Study On Coding Tools Beyond AV1

Zhao, Xin; Zhao, Liang; Krishnan, Madhu; Du, Yixin; Liu, Shan; Mukherjee, Debargha; Xu, Yaowu; Grange, Adrian

doi:10.1109/icme51207.2021.9428244

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…The major objective is to individually optimize the compression efficiency of each coding module. The local optimization of each module extensively promoted the ratedistortion (R-D) performances of video codecs, resulting in different families of video compression standards, such as MPEG [5], [6], H.26x [7]- [9], AVS [10]- [12], and AOM [13], [14] series standards. Basically, these frameworks rely on the fine-grained tuning of the rule-based methods for marginal coding efficiency enhancement.…”

Section: Introductionmentioning

confidence: 99%

MPAI-EEV: Standardization Efforts of Artificial Intelligence Based End-to-End Video Coding

Jia,

Ye,

Dong

et al. 2024

IEEE Trans. Circuits Syst. Video Technol.

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The rapid advancement of artificial intelligence (AI) technology has led to the prioritization of standardizing the processing, coding, and transmission of video using neural networks. To address this priority area, the Moving Picture, Audio, and Data Coding by Artificial Intelligence (MPAI) group is developing a suite of standards called MPAI-EEV for "end-to-end optimized neural video coding." The aim of this AI-based video standard project is to compress the number of bits required to represent high-fidelity video data by utilizing data-trained neural coding technologies. This approach is not constrained by how data coding has traditionally been applied in the context of a hybrid framework. This paper presents an overview of recent and ongoing standardization efforts in this area and highlights the key technologies and design philosophy of EEV. It also provides a comparison and report on some primary efforts such as the coding efficiency of the reference model. Additionally, it discusses emerging activities such as learned Unmanned-Aerial-Vehicles (UAVs) video coding which are currently planned, under development, or in the exploration phase. With a focus on UAV video signals, this paper addresses the current status of these preliminary efforts. It also indicates development timelines, summarizes the main technical details, and provides pointers to further points of reference. The exploration experiment shows that the EEV model performs better than the state-of-theart video coding standard H.266/VVC in terms of perceptual evaluation metric.

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Section: Introductionmentioning

confidence: 99%

MPAI-EEV: Standardization Efforts of Artificial Intelligence Based End-to-End Video Coding

Jia,

Ye,

Dong

et al. 2024

IEEE Trans. Circuits Syst. Video Technol.

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“…Recently, a lot of researches have been conducted to explore the video coding technologies beyond AV1 [6][7][8][9][10][11][12][13]. Semi-Decoupled Partitioning was proposed to provide more flexibility on switching between dependent and independent partitioning patterns for luma and chroma [7].…”

Section: Introductionmentioning

confidence: 99%

Advanced Motion Vector Difference Coding Beyond AV1

Zhao

Liu

2022

2022 IEEE International Conference on Image Processing (ICIP)

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In AV1, for inter coded blocks with compound reference mode, motion vector differences (MVDs) are signaled for reference frame list 0 or list 1 separately with the same MVD precision regardless of the motion vector magnitude. In this paper, two advanced MVD coding methods are proposed. Firstly, to reduce the overhead for signaling MVD, the precision of the MVD is implicitly determined based on the associated MV class and MVD magnitude. Secondly, a new inter prediction mode is added to explore the correlation of MVDs between two reference frames, wherein one joint MVD is signaled for two reference frames. Experimental results demonstrate that, in the random-access common test condition luma coding gains of around 1.1% in terms of BDrate can be achieved on top of a recent release of AOMedia Video Model (AVM).

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