, int mb_col, int mb_rows, int mb_cols) {
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if ((mb_row >= (mb_rows >> 3)) && (mb_row <= (7 * mb_rows >> 3)) &&
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(mb_col >= (mb_cols >> 3)) && (mb_col <= (7 * mb_cols >> 3)))
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return 1;
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else if ((mb_row >= (mb_rows >> 4)) && (mb_row <= (15 * mb_rows >> 4)) &&
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(mb_col >= (mb_cols >> 4)) && (mb_col <= (15 * mb_cols >> 4)))
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return 2;
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else
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return 3;
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}
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static void ReduceFalseDetection(const std::unique_ptr<uint8_t[]>& d_status,
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std::unique_ptr<uint8_t[]>* d_status_tmp1,
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std::unique_ptr<uint8_t[]>* d_status_tmp2,
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int noise_level,
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int mb_rows,
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int mb_cols) {
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// Draft. This can be optimized. This code block is to reduce false detection
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// in moving object detection.
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int mb_row_min = noise_level ? mb_rows >> 3 : 1;
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int mb_col_min = noise_level ? mb_cols >> 3 : 1;
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int mb_row_max = noise_level ? (7 * mb_rows >> 3) : mb_rows - 2;
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int mb_col_max = noise_level ? (7 * mb_cols >> 3) : mb_cols - 2;
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memcpy((*d_status_tmp1).get(), d_status.get(), mb_rows * mb_cols);
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// Up left.
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for (int mb_row = mb_row_min; mb_row <= mb_row_max; ++mb_row) {
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for (int mb_col = mb_col_min; mb_col <= mb_col_max; ++mb_col) {
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(*d_status_tmp1)[mb_row * mb_cols + mb_col] |=
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((*d_status_tmp1)[(mb_row - 1) * mb_cols + mb_col] |
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(*d_status_tmp1)[mb_row * mb_cols + mb_col - 1]);
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}
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}
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memcpy((*d_status_tmp2).get(), (*d_status_tmp1).get(), mb_rows * mb_cols);
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memcpy((*d_status_tmp1).get(), d_status.get(), mb_rows * mb_cols);
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// Bottom left.
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for (int mb_row = mb_row_max; mb_row >= mb_row_min; --mb_row) {
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for (int mb_col = mb_col_min; mb_col <= mb_col_max; ++mb_col) {
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(*d_status_tmp1)[mb_row * mb_cols + mb_col] |=
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((*d_status_tmp1)[(mb_row + 1) * mb_cols + mb_col] |
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(*d_status_tmp1)[mb_row * mb_cols + mb_col - 1]);
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(*d_status_tmp2)[mb_row * mb_cols + mb_col] &=
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(*d_status_tmp1)[mb_row * mb_cols + mb_col];
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}
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}
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memcpy((*d_status_tmp1).get(), d_status.get(), mb_rows * mb_cols);
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// Up right.
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for (int mb_row = mb_row_min; mb_row <= mb_row_max; ++mb_row) {
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for (int mb_col = mb_col_max; mb_col >= mb_col_min; --mb_col) {
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(*d_status_tmp1)[mb_row * mb_cols + mb_col] |=
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((*d_status_tmp1)[(mb_row - 1) * mb_cols + mb_col] |
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(*d_status_tmp1)[mb_row * mb_cols + mb_col + 1]);
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(*d_status_tmp2)[mb_row * mb_cols + mb_col] &=
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(*d_status_tmp1)[mb_row * mb_cols + mb_col];
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}
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}
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memcpy((*d_status_tmp1).get(), d_status.get(), mb_rows * mb_cols);
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// Bottom right.
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for (int mb_row = mb_row_max; mb_row >= mb_row_min; --mb_row) {
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for (int mb_col = mb_col_max; mb_col >= mb_col_min; --mb_col) {
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(*d_status_tmp1)[mb_row * mb_cols + mb_col] |=
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((*d_status_tmp1)[(mb_row + 1) * mb_cols + mb_col] |
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(*d_status_tmp1)[mb_row * mb_cols + mb_col + 1]);
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(*d_status_tmp2)[mb_row * mb_cols + mb_col] &=
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(*d_status_tmp1)[mb_row * mb_cols + mb_col];
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}
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}
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}
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static bool TrailingBlock(const std::unique_ptr<uint8_t[]>& d_status,
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int mb_row,
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int mb_col,
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int mb_rows,
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int mb_cols) {
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int mb_index = mb_row * mb_cols + mb_col;
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if (!mb_row || !mb_col || mb_row == mb_rows - 1 || mb_col == mb_cols - 1)
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return false;
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return d_status[mb_index + 1] || d_status[mb_index - 1] ||
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d_status[mb_index + mb_cols] || d_status[mb_index - mb_cols];
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}
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#endif
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#if DISPLAY
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void ShowRect(const std::unique_ptr<DenoiserFilter>& filter,
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const std::unique_ptr<uint8_t[]>& d_status,
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const std::unique_ptr<uint8_t[]>& d_status_tmp2,
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const std::unique_ptr<uint8_t[]>& x_density,
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const std::unique_ptr<uint8_t[]>& y_density,
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const uint8_t* u_src,
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const uint8_t* v_src,
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uint8_t* u_dst,
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uint8_t* v_dst,
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int mb_rows,
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int mb_cols,
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int stride_u,
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int stride_v) {
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for (int mb_row = 0; mb_row < mb_rows; ++mb_row) {
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for (int mb_col = 0; mb_col < mb_cols; ++mb_col) {
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int mb_index = mb_row * mb_cols + mb_col;
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const uint8_t* mb_src_u =
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u_src + (mb_row << 3) * stride_u + (mb_col << 3);
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const uint8_t* mb_src_v =
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v_src + (mb_row << 3) * stride_v + (mb_col << 3);
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uint8_t* mb_dst_u = u_dst + (mb_row << 3) * stride_u + (mb_col << 3);
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uint8_t* mb_dst_v = v_dst + (mb_row << 3) * stride_v + (mb_col << 3);
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uint8_t y_tmp_255[8 * 8];
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memset(y_tmp_255, 200, 8 * 8);
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// x_density_[mb_col] * y_density_[mb_row]
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if (d_status[mb_index] == 1) {
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// Paint to red.
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filter->CopyMem8x8(mb_src_u, stride_u, mb_dst_u, stride_u);
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filter->CopyMem8x8(y_tmp_255, 8, mb_dst_v, stride_v);
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#if EXPERIMENTAL
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} else if (d_status_tmp2[mb_row * mb_cols + mb_col] &&
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x_density[mb_col] * y_density[mb_row]) {
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#else
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} else if (x_density[mb_col] * y_density[mb_row]) {
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#endif
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// Paint to blue.
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filter->CopyMem8x8(y_tmp_255, 8, mb_dst_u, stride_u);
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filter->CopyMem8x8(mb_src_v, stride_v, mb_dst_v, stride_v);
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} else {
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filter->CopyMem8x8(mb_src_u, stride_u, mb_dst_u, stride_u);
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filter->CopyMem8x8(mb_src_v, stride_v, mb_dst_v, stride_v);
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}
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}
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}
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}
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#endif
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void VideoDenoiser::DenoiseFrame(const VideoFrame& frame,
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VideoFrame* denoised_frame,
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VideoFrame* denoised_frame_prev,
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int noise_level_prev) {
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int stride_y = frame.stride(kYPlane);
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int stride_u = frame.stride(kUPlane);
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int stride_v = frame.stride(kVPlane);
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// If previous width and height are different from current frame's, then no
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// denoising for the current frame.
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if (width_ != frame.width() || height_ != frame.height()) {
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width_ = frame.width();
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height_ = frame.height();
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denoised_frame->CreateFrame(frame.buffer(kYPlane), frame.buffer(kUPlane),
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frame.buffer(kVPlane), width_, height_,
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stride_y, stride_u, stride_v, kVideoRotation_0);
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denoised_frame_prev->CreateFrame(
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frame.buffer(kYPlane), frame.buffer(kUPlane), frame.buffer(kVPlane),
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width_, height_, stride_y, stride_u, stride_v, kVideoRotation_0);
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// Setting time parameters to the output frame.
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denoised_frame->set_timestamp(frame.timestamp());
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denoised_frame->set_render_time_ms(frame.render_time_ms());
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ne_->Init(width_, height_, cpu_type_);
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return;
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}
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// For 16x16 block.
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int mb_cols = width_ >> 4;
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int mb_rows = height_ >> 4;
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if (metrics_.get() == nullptr)
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metrics_.reset(new DenoiseMetrics[mb_cols * mb_rows]());
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if (d_status_.get() == nullptr) {
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d_status_.reset(new uint8_t[mb_cols * mb_rows]());
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#if EXPERIMENTAL
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d_status_tmp1_.reset(new uint8_t[mb_cols * mb_rows]());
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d_status_tmp2_.reset(new uint8_t[mb_cols * mb_rows]());
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#endif
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x_density_.reset(new uint8_t[mb_cols]());
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y_density_.reset(new uint8_t[mb_rows]());
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}
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// Denoise on Y plane.
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uint8_t* y_dst = denoised_frame->buffer(kYPlane);
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uint8_t* u_dst = denoised_frame->buffer(kUPlane);
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uint8_t* v_dst = denoised_frame->buffer(kVPlane);
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uint8_t* y_dst_prev = denoised_frame_prev->buffer(kYPlane);
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const uint8_t* y_src = frame.buffer(kYPlane);
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const uint8_t* u_src = frame.buffer(kUPlane);
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const uint8_t* v_src = frame.buffer(kVPlane);
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uint8_t noise_level = noise_level_prev == -1 ? 0 : ne_->GetNoiseLevel();
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// Temporary buffer to store denoising result.
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uint8_t y_tmp[16 * 16] = {0};
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memset(x_density_.get(), 0, mb_cols);
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memset(y_density_.get(), 0, mb_rows);
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// Loop over blocks to accumulate/extract noise level and update x/y_density
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// factors for moving object detection.
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for (int mb_row = 0; mb_row < mb_rows; ++mb_row) {
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for (int mb_col = 0; mb_col < mb_cols; ++mb_col) {
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const uint8_t* mb_src = y_src + (mb_row << 4) * stride_y + (mb_col << 4);
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uint8_t* mb_dst_prev =
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y_dst_prev + (mb_row << 4) * stride_y + (mb_col << 4);
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int mb_index = mb_row * mb_cols + mb_col;
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#if EXPERIMENTAL
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int pos_factor = PositionCheck(mb_row, mb_col, mb_rows, mb_cols);
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uint32_t thr_var_adp = 16 * 16 * 5 * (noise_level ? pos_factor : 1);
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#else
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uint32_t thr_var_adp = 16 * 16 * 5;
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#endif
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int brightness = 0;
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for (int i = 0; i < 16; ++i) {
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for (int j = 0; j < 16; ++j) {
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brightness += mb_src[i * stride_y + j];
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}
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}
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// Get the denoised block.
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filter_->MbDenoise(mb_dst_prev, stride_y, y_tmp, 16, mb_src, stride_y, 0,
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1, true);
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// The variance is based on the denoised blocks in time T and T-1.
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metrics_[mb_index].var = filter_->Variance16x8(
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mb_dst_prev, stride_y, y_tmp, 16, &metrics_[mb_index].sad);
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if (metrics_[mb_index].var > thr_var_adp) {
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ne_->ResetConsecLowVar(mb_index);
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d_status_[mb_index] = 1;
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#if EXPERIMENTAL
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if (noise_level == 0 || pos_factor < 3) {
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x_density_[mb_col] += 1;
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y_density_[mb_row] += 1;
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}
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#else
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x_density_[mb_col] += 1;
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y_density_[mb_row] += 1;
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#endif
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} else {
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uint32_t sse_t = 0;
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// The variance is based on the src blocks in time T and denoised block
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// in time T-1.
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uint32_t noise_var = filter_->Variance16x8(mb_dst_prev, stride_y,
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mb_src, stride_y, &sse_t);
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ne_->GetNoise(mb_index, noise_var, brightness);
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d_status_[mb_index] = 0;
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}
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// Track denoised frame.
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filter_->CopyMem16x16(y_tmp, 16, mb_dst_prev, stride_y);
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}
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}
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#if EXPERIMENTAL
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ReduceFalseDetection(d_status_, &d_status_tmp1_, &d_status_tmp2_, noise_level,
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mb_rows, mb_cols);
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#endif
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// Denoise each MB based on the results of moving objects detection.
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for (int mb_row = 0; mb_row < mb_rows; ++mb_row) {
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for (int mb_col = 0; mb_col < mb_cols; ++mb_col) {
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const uint8_t* mb_src = y_src + (mb_row << 4) * stride_y + (mb_col << 4);
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uint8_t* mb_dst = y_dst + (mb_row << 4) * stride_y + (mb_col << 4);
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const uint8_t* mb_src_u =
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u_src + (mb_row << 3) * stride_u + (mb_col << 3);
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const uint8_t* mb_src_v =
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v_src + (mb_row << 3) * stride_v + (mb_col << 3);
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uint8_t* mb_dst_u = u_dst + (mb_row << 3) * stride_u + (mb_col << 3);
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uint8_t* mb_dst_v = v_dst + (mb_row << 3) * stride_v + (mb_col << 3);
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#if EXPERIMENTAL
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if ((!d_status_tmp2_[mb_row * mb_cols + mb_col] ||
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x_density_[mb_col] * y_density_[mb_row] == 0) &&
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!TrailingBlock(d_status_, mb_row, mb_col, mb_rows, mb_cols)) {
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#else
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if (x_density_[mb_col] * y_density_[mb_row] == 0) {
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#endif
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if (filter_->MbDenoise(mb_dst, stride_y, y_tmp, 16, mb_src, stride_y, 0,
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noise_level, false) == FILTER_BLOCK) {
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filter_->CopyMem16x16(y_tmp, 16, mb_dst, stride_y);
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} else {
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// Copy y source.
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filter_->CopyMem16x16(mb_src, stride_y, mb_dst, stride_y);
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}
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} else {
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// Copy y source.
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filter_->CopyMem16x16(mb_src, stride_y, mb_dst, stride_y);
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}
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filter_->CopyMem8x8(mb_src_u, stride_u, mb_dst_u, stride_u);
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filter_->CopyMem8x8(mb_src_v, stride_v, mb_dst_v, stride_v);
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}
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}
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#if DISPLAY // Rectangle diagnostics
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// Show rectangular region
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ShowRect(filter_, d_status_, d_status_tmp2_, x_density_, y_density_, u_src,
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v_src, u_dst, v_dst, mb_rows, mb_cols, stride_u, stride_v);
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#endif
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// Setting time parameters to the output frame.
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denoised_frame->set_timestamp(frame.timestamp());
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denoised_frame->set_render_time_ms(frame.render_time_ms());
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return;
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}
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} // namespace webrtc
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