Online-Learning-Based Mode Prediction Method for Quality Scalable Extension of the High Efficiency Video Coding (HEVC) Standard

SHVC, the scalable extension of High Efficiency Video Coding (HEVC), uses advanced inter-layer prediction features in addition to the advanced compression tools of HEVC to improve the compression performance. Using combined features has brought us improved compression performance at the cost of huge...

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Veröffentlicht in:IEEE transactions on circuits and systems for video technology Jg. 27; H. 10; S. 2204 - 2215
Hauptverfasser: Tohidypour, Hamid Reza, Bashashati, Hossein, Pourazad, Mahsa T., Nasiopoulos, Panos
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.10.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Bayesian classifier
Codec
Coding
Complexity
Complexity theory
Distance learning
Encoding
low complexity compression
online-learning approach
Performance evaluation
Probabilistic logic
Probabilistic models
Scalability
scalable High Efficiency Video Coding (HEVC)
Static VAr compensators
Video compression
ISSN:1051-8215, 1558-2205
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Zusammenfassung:SHVC, the scalable extension of High Efficiency Video Coding (HEVC), uses advanced inter-layer prediction features in addition to the advanced compression tools of HEVC to improve the compression performance. Using combined features has brought us improved compression performance at the cost of huge computational complexity for the SHVC encoder. This complexity is mainly because of the the inter/intra-prediction mode search of the coding units. The focus of this study is on developing an efficient complexity reduction for quality scalability of SHVC encoder, with the intention to facilitate the adoption of SHVC for real-time applications. In this regard, first, we build a probabilistic model that uses the mode information and motion homogeneity of already encoded blocks in the enhancement layer (EL) and the base layer to predict the probabilities of all the available inter/intra modes of the to-be-coded block in the EL. Then, we propose an online-learning-based fast mode, assigning (FMA) method that uses the proposed probabilistic model to predict the mode of the to-be-coded block in the EL. Performance evaluation shows that our proposed FMA method reduces the total execution time of the SHVC encoder by 45.40% on average compared with unmodified SHVC codec while maintaining the overall video quality.
Bibliographie:ObjectType-Article-1
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ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2016.2576738