Abstract
With the great flexibility and performance of deep learning technology, there have been many attempts to replace existing functions inside video codecs such as High-Efficiency Video Coding (HEVC) with deep-learning-based solutions. One of the most researched approaches is adopting a deep network as an image restoration filter to recover distorted compressed frames. In this paper, instead, we introduce a novel idea for using a deep network, in which it chooses and transmits the side information according to the type of errors and contents, inspired by the sample adaptive offset filter in HEVC. A part of the network computes the optimal offset values while another part estimates the type of error and contents simultaneously. The combination of two subnetworks can address the estimation of highly nonlinear and complicated errors compared to conventional deep- learning-based schemes. Experimental results show that the proposed system yields an average bit-rate saving of 4.2% and 2.8% for the low-delay P and random access modes, respectively, compared to the conventional HEVC. Moreover, the performance improvement is up to 6.3% and 3.9% for higher-resolution sequences.
Highlights
With the continuous development of electronic devices such as smartphones and digital TVs, video plays increasingly important roles in our lives
Variable-filter-size residual-learning CNN (VRCNN) and deep convolutional neural network-based auto decoder (DCAD) are originally proposed as post-processing method, and residual highway convolutional neural network (RHNet) and dense residual convolutional neural network (DRN) are proposed as in-loop filter
VRCNN replaces both deblocking filter (DF) and sample adaptive offset (SAO), and training samples were prepared with DF and SAO turned off
Summary
With the continuous development of electronic devices such as smartphones and digital TVs, video plays increasingly important roles in our lives. With the same video resolution, HEVC requires 40–50% fewer bits than H.264/AVC to yield similar-quality video It is composed of dozens of modules, such as motion compensation, discrete cosine transform (DCT), quantization, in-loop filtering, and context-adaptive binary arithmetic coding (CABAC) [3]. The in-loop filter is designed to reduce compression artifacts caused by block-wise quantization and at the same time achieve bit-rate savings by restoring a damaged frame as close as possible to the original. It consists of a deblocking filter (DF) [4] and a sample adaptive offset (SAO) filter [5].
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