Content classification based reference frame reduction and machine learning based non-square block partition skipping for inter prediction of screen content coding

Yawei Wang, Gaoxing Chen, Takeshi Ikenaga

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

1 Citation (Scopus)

Abstract

Screen Content Coding (SCC) is the extension of the latest video compression standard High Efficiency Video Coding (HEVC). SCC is mainly developed for reducing the bit-rate of videos generated from computers. However, under inter configuration, SCC has large complexity which brings heavy burden to encoding. This paper proposes a content classification based reference frame reduction method and a non-square prediction unit (PU) skipping method to accelerate SCC. In reference frame reduction method, according to number of colors, input coding tree unit (CTUs) will be divided into two classes: natural contents and screen contents. For each class, reference frame can be reduced based on different standard. In PU partition skipping method, five features are extracted from a CTU. The classic learning tool SVM is used to classify CTUs, then six non-square PU partition in depth 1, 2, 3 can be skipped. Finally, 40.83% encoding time saving on average is achieved with only 0.71% BD-rate degradation compared with SCC reference software (SCM6.0).

Original languageEnglish
Title of host publicationProceedings - 2017 2nd International Conference on Multimedia and Image Processing, ICMIP 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages240-244
Number of pages5
Volume2017-January
ISBN (Electronic)9781509059546
DOIs
Publication statusPublished - 2017 Dec 15
Event2nd International Conference on Multimedia and Image Processing, ICMIP 2017 - Wuhan, Hubei, China
Duration: 2017 Mar 172017 Mar 19

Other

Other2nd International Conference on Multimedia and Image Processing, ICMIP 2017
CountryChina
CityWuhan, Hubei
Period17/3/1717/3/19

    Fingerprint

Keywords

  • HEVC
  • Inter prediction
  • Machine learning
  • Prediction unit
  • Reference frame reduction
  • Screen content coding

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Media Technology

Cite this

Wang, Y., Chen, G., & Ikenaga, T. (2017). Content classification based reference frame reduction and machine learning based non-square block partition skipping for inter prediction of screen content coding. In Proceedings - 2017 2nd International Conference on Multimedia and Image Processing, ICMIP 2017 (Vol. 2017-January, pp. 240-244). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMIP.2017.58