An Adaptive CU Size Decision Algorithm for HEVC Intra Prediction Based on Complexity Classification Using Machine Learning

Liu, Xingang; Li, Yayong; Liu, Deyuan; Wang, Peicheng; Yang, Laurence T.

doi:10.1109/tcsvt.2017.2777903

Cited by 84 publications

(48 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All of these methods are implemented on the top of HM 16.6. Encoder controls follow the common HM test conditions with All Intra Main configuration [33]. In detail, the CTB indicating the maximum CU size has a fixed size of 64 × 64 pixels.…”

Section: Resultsmentioning

confidence: 99%

“…Liquan Shen et al [32] proposed an early CU size decision algorithm based on the texture homogeneity. Xingang Liu et al [33] also introduced a fast CU size decision algorithm that utilizes a CU complexity classifier built by using machine learning technology. H. Zhang et al [34] proposed a fast mode decision that was made by priority classification according to coding information of spatial and temporal neighboring PUs.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Relative SATD-based Minimum Risk Bayesian Framework for Fast Intra Decision of HEVC

2019

KSII TIIS

View full text Add to dashboard Cite

High Efficiency Video Coding (HEVC) enables significantly improved compression performance relative to existing standards. However, the advance also requires high computational complexity. To accelerate the intra prediction mode decision, a minimum risk Bayesian classification framework is introduced. The classifier selects a small number of candidate modes to be evaluated by a rate-distortion optimization process using the sum of absolute Hadamard transformed difference (SATD). Moreover, the proposed method provides a loss factor that is a good trade-off model between computational complexity and coding efficiency. Experimental results show that the proposed method achieves a 31.54% average reduction in the encoding run time with a negligible coding loss of 0.93% BD-rate relative to HEVC test model 16.6 for the Intra_Main common test condition.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Relative SATD-based Minimum Risk Bayesian Framework for Fast Intra Decision of HEVC

2019

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…The above studies have not taken into consideration the relationship between the MV-HEVC bit rate control model and related coding performance. Most of the studies are engaged in research on rate control for HEVC or MVC [19][20][21][22][23][24][25][26][27]. There have been many studies on rate control for HEVC based on scene switching [28,29], but most of the rate control algorithms for HEVC are concentrated in the single-channel video coding standard, which is not applicable to multiview video coding.…”

Section: Discussion Of the Pros And Cons Of The Various Approachesmentioning

confidence: 99%

A Novel Rate Control Algorithm Based on ρ Model for Multiview High Efficiency Video Coding

Yan

Zhang

et al. 2020

Electronics

View full text Add to dashboard Cite

Most existing rate control algorithms are based on the rate-quantization (R-Q) model. However, with video coding schemes becoming more flexible, it is very difficult to accurately model the R-Q relationship. Therefore, in this study we propose a novel ρ domain rate control algorithm for multiview high efficiency video coding (MV-HEVC). Firstly, in order to further improve the efficiency of MV-HEVC, this paper uses our previous research algorithm to optimize the MV-HEVC prediction structure. Then, we established the ρ domain rate control model based on multi-objective optimization. Finally, it used image similarity to analyze the correlation between viewpoints, using encoded information and frame complexity to proceed in bit allocation and bit rate control of the inter-view, frame lay, and base unit. The experimental simulation results show that the algorithm can simultaneously maintain high coding efficiency, where the average error of the actual bit rate and the target bit rate is only 0.9%.

show abstract

“…The adjustment is carried out over the inter and intra frames of the quantization parameters for better optimization performance. Liu et al [53] have presented a study where an algorithm is formulated for adaptive decision making associated with the intra-prediction mode. The paper basically addresses the problem associated with the iterative operation of HEVC leading to computational complexity.…”

Section: B Typical Machine Learning Approachmentioning

confidence: 99%

Insights on Video Compression Strategies using Machine Learning

Rao

2019

IJEAT

View full text Add to dashboard Cite

With the rising advancement of the multimedia technology, video compression is becoming a challenging problem. Although, there is availability of various standard compression algorithms, yet robust compression performance is yet to be seen in existing compression techniques. This paper also highlights that machine learning plays a significant contributory role in improving the performance of the video compression. Therefore, this manuscript offers a technical insight about the performance of existing video compression technique using machine learning approach. The contribution of this paper is its findings which states that machine learning approach do have significant advantage but the advantageous features are limited by the inherent and unsolved research problem. The core findings of this paper are basically to highlight the strength and limitations of existing methods as well as to highlight the research gap in terms of open-end research problems which requires immediate attention.

show abstract

An Adaptive CU Size Decision Algorithm for HEVC Intra Prediction Based on Complexity Classification Using Machine Learning

Cited by 84 publications

References 28 publications

Relative SATD-based Minimum Risk Bayesian Framework for Fast Intra Decision of HEVC

Relative SATD-based Minimum Risk Bayesian Framework for Fast Intra Decision of HEVC

A Novel Rate Control Algorithm Based on ρ Model for Multiview High Efficiency Video Coding

Insights on Video Compression Strategies using Machine Learning

Contact Info

Product

Resources

About