基于内在生成机制的多尺度结构相似性图像质量评价

doi:10.11999/JEIT150616

Abstract
Figure/Table
References (22)
Related Citation (15)

Download: PDF (12611 KB)
Export: BibTeX | EndNote (RIS)

Abstract

In order to improve image information uncertainty measurement of the Multiple-scale Structural SIMilarity (MSSIM), a novel algorithm called iMSSIM based on internal generative mechanism is proposed, combining with Human Visual System (HVS). Firstly, internal generative mechanism based on the Piecewise AutoRegressive (PAR) model decomposes distorted image and the original image into two parts, the predicted part of image content using MSSIM algorithm assessment and image information uncertainty Part using PSNR assessment. Then, Mean Square Error is used as weight to combine the two scores to acquire the overall image quality assessmet results. Experiments performed on benchmark IQA databases demonstrate that the proposed algorithm not only has the best performance in different types of distortion, but also is better than the existing algorithms.

Key words： Image quality assessment Multi-scale Structural SIMilarity (MSSIM) Internal Generative Mechanism (IGM) Piecewise AutoRegressive (PAR) model

Received: 25 May 2015 Published: 19 November 2015

PACS:

TN919.8

Fund:

The Jiangsu Province Laboratory of Electrical and Automation Engineering for Coal Mining (2014KJZX05), The Perspective Research Foundation of Production Study and Research Alliance of Jiangsu Province (BY2014028-01), The Fundamental Research Foundation for the Central Universities (2014ZDPY16), The National Natural Science Foundation of China (51274202), The Natural Science Foundation of Jiangsu Province (BK201311240), The Fundamental Research Funds for the Central Universities (2013RC11)

Corresponding Authors: YANG Yufen E-mail: hk_yyf@163.com

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	SUN Yanjing
	YANG Yufen
	LIU Donglin
	SHI Wenjuan

Cite this article:

SUN Yanjing,YANG Yufen,LIU Donglin等. Multiple-scale Structural Similarity Image Quality Assessment Based on Internal Generative Mechanism[J]. JEIT, 2016, 38(1): 127-134.

URL:

http://jeit.ie.ac.cn/EN/ 10.11999/JEIT150616 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I1/127

[1]	ZHANG L, ZHANG D, MOU X, et al. FSIM: a feature similarity index for image quality assessment[J]. IEEE Transactions on Image Processing, 2011, 20(8): 2378-2386.
[2]	赵岩, 孟丽茹, 王世刚, 等. 符合人眼视觉感知特性的改进PSNR评价方法[J]. 吉林大学学报: 工学版, 2015, 45(1): 309-313.
	ZHAO Yan, MENG Liru, WANG Shigang, et al. Improved PSNR assessment method according with human visual perception characteristics[J]. Journal of Jilin University (Engineering and Technology Edition), 2015, 45(1): 309-313.
[3]	蒋刚毅, 黄大江, 王旭, 等. 图像质量评价方法研究进展[J]. 电子与信息学报, 2010, 32(1): 219-226. doi: 10.3724/SP.J. 1146.2009.00091.
	JIANG Gangyi, HUANG Dajiang, WANG Xu, et al. Research advancement on image quality assessment methods[J]. Journal of Electronics & Information Technology, 2010, 32(1): 219-226. doi: 10.3724/SP.J.1146. 2009. 00091.
[4]	DAMERA-VENKATA N, KITE T D, GEISLER W S, et al. Image quality assessment based on a degradation model[J]. IEEE Transactions on Image Processing, 2000, 9(4): 636-650.
[5]	CHANDLER D M and HEMAMI S S. VSNR: a wavelet-based visual signal-to-noise ratio for natural images[J]. IEEE Transactions on Image Processing, 2007, 16(9): 2284-2298.
[6]	WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.
[7]	WANG Z, SIMONCELLI E P, and BOVIK A C. Multi-scale structural similarity for image quality assessment[C]. Proceedings of the 37th IEEE Asilomar Conference on Signals, Systems and Computers. Pacific Grove, CA, 2003: 1398-1402.
[8]	靳鑫, 蒋刚毅, 陈芬, 等. 基于结构相似度的自适应图像质量评价[J]. 光电子 . 激光. 2014, 25(2): 378-385.
	JIN Xin, JIANG Gangyi, CHEN Fen, et al. Adaptive image quality assessment based on structural similarity[J]. Journal of Optoelectronics Laser, 2014, 25(2): 378-385.
[9]	KNILL D C and POUGET R. The Bayesian brain: the role of uncertainty in neural coding and computation[J]. Trends in Neuroscience, 2004, 27(12): 712-719.
[10]	FRISTON K. The free-energy principle: a unified brain theory?[J]. Nature Reviews Neuroscience, 2010, 11(2): 127-138.
[11]	ZHAI G, WU X, YANG X, et al. A psychovisual quality metric in Free-Energy principle[J]. IEEE Transactions on Image Processing, 2012, 21(1): 41-52.
[12]	WU J J, LIN W S, and SHI G G. Perceptual quality metric with internal generative mechanism[J]. IEEE Transactions on Image Processing, 2013, 22(1): 43-54.
[13]	ZHENG K H, YU M, JIN X, et al. New reduced-reference objective stereo image quality assessment model based on human visual system[C]. 2014 3DTV-Conference: The True Vision-Capture, Transmission and Display of 3D Video (3DTV-CON 2014), 2014: 1-4.
[14]	WU J J, LIN W S, and SHU G M. Visual masking estimation based on structural uncertainty[C]. IEEE International Symposium on Circuits and Systems (ISCAS), Beijing, 2013: 933-936.
[15]	张艳, 安平, 尤志翔, 等. 基于边缘差异的虚拟视图像质量评价方法[J]. 电子与信息学报, 2013, 35(8): 1894-1900. doi: 10. 3724/SP.J.1146.2012.01475.
	ZHANG Yan, AN Ping, YOU Zhi-xiang, et al. Quality assessment for virtual view image based on edge difference[J]. Journal of Electronics & Information Technology, 2013, 35(8): 1894-1900. doi: 10.3724/SP.J.1146.2012.01475.
[16]	YANG X K, LING W S, LU Z K, et al. Just noticeable distortion model and its applications in video coding[J]. Signal Processing: Image Communication, 2005, 20(7): 662-680.
[17]	南栋, 毕笃彦, 查宇飞, 等. 基于参数估计的无参考型图像质量评价算法[J]. 电子与信息学报, 2013, 35(9): 2066-2072. doi: 10. 3724/SP.J.1146.2012.01652.
	NAN Dong, BI Duyan, ZHA Yufei, et al. A no-reference image quality assessment method based on parameter estimation[J]. Journal of Electronics & Information Technology, 2013, 35(9): 2066-2072. doi: 10.3724/SP.J. 1146. 2012.01652.
[18]	HORITA Y, SHIBATA K, KAWAYOKE Y, et al. Mict: image quality evaluation database[OL]. http://mict.eng. utoyama.ac.jp/mict/index2.html, 2011.
[19]	NINASSI A, CALLET P L, and AUTRUSSEAU F. Subjective quality assessment-ivc database[OL]. http:// www2.irccyn.ec-nantes.fr/ivcdb/, 2005.
[20]	SHEIKH H R, WANG Z, BOVIKAND A C, et al. Image and video quality assessment research at live[OL]. http:// live.ece.utexas.edu/research/quality/, 2008.3.10.
[21]	LARSON E C and CHANDLER D M. Categorical image quality (csiq) database[OL]. http://vision.okstate.edu/csiq, 2010.
[22]	LUKIN N P V, ZELENSKY A, CARLI M, et al. Tid2008-a database for evaluation of full reference visual quality assessment metrics[J]. Advances of Modern Radio Electronics, 2009, 10(5): 30-45.