DeepSCC: Deep Learning-Based Fast Prediction Network for Screen Content Coding

Wei Kuang, Yui Lam Chan, Sik Ho Tsang, Wan Chi Siu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Screen content coding (SCC) is an extension of high efficiency video coding (HEVC), and it is developed to improve the coding efficiency of screen content videos by adopting two new coding modes: Intra Block Copy (IBC) and Palette (PLT). However, the flexible quadtree-based coding tree unit (CTU) partitioning structure and various mode candidates make the fast algorithms of the SCC extremely challenging. To efficiently reduce the computational complexity of SCC, we propose a deep learning-based fast prediction network DeepSCC that contains two parts: DeepSCC-I and DeepSCC-II. Before feeding to DeepSCC, incoming coding units (CUs) are divided into two categories: dynamic CTUs and stationary CTUs. For dynamic CTUs having different content as their collocated CTUs, DeepSCC-I takes raw sample values as the input to make fast predictions. For stationary CTUs having the same content as their collocated CTUs, DeepSCC-II additionally utilizes the optimal mode maps of the stationary CTU to further reduce the computational complexity. Compared with the HEVC-SCC reference software SCM-8.3, the proposed DeepSCC reduces the encoding time by 48.81% on average with a negligible Bjontegaard delta bitrate increase of 1.18% under all-intra configuration.

Original languageEnglish
Article number8764598
Pages (from-to)1917-1932
Number of pages16
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume30
Issue number7
DOIs
Publication statusPublished - Jul 2020

Keywords

  • convolutional neural network (CNN)
  • deep learning
  • fast algorithm
  • high efficiency video coding (HEVC)
  • Screen content coding (SCC)

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering

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