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 language | English |
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Title of host publication | Proceedings - 2017 2nd International Conference on Multimedia and Image Processing, ICMIP 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 240-244 |
Number of pages | 5 |
Volume | 2017-January |
ISBN (Electronic) | 9781509059546 |
DOIs | |
Publication status | Published - 2017 Dec 15 |
Event | 2nd International Conference on Multimedia and Image Processing, ICMIP 2017 - Wuhan, Hubei, China Duration: 2017 Mar 17 → 2017 Mar 19 |
Other
Other | 2nd International Conference on Multimedia and Image Processing, ICMIP 2017 |
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Country | China |
City | Wuhan, Hubei |
Period | 17/3/17 → 17/3/19 |
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