Low-Complexity Multiple Transform Selection Combining Multi-Type Tree Partition Algorithm for Versatile Video Coding

He, Liqiang; Xiong, Shengwu; Yang, Ruoyu; He, Xiufeng; Chen, Honggang

doi:10.3390/s22155523

Cited by 8 publications

(4 citation statements)

References 36 publications

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“…The comparison shows that in standard VTM our method processes 17.84% of the blocks, our method can perform even better if the video is of high resolution, which in another way illustrates our better performance in high resolution sequences. We can verify the effectiveness of our method through many papers [29], [30], [31], [32], [33], [34], [35], [36], [37].…”

Section: Performance Evaluationmentioning

confidence: 77%

Fast CU Partitioning Algorithm for VVC Based on Multi-Stage Framework and Binary Subnets

et al. 2023

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VVC is the latest video compression technology available, and while the coding efficiency has improved significantly over the previous generation of standards, it has also led to a dramatic increase in coding complexity. As VVC uses a QTMT division structure, the more flexible division structure also allows for a significant increase in coding time. We have built a multi-stage network framework to solve the above problem by dividing the CU into different stages according to the size of the blocks. The desired features are extracted by dynamically adjusting to the size of the input CU. Secondly, we construct a binary classification subnet to perform the classification task at each stage and can determine the QT and MT division decisions. Finally, the resulting experimental results can demonstrate that our novel two-threshold decision scheme can achieve a balance between RD performance and TS. Our method succeeds in reducing the coding time by 49.08% to 52.56%, while the complexity of the negligible BD-BR increases by only 1.10% to 1.42%.INDEX TERMS Versatile video coding, intra coding, fast coding algorithm, CNN, deep learning.

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Section: Performance Evaluationmentioning

confidence: 77%

Fast CU Partitioning Algorithm for VVC Based on Multi-Stage Framework and Binary Subnets

et al. 2023

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“…However, a few studies focus on fast algorithms for interprediction. In [35], a low-complexity method combining multi-type tree partition was proposed to reduce the computation of multiple transform selection. In [36], Ren et al proposed an advanced fast interprediction method based on edge detection.…”

Section: Related Workmentioning

confidence: 99%

An Adjacency Encoding Information-Based Fast Affine Motion Estimation Method for Versatile Video Coding

et al. 2022

Electronics

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Versatile video coding (VVC), a new generation video coding standard, achieves significant improvements over high efficiency video coding (HEVC) due to its added advanced coding tools. Despite the fact that affine motion estimation adopted in VVC takes into account the translational, rotational, and scaling motions of the object to improve the accuracy of interprediction, this technique adds a high computational complexity, making VVC unsuitable for use in real-time applications. To address this issue, an adjacency encoding information-based fast affine motion estimation method for VVC is proposed in this paper. First, this paper counts the probability of using the affine mode in interprediction. Then we analyze the trade-off between computational complexity and performance improvement based on statistical information. Finally, by exploring the mutual exclusivity between skip and affine modes, an enhanced method is proposed to reduce interprediction complexity. Experimental results show that compared with the VVC, the proposed low-complexity method achieves 10.11% total encoding time reduction and 40.85% time saving of affine motion estimation with a 0.16% Bjøontegaard delta bitrate (BDBR) increase.

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“…On the whole, it excels beyond any prior video coding standards in terms of coding performance. Currently, several studies have improved almost all modules in VVC by introducing new coding tools [6][7][8][9][10][11][12], including block partition, intra/inter prediction, transform and rate control, etc., to further optimize the coding performance efficiency. Specifically, in order to accommodate high-resolution videos, VVC implements the larger coding tree unit (CTU) of 128×128 and flexible partition methods like binary tree and triple tree partition [6].…”

Section: Introductionmentioning

confidence: 99%

Neural network-based cross-channel chroma prediction for versatile video coding

Liang,

Zhang

2024

J Supercomput

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Despite linear models being introduced in the latest Versatile Video Coding (VVC) standard to exploit the correlation among luma and chroma channels for removing redundancy, these models cannot take into account the nonlinearity of components, resulting in degraded intra prediction precision. In this paper, a neural network-based method is proposed for cross-channel chroma intra prediction to enhance the coding efficiency. Specifically, the neighboring reference and co-located samples are separately input into the proposed network to exploit spatial and cross-channel correlations fully. Furthermore, in order to acquire a more compact representation of residual signals, a transform-based loss is employed to enhance the effectiveness of the compression. The proposed method is integrated into VVC, competing with the intrinsic chroma prediction regarding rate-distortion optimization to enhance coding performance further. The extensive experimental results demonstrate the superiority of the proposed method over the VVC test model (VTM) 7.0, achieving average bitrate savings of 2.68% and 2.11% for U and V chroma components, respectively.

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Low-Complexity Multiple Transform Selection Combining Multi-Type Tree Partition Algorithm for Versatile Video Coding

Cited by 8 publications

References 36 publications

Fast CU Partitioning Algorithm for VVC Based on Multi-Stage Framework and Binary Subnets

Fast CU Partitioning Algorithm for VVC Based on Multi-Stage Framework and Binary Subnets

An Adjacency Encoding Information-Based Fast Affine Motion Estimation Method for Versatile Video Coding

Neural network-based cross-channel chroma prediction for versatile video coding

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