A GCN-based fast CU partition method of intra-mode VVC

Zhang, Saiping; Feng, Shixuan; Chen, Jingwu; Zhou, Chunjie; Yang, Fuzheng

doi:10.1016/j.jvcir.2022.103621

Cited by 9 publications

(3 citation statements)

References 13 publications

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“…Therefore, a more appropriate comparison would be to compare the proposed approach with other state-of-the-art works embedded on the VVenC encoder and that have been optimized for similar use cases. The results in Table 12 showed that, in terms of encoding efficiency, the best results measured by the BDBR metric were achieved by our proposed approach using the Slower preset followed by the approach presented by Saldanha in [13] and Zhang in [31] with values of 1.42% and 1.52%, respectively, while Guoqing in [33] achieved reached a higher degradation in BDBR of 2.71%. In terms of ETR, our proposed preset outperforms the other cited works with an average of 73.07%, followed by the solutions of Guoqing [33], Zhang [31], Saldanha [13], and finally our proposed solution with the Slower preset.…”

Section: Comparison With the State-of-the-art Techniquesmentioning

confidence: 96%

“…A fast CU partitioning approach for VVC intra coding was also proposed by Zhang et al [31], where they designed a Global Convolutional Network (GCN) module with a large kernel size convolution, which is able to effectively capture the CU global information and predict the partition mode over the QTMTT structure. With this technique implemented in VTM 10.0, they achieved an encoding time reduction ranging between 51.06% and 61.15% with an increase of the BDBR varying from 0.84% to 1.52%.…”

Section: Related Workmentioning

confidence: 99%

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Machine Learning Based Fast QTMTT Partitioning Strategy for VVenC Encoder in Intra Coding

et al. 2023

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The newest video compression standard, Versatile Video Coding (VVC), was finalized in July 2020 by the Joint Video Experts Team (JVET). Its main goal is to reduce the bitrate by 50% over its predecessor video coding standard, the High Efficiency Video Coding (HEVC). Due to the new advanced tools and features included in VVC, it actually provides high coding performances—for instance, the Quad Tree with nested Multi-Type Tree (QTMTT) involved in the partitioning block. Furthermore, VVC introduces various techniques that allow for superior performance compared to HEVC, but with an increase in the computational complexity. To tackle this complexity, a fast Coding Unit partition algorithm based on machine learning for the intra configuration in VVC is proposed in this work. The proposed algorithm is formed by five binary Light Gradient Boosting Machine (LightGBM) classifiers, which can directly predict the most probable split mode for each coding unit without passing through the exhaustive process known as Rate Distortion Optimization (RDO). These LightGBM classifiers were offline trained on a large dataset; then, they were embedded on the optimized implementation of VVC known as VVenC. The results of our experiment show that our proposed approach has good trade-offs in terms of time-saving and coding efficiency. Depending on the preset chosen, our approach achieves an average time savings of 30.21% to 82.46% compared to the VVenC encoder anchor, and a Bjøntegaard Delta Bitrate (BDBR) increase of 0.67% to 3.01%, respectively.

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Section: Comparison With the State-of-the-art Techniquesmentioning

confidence: 96%

Section: Related Workmentioning

confidence: 99%

Machine Learning Based Fast QTMTT Partitioning Strategy for VVenC Encoder in Intra Coding

et al. 2023

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“…And using probabilistic model and spatio-temporal coherence based to select the candidate split mode with the best encoding depth. Literature [27] establishes a pre-decision dictionary based on statistical theory and combines it with a convolutional neural network model based on adaptive adjustment of pooling layer size to construct a size-adaptive convolutional neural network to make decisions on CU size split, literature [28] proposes a global convolutional network based intra-frame mode that The global information of CU is captured by setting larger convolutional kernels so as to achieve pure deficit partition mode prediction in a quadtree plus multi-type tree structure and discard the partition modes with lower probability to reduce the computational complexity by ranking them according to the prediction probability. In the literature [29], a CNN model, which is lightweight, has been created.…”

Section: Background and Related Workmentioning

confidence: 99%

Fast CU Division Pattern Decision Based on the Combination of Spatio-Temporal Information

2023

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In order to satisfy the growing need for high-quality video, VVC comes with more efficient coding performance. According to statistical analysis, the level of coding complexity in VVC is tenfold greater compared to that of HEVC, so it is our main goal to study that what methods can be employed to decrease the time complexity of VVC. CU split in intra-frame modes requires the split mode decision by RD loss calculation, and the process of coding makes it to calculate RD calculation for all possible mode combinations, which is an important area that brings complexity to video coding, so in order to achieve our goal. Initially, we introduce an optimal depth prediction algorithm for Coding Units (CUs) by leveraging temporal combination. This algorithm collects depth information of CUs to predict the coding depth of CU blocks. Additionally, we suggest a decision tree-based method for CU split mode decision. With this method, we can make a decision on the CU split mode within the obtained split depth, reducing the time complexity of coding. This decision is based on the predictions from the first algorithm. The results demonstrate that our algorithm achieves superior performance over state-of-the-art methods in terms of computational complexity and compression quality. Compared to the VVC reference software (VTM), our method saves an average of 53.92% in coding time and improves the BDBR by 1.74%. These findings suggest that our method is highly effective in improving both computational efficiency and compression quality.

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Fast QTMT Decision for H.266/VVC via Jointly Leveraging Neural Network and Machine Learning Models

Ding,

Lin,

Wang

et al. 2024

Communications in Computer and Information Science

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A GCN-based fast CU partition method of intra-mode VVC

Cited by 9 publications

References 13 publications

Machine Learning Based Fast QTMTT Partitioning Strategy for VVenC Encoder in Intra Coding

Machine Learning Based Fast QTMTT Partitioning Strategy for VVenC Encoder in Intra Coding

Fast CU Division Pattern Decision Based on the Combination of Spatio-Temporal Information

Fast QTMT Decision for H.266/VVC via Jointly Leveraging Neural Network and Machine Learning Models

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