Traditional video compression techniques heavily rely on the concept of motion compensation to predict frames in a video given previous or future frames. With the recent advances in artificial intelligence powered techniques, new approaches to video coding become apparent. We describe a video compression scheme that employs neural network based image compression and frame generation. The proposed method encodes key frames (I frames) as still images and entirely skips compression and signalling of intermediate frames (S frames), which are conversely synthesized on the decoder side exclusively using I frames. Varying complexities of motion can occur within a sequence, which can have a strong impact on the quality of the generated frames. In order to address this challenge, we propose to let the encoder dynamically adjust the group of pictures (GOP) structure. This adjustment is performed based on the quality of predicted S frames. The achieved performance of the proposed method suggests that entirely skipping coding and instead synthesizing frames is promising and should be considered for future developments of learning based video codecs.
Joint Exploration Model (JEM) studies the next generation video coding standard potential. It demonstrates over 30% performance gain beyond HEVC. This paper provides tool-on and tool-off performance test results for 24 methods included into JEM. Overlap in the functionalities of those tools is discussed. Potential problems for mobile platform implementation are listed. Suggestion on standard development principals and tools selection for next generation video coding standard are made. Paper is intended to assist high quality Call for Proposal responses preparation.
Advances in display technologies both for head mounted devices and television panels demand resolution increase beyond 4K for source signal in virtual reality video streaming applications. This poses a problem of content delivery trough a bandwidth limited distribution networks. Considering a fact that source signal covers entire surrounding space investigation reviled that compression efficiency may fluctuate 40% in average depending on origin selection at the conversion stage from 3D space to 2D projection. Based on these knowledge the origin selection algorithm for video compression applications has been proposed. Using discontinuity entropy minimization function projection origin rotation may be defined to provide optimal compression results. Outcome of this research may be applied across various video compression solutions for omnidirectional content.
KEYWORDS: High dynamic range imaging, Visualization, Visual analytics, Statistical analysis, Computer programming, Video coding, Video, Quantization, Video compression, Visual compression
In this paper, the visual quality of different solutions for high dynamic range (HDR) compression using MPEG test contents is analyzed. We also simulate the method for an efficient HDR compression which is based on statistical property of the signal. The method is compliant with HEVC specification and also easily compatible with other alternative methods which might require HEVC specification changes. It was subjectively tested on commercial TVs and compared with alternative solutions for HDR coding. Subjective visual quality tests were performed using SUHD TVs model which is SAMSUNG JS9500 with maximum luminance up to 1000nit in test. The solution that is based on statistical property shows not only improvement of objective performance but improvement of visual quality compared to other HDR solutions, while it is compatible with HEVC specification.
In this paper, several coding tools are evaluated on top of the HEVC version 1. Among them there are straightforward extension of HEVC coding tools (such as Coding Unit size enlarging, fine granularity of Intra prediction angles) and algorithms that have been studied during HEVC development (such as secondary transform, multi-hypothesis CABAC, multi-parameter Intra prediction, bidirectional optical flow). Most of them improve performance of Intra coding. Minor adjustment to the final version of HEVC standard was done for efficient harmonization of the proposed coding tools with HEVC. Performance improvement observed from investigated tools is up to 7,1%, 9,9%, 4,5% and 5,7% in all-intra, random access, low-delay B and low-delay P test scenario (using HEVC common test conditions).
High Efficiency Video Coding (HEVC) draft standard has a challenging goal to improve coding efficiency twice
compare to H.264/AVC. Many aspects of the traditional hybrid coding framework were improved during new standard
development. Motion compensated prediction, in particular the interpolation filter, is one area that was improved
significantly over H.264/AVC. This paper presents the details of the interpolation filter design of the draft HEVC
standard. The coding efficiency improvements over H.264/AVC interpolation filter is studied and experimental results
are presented, which show a 4.0% average bitrate reduction for Luma component and 11.3% average bitrate reduction
for Chroma component. The coding efficiency gains are significant for some video sequences and can reach up 21.7%.
This paper proposes a new 32-point fast discrete cosine transform (DCT) algorithm based on the Loeffler's 16-point transform. Fast integer realizations of 16-point and 32-point transforms are also provided based on the proposed transform. For the recent development of High Efficiency Video Coding (HEVC), simplified quanti-zation and de-quantization process are proposed. Three different forms of implementation with the essentially same performance, namely matrix multiplication, partial butterfly, and full factorization can be chosen accord-ing to the given platform. In terms of the number of multiplications required for the realization, our proposed full-factorization is 3~4 times faster than a partial butterfly, and about 10 times faster than direct matrix multiplication.
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