TY - GEN
T1 - A novel screen content fast transcoding framework based on statistical study and machine learning
AU - Duanmu, Fanyi
AU - Ma, Zhan
AU - Wang, Wei
AU - Xu, Meng
AU - Wang, Yao
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/8/3
Y1 - 2016/8/3
N2 - 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.
AB - 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.
KW - Fast Mode Decision (FMD)
KW - High Efficiency Video Coding (HEVC)
KW - Machine Learning (ML)
KW - Screen Content Coding (SCC)
KW - Video Transcoding (VTC)
UR - http://www.scopus.com/inward/record.url?scp=85006783293&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85006783293&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2016.7533152
DO - 10.1109/ICIP.2016.7533152
M3 - Conference contribution
AN - SCOPUS:85006783293
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 4205
EP - 4209
BT - 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PB - IEEE Computer Society
T2 - 23rd IEEE International Conference on Image Processing, ICIP 2016
Y2 - 25 September 2016 through 28 September 2016
ER -