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

Zhao, Jinchao; Zhang, Qiuwen

doi:10.1155/2022/7675749

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…The core of the decision lies in selecting the segmentation mode suitable for the current CU texture characteristics from the six available segmentation modes. In [33], a classical CU division decision termination framework is presented, as depicted in Figure 7a. The method uses coding information derived from the current CU depth for intra-frame prediction to decide whether to terminate the division early.…”

Section: Decision-tree-based Series Partitioning Decision Algorithmmentioning

confidence: 99%

Fast Decision-Tree-Based Series Partitioning and Mode Prediction Termination Algorithm for H.266/VVC

Li,

He,

Zhang

2024

Electronics

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With the advancement of network technology, multimedia videos have emerged as a crucial channel for individuals to access external information, owing to their realistic and intuitive effects. In the presence of high frame rate and high dynamic range videos, the coding efficiency of high-efficiency video coding (HEVC) falls short of meeting the storage and transmission demands of the video content. Therefore, versatile video coding (VVC) introduces a nested quadtree plus multi-type tree (QTMT) segmentation structure based on the HEVC standard, while also expanding the intra-prediction modes from 35 to 67. While the new technology introduced by VVC has enhanced compression performance, it concurrently introduces a higher level of computational complexity. To enhance coding efficiency and diminish computational complexity, this paper explores two key aspects: coding unit (CU) partition decision-making and intra-frame mode selection. Firstly, to address the flexible partitioning structure of QTMT, we propose a decision-tree-based series partitioning decision algorithm for partitioning decisions. Through concatenating the quadtree (QT) partition division decision with the multi-type tree (MT) division decision, a strategy is implemented to determine whether to skip the MT division decision based on texture characteristics. If the MT partition decision is used, four decision tree classifiers are used to judge different partition types. Secondly, for intra-frame mode selection, this paper proposes an ensemble-learning-based algorithm for mode prediction termination. Through the reordering of complete candidate modes and the assessment of prediction accuracy, the termination of redundant candidate modes is accomplished. Experimental results show that compared with the VVC test model (VTM), the algorithm proposed in this paper achieves an average time saving of 54.74%, while the BDBR only increases by 1.61%.

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Section: Decision-tree-based Series Partitioning Decision Algorithmmentioning

confidence: 99%

Fast Decision-Tree-Based Series Partitioning and Mode Prediction Termination Algorithm for H.266/VVC

Li,

He,

Zhang

2024

Electronics

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“…To enable the DAG-SVM model to be trained and applied to the new dataset to make accurate predictions, an offline training mode [32] is used here, with video sequences of different resolutions selected from the UHD video training set. These sequences have good diversity and cover various aspects.…”

Section: Fast Cu Partitioning Based On Dag-svmsmentioning

confidence: 99%

FSVM- and DAG-SVM-Based Fast CU-Partitioning Algorithm for VVC Intra-Coding

Wang

Zhang

2023

Symmetry

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H.266/VVC introduces the QTMT partitioning structure, building upon the foundation laid by H.265/HEVC, which makes the partitioning more diverse and flexible but also brings huge coding complexity. To better address the problem, we propose a fast CU decision algorithm based on FSVMs and DAG-SVMs to reduce encoding time. The algorithm divides the CU-partitioning process into two stages and symmetrically extracts some of the same CU features. Firstly, CU is input into the trained FSVM model, extracting the standard deviation, directional complexity, and content difference complexity of the CUs, and it uses these features to make a judgment on whether to terminate the partitioning early. Then, the determination of the partition type of CU is regarded as a multi-classification problem, and a DAG-SVM classifier is used to classify it. The extracted features serve as input to the classifier, which predicts the partition type of the CU and thereby prevents unnecessary partitioning. The results of the experiment indicate that compared with the reference software VTM10.0 anchoring algorithm, the algorithm can save 49.38%~58.04% of coding time, and BDBR only increases by 0.76%~1.37%. The video quality and encoding performance are guaranteed while the encoding complexity is effectively reduced.

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“…The significant advancement in communication and multimedia technology has led to the emergence of virtual reality (VR), ultra-high definition (UHD), and high dynamic range (HDR) videos. These innovations have greatly enhanced the visual experience of high-efficiency video coding (HEVC) [1]. This increasing introduced of video information leads to issues on storage and transmission [2].…”

Section: Introductionmentioning

confidence: 99%

“…International Journal of Intelligent Engineering and Systems, Vol.17, No. 1,2024 DOI: 10.22266/ijies2024.0229. 23 The quadtree of HEVC partitioning utilizes the search of brute force to Rate Distortion Optimization (RDO) cost measurement [11,12].…”

Section: Introductionmentioning

confidence: 99%

Coding Tree Unit Partition Prediction Algorithm Utilizing Residual AttentionBased Long Short-Term Memory for Video Intra Coding

2024

IJIES

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High-efficiency video coding (HEVC) stands as one of the most extensively employed video coding standards, with the primary encoding challenge revolving around the searching process. To minimize coding complexity of HEVC block partition, a new algorithm is proposed for supporting the partition architecture of coding tree unit (CTU) in intra-coding. In this research, problem of partition architecture decisions of CTU is resolved through two stages. In first phase, the bagged tree method is evaluated for predicting CTU splitting, and in the second phase, the partition issue of 32 × 32 size CU is given as 17 outcome classification. To attain a huge accuracy of prediction, a residual attention-based long short-term memory (LSTM) with parametric rectified linear unit (AT-LSTM-PReLU) is proposed. The proposed method produces a structure of partition quad-tree of CTU that makes multiple decisions at variant depth levels. The performance of proposed method is estimated with a dataset gathered from the joint collaborative team and video coding (JCT-VC). The proposed approach achieved a remarkable bit rate improvement of 3.72%, a significant ∆T enhancement of 97.28%, and minimized video quality degradation by just -0.05dB. These results outperform other deep learning methods like convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM).

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

Cited by 6 publications

References 30 publications

Fast Decision-Tree-Based Series Partitioning and Mode Prediction Termination Algorithm for H.266/VVC

Fast Decision-Tree-Based Series Partitioning and Mode Prediction Termination Algorithm for H.266/VVC

FSVM- and DAG-SVM-Based Fast CU-Partitioning Algorithm for VVC Intra-Coding

Coding Tree Unit Partition Prediction Algorithm Utilizing Residual AttentionBased Long Short-Term Memory for Video Intra Coding

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