Abstract
The joint video expert team (JVET) is currently developing a new video coding standard called H.266/Versatile Video Coding (VVC). Compared with High Efficiency Video Coding (HEVC), VVC has added a variety of coding tools. These tools have greatly improved video compression efficiency and maintained a high level video quality. However, due to the increase in computational complexity, the encoding time is much longer than HEVC. We propose a prediction tool based on DenseNet (a convolutional neural network) to decrease the VVC coding complexity. We predict the probability that the edge of $4 \times 4$ blocks in each $64 \times 64$ block is the division boundary by Convolutional Neural Networks (CNN). Then, we skip the unnecessary rate distortion optimization (RDO) and speed up the coding by probability vectors in advance. The proposed method can reduce the coding complexity of 46.10% in VTM10.0 intra coding, while Bjontegaard delta bit rate (BDBR) only increases by 1.86%. In the sequence with a resolution greater than 1080P, the acceleration efficiency can be at 64.81%, the BDBR loss only increased by 1.92%.
Highlights
Due to the increase in IP video traffic in recent years [1] and the emergence of new video formats such as 4K, 8K, High Frame Rate (HFR), Wide Color Gamut (WCG) and VR video, the demand for video transmission bandwidth and storage has exploded
We provide Convolutional Neural Networks (CNN) with a 64×64 pixel luminance Coding Unit (CU) to predict a vector to represent the probability of an edge on the 4×4 boundary of the block
Tissier et al studied the CU partition complexity of video coding in [6]. They proposed that the computational complexity of block partitioning in Versatile Video Coding (VVC) can be reduced to 3% of the original at most by predicting the correct CU splitting method
Summary
Due to the increase in IP video traffic in recent years [1] and the emergence of new video formats such as 4K, 8K, High Frame Rate (HFR), Wide Color Gamut (WCG) and VR video, the demand for video transmission bandwidth and storage has exploded. The International Telecommunication Union (ITU) and the ISO/IEC Moving Picture Experts Group (MPEG) formed the JVET to develop new video coding standards. Compared with HEVC, the compression ratio of VVC has been greatly improved, but its encoding time is many times that of HEVC. The QTMT division scheme [3] [4] is used in VVC to obtain better compression efficiency. Compared with HEVC [5], this change increases the coding efficiency by 8.5%. An important direction of the current VVC video coding research is how to reduce the coding complexity and increase the coding speed without reducing the VVC coding efficiency or with little loss. The development of artificial neural networks has provided a new direction for the development of fast video coding. The encoder further uses this probability vector to skip the low-probability segmentation
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