Edge-based Adaptive Directional Intra Prediction

Abstract

H.264/AVC employs intra prediction to reduce spatial redundancy between neighboring blocks. Different directional prediction modes are used to cater diversified video content. Although it achieves quite high coding efficiency, it is desirable to analyze its drawbacks in the existing video coding standard, since it allows us to design better ones. Basically, even after intra prediction, the residue still contains a lot of edge or texture information. Unfortunately, these high frequency components consume a large quantity of bits and the distortion is usually quite high. Based on this drawback, an Edge-based Adaptive Directional Intra Prediction is proposed (EADIP) to reduce the residue energy especially for the edge region. In particular, we establish an edge model in EADIP, which is quite flexible for natural images. Within the model, the edge splits the macroblock into two regions, each being predicted separately. In implementation, we consider the current trend of mode selection and complexity issues. A mode extension is made on INTRA 16×16 in H.264/AVC. Experimental results show that the proposed algorithm outperforms H.264/AVC. And the proposed mode is more likely to be chosen in low bitrate situations.