A novel screen content fast transcoding framework based on statistical study and machine learning

Fanyi Duanmu, Zhan Ma, Wei Wang, Meng Xu, Yao Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, a novel screen content transcoding framework is presented to efficiently bridge the state-of-art High Efficiency Video Coding (HEVC) standard and its incoming screen content coding (SCC) extension currently pending finalization. The proposed scheme is implemented as an Intra-coding 'pre-processing' module on top of official SCC test model software (SCM). Both Coding Unit (CU) statistical features (such as CU color quantity, CU pixel variance, CU edge directionality distribution, etc.) and decoded video side information (such as CU partitions, modes, residual, etc.) are jointly analyzed. Accordingly, fast CU mode decisions and CU partitions bypass / termination heuristics are designed. Compared with SCM-4.0 official release, the proposed fast transcoding scheme can achieve an average of 48% re-encoding complexity reduction over JCT-VC screen content testing sequences with less than 2.14% marginal BD-Rate increase under SCC common testing conditions for All-Intra (AI) configuration.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PublisherIEEE Computer Society
Pages4205-4209
Number of pages5
ISBN (Electronic)9781467399616
DOIs
StatePublished - Aug 3 2016
Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
Duration: Sep 25 2016Sep 28 2016

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2016-August
ISSN (Print)1522-4880

Other

Other23rd IEEE International Conference on Image Processing, ICIP 2016
CountryUnited States
CityPhoenix
Period9/25/169/28/16

Keywords

  • Fast Mode Decision (FMD)
  • High Efficiency Video Coding (HEVC)
  • Machine Learning (ML)
  • Screen Content Coding (SCC)
  • Video Transcoding (VTC)

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Fingerprint Dive into the research topics of 'A novel screen content fast transcoding framework based on statistical study and machine learning'. Together they form a unique fingerprint.

Cite this