A early mode decision algorithm based on statistics and machine learning for enhancement layers in H.264 Scalable Video Coding

In this paper, a early mode decision algorithm is proposed to reduce the complexity of the mode selection process for enhancement layers in H.264 Scalable Video Coding. Generally, the proposed algorithm consists of the following three main steps, which are applied to the enhancement layer. Firstly,...

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Bibliographic Details
Main Authors: Chun Yuan, Bolin Xu, Yuguang Guo
Format: Conference Proceeding
Language:English
Subjects:
early termination
Educational institutions
Encoding
fast mode decision
Information science
machine learning
Machine learning algorithms
Scalable Video Coding
Signal processing algorithms
Static VAr compensators
statistics
Video coding
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Summary:In this paper, a early mode decision algorithm is proposed to reduce the complexity of the mode selection process for enhancement layers in H.264 Scalable Video Coding. Generally, the proposed algorithm consists of the following three main steps, which are applied to the enhancement layer. Firstly, we divide all the macroblocks into 4 classes according to the mode of collocated macroblocks in the base layer. Then, according to the mode of neighboring macroblocks, the macroblocks are subdivided with trained BP (Back Propagation) network. At last, for different cases we choose different mode selection algorithms. Compared to JSVM 9.18, experiment results show that, with this algorithm, 30% encoding time can be saved with a negligible loss in BDSNR, and BDBR can be significantly reduced.
DOI:10.1109/ICMT.2011.6001987