A unified architecture for fast HEVC intra-prediction coding

Abstract

The high efficiency video coding (HEVC) is the new video coding standard, which obtains over 50% bit rate savings compared with H.264/AVC for the same perceptual quality. Intra-prediction coding in HEVC achieves high coding performance in expense of high computational complexity, due to the exhaustive evaluation of all available coding units (CU) sizes, with up to 35 prediction modes for each CU, selecting the one with the lower rate distortion cost, among other new features. This paper presents a Unified Architecture to form a novel fast HEVC intra-prediction coding algorithm, denoted as fast partitioning and mode decision. This approach combines a fast partitioning decision algorithm, based on decision trees, which are trained using machine learning techniques, and a fast mode decision algorithm, based on a novel texture orientation detection algorithm, which computes the mean directional variance along a set of co-lines with rational slopes using a sliding window over the prediction unit. Both algorithms proposed apply a similar approach, exploiting the strong correlation between several image features and the optimal CTU partitioning and the optimal prediction mode. The key point of the combined approach is that both algorithms compute the image features with low complexity, and the partition decision and the mode decision can also be taken with low complexity, using decision trees (if-else statements) and by selecting the minimum directional variance between a reduced set of directions. This approach can be implemented using any combination of nodes, obtaining a wide range of time savings, from 44 to 67%, and light penalties from 1.1 to 4.6%. Comparisons with similar state-of-the-art works show the proposed approach achieves the best trade-off between complexity reduction and rate distortion.