Recent Advances in Intra Prediction for the Emerging H.266/VVC Video Coding Standard

Filippov, Alexey; Rufitskiy, Vasily

doi:10.1109/sibircon48586.2019.8958416

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…Although the VVC coding structure is very similar to its predecessor HEVC, many new technologies have been added on this basis: QTMT block partition structure, 67 intra-frame prediction modes, intra-frame cross-component linear (CCLM) prediction, adaptive multi-core transform (AMT) technology, matrix weighted intra prediction technology (MIP), motion vector derivation FRUC technology based on pattern matching, optical flow–based bidirectional motion (BIO) compensation, affine (Affine) motion prediction technology, and SubPU-level motion vector prediction technology, relying on scalar quantization technique (DSQP), low-frequency nonseparable transform technique (LFNST), intra-frame subdivision (ISP), etc. [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

A Fast Decision Algorithm for VVC Intra-Coding Based on Texture Feature and Machine Learning

Zhao

Zhang

2022

Computational Intelligence and Neuroscience

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Due to the development and application of information technology, a series of modern information technologies represented by 5G, big data, and artificial intelligence are changing rapidly, and people’s requirements for video coding standards have become higher. In the High-Efficiency Video Coding (HEVC) standard, the coding block division is not flexible enough, and the prediction mode is not detailed enough. A new generation of Versatile Video Coding (VVC) standards was born. VVC inherits the hybrid coding framework adopted by HEVC, improves the original technology of each module, introduces a series of new coding technologies, and builds on this greatly improving the coding efficiency. Compared with HEVC, the block division structure of VVC has undergone great changes, retaining the quad-tree (QT) division method and increasing the multi-type tree (MTT) division method, which brings high coding complexity. To reduce the computational complexity of VVC coding block division, a fast decision algorithm for VVC intra-frame coding based on texture characteristics and machine learning is proposed. First, we analyze the characteristics of the CU partition structure decision and then use the texture complexity of the CU partition structure decision to terminate the CU partition process early; for CUs that do not meet the early termination of the partition, use the global sample information, local sample information, and context information. The three-category feature-trained tandem classifier framework predicts the division type of CU. The experimental results show that in the full intra mode, compared with the existing VTM10.0, the encoding output bit rate is increased by 1.36%, and the encoding time is saved by 52.63%.

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Section: Introductionmentioning

confidence: 99%

A Fast Decision Algorithm for VVC Intra-Coding Based on Texture Feature and Machine Learning

Zhao

Zhang

2022

Computational Intelligence and Neuroscience

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“…The computational complexity of the VVC averagely increased by 1020% [2]. According to the report [3], the complexity of intra video coding in VVC is more prominent. Hence, it is significant to design fast intra coding algorithm for 360-degree video.…”

Section: Introductionmentioning

confidence: 99%

Fast intra partition and mode prediction for equirectangular projection 360‐degree video coding

Shu

Peng

Jiang

et al. 2022

IET Image Processing

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360‐degree videos have drawn great attention from both the academia and the industry. For transmission and storage of 360‐degree video, the joint video exploration team proposed the Versatile Video Coding (VVC) standard. VVC can significantly reduce the bitrate while maintaining the same subjective visual quality compared to the preceding high efficiency video coding. However, the computational complexity of VVC is extremely high which hinders the interactive applications. This paper proposes a fast intra partition method and mode prediction algorithm for equirectangular projection 360‐degree video coding. First, a latitude‐based preprocessing is introduced to early terminate the Coding Unit (CU) partition in the polar region. Second, the support vector machine is used to predict the CU partition type. Third, the fast intra mode search method accelerates the intra mode prediction. Experimental results show that the proposed algorithm can significantly obtain an average time reduction rate of 60.40% and a Bjontegaard delta rate increase of 1.96%.

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“…H.266/VVC introduces many new compression tools to enhance the compression efficiency. For the intra prediction, H.266/VVC introduces several new mechanisms [25], such as Matrix weighted Intra Prediction (MIP) [26], Multiple reference line (MRL) [27], and Crosscomponent linear model (CCLM) [28], which increases the flexibility of mode selection, thus potentially suiting information hiding. Further, H.266/VVC designs a novel adaptive transform selection mechanism, named Multiple Transform Selection (MTS) [29], to switch between horizontal and vertical residual transforms based on the hybrid DCT+DST scheme and make the compression more effective.…”

Section: Introductionmentioning

confidence: 99%

A Novel Information Hiding Method for H.266/VVC Based on Selections of Luminance Transform and Chrominance Prediction Modes

Liu

Shi

et al. 2021

2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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This paper proposes a novel information hiding method designed for H.266/Versatile Video Coding (VVC) compressed video streams. In this work, we explore two exclusive tools in H.266/VVC standard, named Multiple Transform Selection (MTS) and Cross-component linear model (CCLM), to embed information. These two tools are utilized to preserve high video reconstruction quality and compression efficiency as well as enhance embedding capacity. In specific, MTS is for embedding information into luminance blocks by modifying the selections of coding transforms. Comparing with other tools, MTS has less significant impact on compression quality and efficiency. In addition, CCLM is further used to embed information into chrominance blocks to further enlarge the embedding capacity with little impact on the other two metrics. To our best knowledge, it is the first information hiding method exclusively designed for H.266/VVC. Experimental results show that our proposed information hiding method ensures high embedding capacity, remarkable video reconstruction quality and insignificant impact on compression efficiency, which achieves a better overall performances comparing to existing methods for compressed video.

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Recent Advances in Intra Prediction for the Emerging H.266/VVC Video Coding Standard

Cited by 5 publications

References 4 publications

A Fast Decision Algorithm for VVC Intra-Coding Based on Texture Feature and Machine Learning

A Fast Decision Algorithm for VVC Intra-Coding Based on Texture Feature and Machine Learning

Fast intra partition and mode prediction for equirectangular projection 360‐degree video coding

A Novel Information Hiding Method for H.266/VVC Based on Selections of Luminance Transform and Chrominance Prediction Modes

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