This paper presents a video encoding method in which noise is encoded using a novel parametric model representing spectral envelope and spatial distribution of energy. The proposed method has been experimentally assessed using video test sequences in a practical setup consisting of a simple, real-time noise reduction technique and High Efficiency Video Codec (HEVC). The attained results show that the use of the proposed parametric modelling of noise can improve the subjective quality of reconstructed video by approximately 1.8 Mean Opinion Scope (MOS) points (in 11-point scale) related to the classical video coding. Moreover, the present work confirms results attained in the previous works that the usage of even sole noise reduction prior to the encoding provides quality increase.

As the most recent video coding standard, High Efficiency Video Coding (HEVC) adopts various novel techniques, including a quad-tree based coding unit (CU) structure and additional angular modes used for intra encoding. These new techniques achieve a notable improvement in coding efficiency at the penalty of significant computational complexity increase. Thus, a fast HEVC coding algorithm is highly desirable. In this paper, we propose a fast intra CU decision algorithm for HEVC to reduce the coding complexity, mainly based on a key-point detection. A CU block is considered to have multiple gradients and is early split if corner points are detected inside the block. On the other hand, a CU block without corner points is treated to be terminated when its RD cost is also small according to statistics of the previous frames. The proposed fast algorithm achieves over 62% encoding time reduction with 3.66%, 2.82%, and 2.53% BD-Rate loss for Y, U, and V components, averagely. The experimental results show that the proposed method is efficient to fast decide CU size in HEVC intra coding, even though only static parameters are applied to all test sequences.

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.