Fast Adaptive Hierarchical Prediction Algorithm for H.264/AVC Scalable Video Coding

Abstract

The objective of scalable video coding (SVC) is to enable the generation of a unique bitstream that can adapt to various bit-rates, transmission channels and display capabilities. The scalability is categorised in terms of temporal, spatial, and quality. In order to improve encoding efficiency, the SVC scheme incorporates inter-layer prediction mechanisms to complement the H.264/AVC very refined Motion Estimation (ME) and mode decision processes. However, this further increases the overall encoding complexity of the scalable coding standard. In this paper several conditional probabilities are established relating motion estimation characteristics and the mode distribution at different layers of the H.264/SVC. An evaluation of these probabilities is used to structure a low-complexity prediction algorithm for Group of Pictures (GOP) in H.264/SVC, reducing computational complexity whilst maintaining similar RD performance. When compared to the JSVM software, the proposed algorithm achieves a significant reduction of encoding time, with a negligible average PSNR loss and bit-rate increase in temporal, spatial and SNR scalability. Experiments are conducted to provide a comparison between our method and recently developed fast mode selection algorithms. These demonstrate the proposed method achieves appreciable time savings for scalable spatial and scalable quality video coding, while maintaining similar PSNR and bit rate.