The increasing proportion of video traffic in telecommunication networks puts an emphasis on efficient video compression technology. High Efficiency Video Coding (HEVC) is the forthcoming video coding standard that provides substantial bit rate reductions compared to its predecessors. In the HEVC standardization process, technologies such as picture partitioning, reference picture management and parameter sets are categorized as "high-level syntax". The design of the high-level syntax impacts the interface to systems, error resilience, and provides new functionalities. This paper presents an overview of the HEVC high-level syntax, including NAL unit headers, parameter sets, picture partitioning schemes, reference picture management, and SEI messages.Index Terms-High efficiency video coding (HEVC), JCT-VC, parameter set, reference picture list, reference picture set, video coding.

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The bulk of the video content available today over the Internet and over mobile networks suffers from many imperfections caused during acquisition and transmission. In the case of user-generated content, which is typically produced with inexpensive equipment, these imperfections manifest in various ways through noise, temporal flicker and blurring, just to name a few. Imperfections caused by compression noise and temporal flicker are present in both studio-produced and user-generated video content transmitted at low bit-rates. In this paper, we introduce an algorithm designed to reduce temporal flicker and noise in video sequences. The algorithm takes advantage of the sparse nature of video signals in an appropriate transform domain that is chosen adaptively based on local signal statistics. When the signal corresponds to a sparse representation in this transform domain, flicker and noise, which are spread over the entire domain, can be reduced easily by enforcing sparsity. Our results show that the proposed algorithm reduces flicker and noise significantly and enables better presentation of compressed videos.

This paper presents a method that exploits an optical illusion due to the motion of video sequences for improving the coding efficiency of sequences with large motion. The employed optical illusion is a special case of the motion sharpening, which we call "quasi-motion sharpening". Under this effect, comparable subjective quality is perceived when comparing an original video against the processed same video where some images between two frames are being blurred. A quantitative model based on this phenomenon has been constructed for deriving the level of blurriness of selected frames without sacrificing the subjective quality of the whole sequence.Subjective experiments on decoded video of H.264 show that, using our model for pre-processing video sequences with fast motion, we can improved the coding efficiency by 9.3~14.0% while maintaining the subjective quality.