Efficient Prediction Methods With Enhanced Spatial-Temporal Correlation for HEVC

Abstract

The state-of-the-art high efficiency video coding (HEVC) standard provides a significant improvement relative to H.264/AVC, with almost 50% bitrate reduction. However, there are still requirements to further improve the coding performance of HEVC. In this paper, we reveal some problems in both intra and inter prediction by giving detailed observations, and solve them by further exploring spatial and temporal correlations. First, it is noteworthy that large boundary distortions in intra prediction blocks arise from the simplistic extrapolation operation with partial reference samples. To reduce these boundary distortions, we propose an enhanced prediction boundary filtering method based on generalized Gaussian distribution functions, and extend this method to merge prediction. Second, we combine intra and inter prediction to create a novel prediction method, in which spatial and temporal correlations are simultaneously considered. In addition, the existing merge mode can only provide accurate prediction results for simple movements, owing to the lack of motion estimation. To improve the flexibility, we optimize the existing merge mode by intensifying the temporal correlation with a simple motion search process. The experimental results show that the proposed methods achieve consistent bitrate savings, 1.3% for all intra, 1.5% for random access, 2.5% (up to 4.2%) for low delay P, and 1.2% for low delay B configuration. The encoding time only rises by 70% - 80%, and the decoding time increases negligibly.