Fast Intra Coding Algorithm for HEVC Based on Decision Tree

Qin, Jia; Bai, Huihui; Zhang, Mengmeng; Zhao, Yao

doi:10.1587/transfun.e100.a.1274

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…Meanwhile, Grellert et al [28] and Zhang et al [29] also proposed SVM-based approaches which focused on features analysis. In addition, decision tree or data mining methods were also used to reduce the encoding complexity [30][31][32][33][34]. Furthermore, Fisher's linear discriminant analysis and the k-nearest neighbors classifier were employed in order to quickly decide on a CU partition [35], and Kim et al [36] proposed a joint online and offline Bayesian decision rule-based fast CU partition algorithm.…”

Section: Introductionmentioning

confidence: 99%

Bagged Tree and ResNet-Based Joint End-to-End Fast CTU Partition Decision Algorithm for Video Intra Coding

Peng

et al. 2022

Electronics

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Video coding standards, such as high-efficiency video coding (HEVC), versatile video coding (VVC), and AOMedia video 2 (AV2), achieve an optimal encoding performance by traversing all possible combinations of coding unit (CU) partition and selecting the combination with the minimum coding cost. It is still necessary to further reduce the encoding time of HEVC, because HEVC is one of the most widely used coding standards. In HEVC, the process of searching for the best performance is the source of most of the encoding complexity. To reduce the complexity of the coding block partition in HEVC, a new end-to-end fast algorithm is presented to aid the partition structure decisions of the coding tree unit (CTU) in intra coding. In the proposed method, the partition structure decision problem of a CTU is solved by a novel two-stage strategy. In the first stage, a bagged tree model is employed to predict the splitting of a CTU. In the second stage, the partition problem of a 32 × 32-sized CU is modeled as a 17-output classification task for the first time, so that it can be solved by a single prediction. To achieve a high prediction accuracy, a residual network (ResNet) with 34 layers is employed. Jointly using bagged tree and ResNet, the proposed fast CTU partition algorithm is able to generate the partition quad-tree structure of a CTU through an end-to-end prediction process, which abandons the traditional scheme of making multiple decisions at various depth levels. In addition, several datasets are used in this paper to lay the foundation for high prediction accuracy. Compared with the original HM16.7 encoder, the experimental results show that the proposed algorithm can reduce the encoding time by 60.29% on average, while the Bjøntegaard delta rate (BD-rate) loss is as low as 2.03%, which outperforms the results of most of the state-of-the-art approaches in the field of fast intra CU partition.

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Section: Introductionmentioning

confidence: 99%

Bagged Tree and ResNet-Based Joint End-to-End Fast CTU Partition Decision Algorithm for Video Intra Coding

Peng

et al. 2022

Electronics

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show abstract

“…Meanwhile, Grellert et al [14] and Zhang et al [15] also proposed SVM based approaches which focused on features analysis. Besides, decision tree or data mining methods were also used to reduce the encoding complexity [16,17,18,19,20]. Also, Fisher's linear discriminant analysis and the k-nearest neighbors classifier were employed in order to fastly decide a CU partition [21], and Kim et al [22] proposed a joint online and offline Bayesian decision rule based fast CU partition algorithm.…”

Section: Introductionmentioning

confidence: 99%

Bagged Tree And ResNet Based Joint End-to-End Fast CTU Partition Decision Algorithm For Video Intra Coding

Peng

et al. 2021

Preprint

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In the development of video coding standards, advanced ones have greatly improved the bit rate compared with those of previous generation, but also brought a huge increase in coding complexity. Coding standards, such as high efficiency video coding (HEVC), versatile video coding (VVC) and AOMedia video 2 (AV2), get the optimal encoding performance by traversing all possible combinations of coding unit (CU) partition and selecting the combination with minimum coding cost. This process of searching for the best makes up a large part of encoding complexity. To reduce the complexity of coding block partition for many video coding standards, this paper proposes an end-to-end fast algorithm for partition structure decision of coding tree unit (CTU) in intra coding. It can be extended to various coding standards with fine tuning, and is applied to the intra coding of HEVC reference software HM16.7 as an example. In the proposed method, the splitting decision of a CTU is made by a well designed bagged tree model firstly. Then, the partition problem of a 32×32 sized CU is modeled as a 17-output classification task and solved by a well trained residual network (ResNet). Jointly using bagged tree and ResNet, the proposed fast CTU partition algorithm is able to generate the partition quad-tree structure of a CTU through an end-to-end prediction process, instead of multiple decision making procedures at depth level. Besides, several effective and representative datasets are also conducted in this paper to lay the foundation of high prediction accuracy. Compared with the original HM16.7 encoder, experimental results show that the proposed algorithm can reduce the encoding time by 59.79% on average, while the BD-rate loss is as less as 2.02%, which outperforms the results of most of state-of-the-art approaches in the fast intra CU partition area.

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Cost Sensitive Learning Based HEVC Screen Content Intra Coding for Mobile Devices

Zhu

2020

Mobile Netw Appl

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Fast Intra Coding Algorithm for HEVC Based on Decision Tree

Cited by 5 publications

References 9 publications

Bagged Tree and ResNet-Based Joint End-to-End Fast CTU Partition Decision Algorithm for Video Intra Coding

Bagged Tree and ResNet-Based Joint End-to-End Fast CTU Partition Decision Algorithm for Video Intra Coding

Bagged Tree And ResNet Based Joint End-to-End Fast CTU Partition Decision Algorithm For Video Intra Coding

Cost Sensitive Learning Based HEVC Screen Content Intra Coding for Mobile Devices

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