The paper presents Improved Adaptive Arithmetic Coding algorithm for application in future video compression technology. The proposed solution is based on the Context-based Adaptive Binary Arithmetic Coding (CABAC) technique and uses the authors mechanism of symbols probability estimation that exploits Context-Tree Weighting (CTW) technique. This paper proposes the version of the algorithm, that allows an arbitrary selection of depth D of context trees, when activating the algorithm in the framework of the AVC or HEVC video encoders. The algorithm has been tested in terms of coding efficiency of data and its computational complexity. Results showed, that depending on depth of context trees from 0.1% to 0.86% reduction of bitrate is achieved, when using the algorithm in the HEVC video encoder and 0.4% to 2.3% compression gain in the case of the AVC. The new solution increases complexity of entropy encoder itself, however, this does not cause an increase of the complexity of the whole video encoder.
Optimization of encoding process in video compression is an important research problem, especially in the case of modern, sophisticated compression technologies. In this paper, we consider HEVC, for which a novel method for selection of the encoding modes is proposed. By the encoding modes we mean e.g. coding block structure, prediction types and motion vectors. The proposed selection is done basing on noise-reduced version of the input sequence, while the information about the video itself, e.g. transform coefficients, is coded basing on the unaltered input. The proposed method involves encoding of two versions of the input sequence. Further, we show realization proving that the complexity is only negligibly higher than complexity of a single encoding. The proposal has been implemented in HEVC reference software from MPEG and tested experimentally. The results show that the proposal provides up to 1.5% bitrate reduction while preserving the same quality of a decoded video.