Efficient VVC Intra Prediction Based on Deep Feature Fusion and Probability Estimation

TL;DR

This paper introduces a two-stage deep learning-based method to reduce the complexity of VVC intra prediction, maintaining high coding efficiency while significantly decreasing encoding time for HD and UHD videos.

Contribution

It presents a novel deep feature fusion and probability estimation framework to optimize intra prediction complexity in VVC, improving speed without sacrificing performance.

Findings

Reduces encoding complexity significantly for HD and UHD videos.

Maintains comparable Rate Distortion performance with faster encoding.

Demonstrates superior results on standard video datasets.

Abstract

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.