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

Tiesong ZHAO, Yuhang HUANG, Weize FENG, Yiwen XU, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

7 Citations (Scopus)

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 spatial-temporal 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.
Original languageEnglish
Pages (from-to)6411-6421
Number of pages11
JournalIEEE Transactions on Multimedia
Volume25
Early online date21 Sept 2022
DOIs
Publication statusPublished - 2023
Externally publishedYes

Keywords

  • Complexity theory
  • Computational modeling
  • Convolutional neural networks
  • Encoding
  • Feature extraction
  • intra coding
  • Kernel
  • Rate-Distortion (RD)
  • Versatile Video Coding (VVC)
  • Video coding
  • video coding

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