Efficient VVC Intra Prediction Based on Deep Feature Fusion and Probability Estimation
This work addresses the need for faster video encoding with comparable quality, which is crucial for reducing computational costs in multimedia applications, though it is incremental as it builds on existing VVC methods.
The paper tackles the high encoding complexity of the Versatile Video Coding (VVC) standard by proposing a two-stage framework using deep feature fusion and probability estimation to optimize intra-frame prediction, achieving superior performance for HD and UHD video sequences.
The ever-growing multimedia traffic has underscored the importance of effective multimedia codecs. Among them, the up-to-date lossy video coding standard, Versatile Video Coding (VVC), has been attracting attentions of video coding community. However, the gain of VVC is achieved at the cost of significant encoding complexity, which brings the need to realize fast encoder with comparable Rate Distortion (RD) performance. In this paper, we propose to optimize the VVC complexity at intra-frame prediction, with a two-stage framework of deep feature fusion and probability estimation. At the first stage, we employ the deep convolutional network to extract the spatialtemporal neighboring coding features. Then we fuse all reference features obtained by different convolutional kernels to determine an optimal intra coding depth. At the second stage, we employ a probability-based model and the spatial-temporal coherence to select the candidate partition modes within the optimal coding depth. Finally, these selected depths and partitions are executed whilst unnecessary computations are excluded. Experimental results on standard database demonstrate the superiority of proposed method, especially for High Definition (HD) and Ultra-HD (UHD) video sequences.