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Associations between MSE and SSIM as cost functions in linear decomposition with application to bit allocation for sparse coding

文献类型：期刊论文

作者Wang, Jianji2,3; Chen, Pei2,3; Zheng, Nanning2,3; Chen, Badong2,3; Principe, Jose C.4; Wang, Fei-Yue1,5,6
刊名NEUROCOMPUTING
出版日期2021-01-21
卷号422页码:139-149
关键词Mean Square Error (MSE) Structural Similarity (SSIM) index Bit allocation Sparse coding Image contrast
ISSN号0925-2312
DOI10.1016/j.neucom.2020.10.018
通讯作者Wang, Jianji(wangjianji@xjtu.edu.cn) ; Wang, Fei-Yue(feiyue.wang@ia.ac.cn)
英文摘要The traditional image quality assessments, such as the mean squared error (MSE), the signal-to-noise ratio (SNR), and the Peak signal-to-noise ratio (PSNR), are all based on the absolute error of images. Structural similarity (SSIM) index is another important image quality assessment which has been shown to be more effective in the human vision system (HVS). Although there are many essential differences between MSE and SSIM, some important associations exist between them. In this paper, the associations between MSE and SSIM as cost functions in linear decomposition are investigated. Based on the associ-ations, a bit-allocation algorithm for sparse coding is proposed by considering both the reconstructed image quality and the reconstructed image contrast. In the proposed algorithm, the space occupied by a linear coefficient of a basis in sparse coding is reduced to only 9 to 10 bits, in which 1 bit is used to save the sign of linear coefficient, 3 bits are used to save the number of powers of 10 in scientific notation, and only 5 to 6 bits are used to save the significance digits. The experimental results show that the proposed bit-allocation algorithm for sparse coding can maintain both the image quality and the image contrast well. (c) 2020 Elsevier B.V. All rights reserved.
资助项目National Key Research and Development Program of China[2016YFB1000901] ; key project of Trico-Robot plan of NSFC[91748208] ; National Natural Science Foundation of China[91648208]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000590173600012
出版者ELSEVIER
资助机构National Key Research and Development Program of China ; key project of Trico-Robot plan of NSFC ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/41782]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Wang, Jianji; Wang, Fei-Yue
作者单位1.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China
2.Xi An Jiao Tong Univ, Coll Artificial Intelligence, Inst Artificial Intelligence & Robot, Xian 710049, Shaanxi, Peoples R China
3.Xi An Jiao Tong Univ, Coll Artificial Intelligence, Natl Engn Lab Visual Informat Proc & Applicat, Xian 710049, Shaanxi, Peoples R China
4.Univ Florida, Dept Elect & Comp Engn, Gainesville, FL 32611 USA
5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
6.Macau Univ Sci & Technol, Inst Syst Engn, Macau, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Jianji,Chen, Pei,Zheng, Nanning,et al. Associations between MSE and SSIM as cost functions in linear decomposition with application to bit allocation for sparse coding[J]. NEUROCOMPUTING,2021,422:139-149.
APA Wang, Jianji,Chen, Pei,Zheng, Nanning,Chen, Badong,Principe, Jose C.,&Wang, Fei-Yue.(2021).Associations between MSE and SSIM as cost functions in linear decomposition with application to bit allocation for sparse coding.NEUROCOMPUTING,422,139-149.
MLA Wang, Jianji,et al."Associations between MSE and SSIM as cost functions in linear decomposition with application to bit allocation for sparse coding".NEUROCOMPUTING 422(2021):139-149.

入库方式： OAI收割

来源：自动化研究所

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