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 |
|---|---|
| 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 |
|---|---|
| Country | China |
| City | Wuhan, Hubei |
| Period | 17/3/17 → 17/3/19 |
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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
Content classification based reference frame reduction and machine learning based non-square block partition skipping for inter prediction of screen content coding. / Wang, Yawei; Chen, Gaoxing; Ikenaga, Takeshi.
Proceedings - 2017 2nd International Conference on Multimedia and Image Processing, ICMIP 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 240-244.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Content classification based reference frame reduction and machine learning based non-square block partition skipping for inter prediction of screen content coding
AU - Wang, Yawei
AU - Chen, Gaoxing
AU - Ikenaga, Takeshi
PY - 2017/12/15
Y1 - 2017/12/15
N2 - 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).
AB - 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).
KW - HEVC
KW - Inter prediction
KW - Machine learning
KW - Prediction unit
KW - Reference frame reduction
KW - Screen content coding
UR - http://www.scopus.com/inward/record.url?scp=85046257099&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046257099&partnerID=8YFLogxK
U2 - 10.1109/ICMIP.2017.58
DO - 10.1109/ICMIP.2017.58
M3 - Conference contribution
AN - SCOPUS:85046257099
VL - 2017-January
SP - 240
EP - 244
BT - Proceedings - 2017 2nd International Conference on Multimedia and Image Processing, ICMIP 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -