Joint Decision Tree and Visual Feature Rate Control Optimization for VVC UHD Coding

Mingliang ZHOU; Xuekai WEI; Weijia JIA; Sam KWONG

doi:10.1109/TIP.2022.3224876

Joint Decision Tree and Visual Feature Rate Control Optimization for VVC UHD Coding

Mingliang ZHOU, Xuekai WEI, Weijia JIA, Sam KWONG

Research output: Journal Publications › Journal Article (refereed) › peer-review

17 Citations (Scopus)

Abstract

In this paper, a joint decision tree and visual feature optimization rate control scheme for ultrahigh-definition (UHD) versatile video coding (VVC) is proposed. First, we design a new rate-distortion (R-D) model for UHD videos, and we establish a decision-tree-based multiclass classification scheme to improve the prediction accuracy of the R-D model by fully considering visual features. Second, based on the proposed R-D model, the globally optimal solution is obtained through convex optimization. Finally, we embed our algorithm into the latest VVC reference software, VTM 10.2. According to our experimental results, compared with the latest algorithm in VTM 10.2 and other state-of-the-art algorithms, our method can achieve significant bit rate reductions while maintaining a given peak signal-to-noise ratio (PSNR) or structural similarity index measure (SSIM).

Original language	English
Pages (from-to)	219-234
Number of pages	16
Journal	IEEE Transactions on Image Processing
Volume	32
Early online date	9 Dec 2022
DOIs	https://doi.org/10.1109/TIP.2022.3224876
Publication status	Published - 2023
Externally published	Yes

Bibliographical note

Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 62176027; in part by the General Program of the National Natural Science Foundation of Chongqing under Grant cstc2020jcyj-msxmX0790; in part by the Human Resources and Social Security Bureau Project of Chongqing under Grant cx2020073; in part by the Hong Kong GRF-RGC General Research Fund under Grant 11209819 and Grant CityU 9042816; in part by the Hong Kong GRF-RGC General Research Fund under Grant 11203820 and Grant CityU 9042598; in part by the Zhuhai Postdoctoral Research Fund under Grant 110321001; in part by the International Postdoctoral Exchange Fellowship Program; and in part by the Hong Kong Innovation and Technology Commission, InnoHK Project Centre for Intelligent Multidimensional Data Analysis (CIMDA).

Publisher Copyright:
© 1992-2012 IEEE.

Keywords

Decision tree
linear
rate control
UHD
visual feature
VVC

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10.1109/TIP.2022.3224876

Cite this

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title = "Joint Decision Tree and Visual Feature Rate Control Optimization for VVC UHD Coding",

abstract = "In this paper, a joint decision tree and visual feature optimization rate control scheme for ultrahigh-definition (UHD) versatile video coding (VVC) is proposed. First, we design a new rate-distortion (R-D) model for UHD videos, and we establish a decision-tree-based multiclass classification scheme to improve the prediction accuracy of the R-D model by fully considering visual features. Second, based on the proposed R-D model, the globally optimal solution is obtained through convex optimization. Finally, we embed our algorithm into the latest VVC reference software, VTM 10.2. According to our experimental results, compared with the latest algorithm in VTM 10.2 and other state-of-the-art algorithms, our method can achieve significant bit rate reductions while maintaining a given peak signal-to-noise ratio (PSNR) or structural similarity index measure (SSIM).",

keywords = "Decision tree, linear, rate control, UHD, visual feature, VVC",

author = "Mingliang ZHOU and Xuekai WEI and Weijia JIA and Sam KWONG",

note = "Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 62176027; in part by the General Program of the National Natural Science Foundation of Chongqing under Grant cstc2020jcyj-msxmX0790; in part by the Human Resources and Social Security Bureau Project of Chongqing under Grant cx2020073; in part by the Hong Kong GRF-RGC General Research Fund under Grant 11209819 and Grant CityU 9042816; in part by the Hong Kong GRF-RGC General Research Fund under Grant 11203820 and Grant CityU 9042598; in part by the Zhuhai Postdoctoral Research Fund under Grant 110321001; in part by the International Postdoctoral Exchange Fellowship Program; and in part by the Hong Kong Innovation and Technology Commission, InnoHK Project Centre for Intelligent Multidimensional Data Analysis (CIMDA). Publisher Copyright: {\textcopyright} 1992-2012 IEEE.",

year = "2023",

doi = "10.1109/TIP.2022.3224876",

language = "English",

volume = "32",

pages = "219--234",

journal = "IEEE Transactions on Image Processing",

issn = "1057-7149",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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N1 - Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 62176027; in part by the General Program of the National Natural Science Foundation of Chongqing under Grant cstc2020jcyj-msxmX0790; in part by the Human Resources and Social Security Bureau Project of Chongqing under Grant cx2020073; in part by the Hong Kong GRF-RGC General Research Fund under Grant 11209819 and Grant CityU 9042816; in part by the Hong Kong GRF-RGC General Research Fund under Grant 11203820 and Grant CityU 9042598; in part by the Zhuhai Postdoctoral Research Fund under Grant 110321001; in part by the International Postdoctoral Exchange Fellowship Program; and in part by the Hong Kong Innovation and Technology Commission, InnoHK Project Centre for Intelligent Multidimensional Data Analysis (CIMDA). Publisher Copyright: © 1992-2012 IEEE.

PY - 2023

Y1 - 2023

N2 - In this paper, a joint decision tree and visual feature optimization rate control scheme for ultrahigh-definition (UHD) versatile video coding (VVC) is proposed. First, we design a new rate-distortion (R-D) model for UHD videos, and we establish a decision-tree-based multiclass classification scheme to improve the prediction accuracy of the R-D model by fully considering visual features. Second, based on the proposed R-D model, the globally optimal solution is obtained through convex optimization. Finally, we embed our algorithm into the latest VVC reference software, VTM 10.2. According to our experimental results, compared with the latest algorithm in VTM 10.2 and other state-of-the-art algorithms, our method can achieve significant bit rate reductions while maintaining a given peak signal-to-noise ratio (PSNR) or structural similarity index measure (SSIM).

AB - In this paper, a joint decision tree and visual feature optimization rate control scheme for ultrahigh-definition (UHD) versatile video coding (VVC) is proposed. First, we design a new rate-distortion (R-D) model for UHD videos, and we establish a decision-tree-based multiclass classification scheme to improve the prediction accuracy of the R-D model by fully considering visual features. Second, based on the proposed R-D model, the globally optimal solution is obtained through convex optimization. Finally, we embed our algorithm into the latest VVC reference software, VTM 10.2. According to our experimental results, compared with the latest algorithm in VTM 10.2 and other state-of-the-art algorithms, our method can achieve significant bit rate reductions while maintaining a given peak signal-to-noise ratio (PSNR) or structural similarity index measure (SSIM).

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KW - rate control

KW - UHD

KW - visual feature

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DO - 10.1109/TIP.2022.3224876

M3 - Journal Article (refereed)

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SN - 1057-7149

VL - 32

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EP - 234

JO - IEEE Transactions on Image Processing

JF - IEEE Transactions on Image Processing

ER -

Joint Decision Tree and Visual Feature Rate Control Optimization for VVC UHD Coding

Abstract

Bibliographical note

Keywords

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Adaptive Dynamic Range Enhancement Oriented to High Dynamic Display (面向高動態顯示的自適應動態範圍增強)

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Joint Decision Tree and Visual Feature Rate Control Optimization for VVC UHD Coding

Abstract

Bibliographical note

Keywords

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Adaptive Dynamic Range Enhancement Oriented to High Dynamic Display (面向高動態顯示的自適應動態範圍增強)

Cite this