Statistical Early Termination and Early Skip Models for Fast Mode Decision in HEVC INTRA Coding

Abstract

In this article, statistical Early Termination (ET) and Early Skip (ES) models are proposed for fast Coding Unit (CU) and prediction mode decision in HEVC INTRA coding, in which three categories of ET and ES sub-algorithms are included. First, the CU ranges of the current CU are recursively predicted based on the texture and CU depth of the spatial neighboring CUs. Second, the statistical model based ET and ES schemes are proposed and applied to optimize the CU and INTRA prediction mode decision, in which the coding complexities over different decision layers are jointly minimized subject to acceptable rate-distortion degradation. Third, the mode correlations among the INTRA prediction modes are exploited to early terminate the full rate-distortion optimization in each CU decision layer. Extensive experiments are performed to evaluate the coding performance of each sub-algorithm and the overall algorithm. Experimental results reveal that the overall proposed algorithm can achieve 45.47% to 74.77%, and 58.09% on average complexity reduction, while the overall Bjøntegaard delta bit rate increase and Bjøntegaard delta peak signal-to-noise ratio degradation are 2.29% and −0.11 dB, respectively.