基于内在生成机制的多尺度结构相似性图像质量评价

孙彦景; 杨玉芬; 刘东林; 施文娟

doi:10.11999/JEIT150616

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

2.
(中国矿业大学信息与电气工程学院徐州 221116) ②(中国矿业大学江苏省煤矿电气与自动化工程实验室徐州 221008)

基金项目:

江苏省煤矿电气与自动化工程实验室建设项目(2014KJZX05，江苏省产学研前瞻性联合研究项目(BY2014028-01)，中央高校重大项目培育专项(2014ZDPY16)，国家自然科学基金(51274202)，江苏省自然科学基金(2013-2016, BK20131124)和中央高校创新人才基金(2013RC11)

计量
- 文章访问数: 1238
- HTML全文浏览量: 122
- PDF下载量: 494
- 被引次数: 0
出版历程
- 收稿日期: 2015-05-25
- 修回日期: 2015-08-21
- 刊出日期: 2016-01-19

Multiple-scale Structural Similarity Image Quality Assessment Based on Internal Generative Mechanism

2.
(School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China)

Funds:

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)

摘要

摘要: 该文针对多尺度结构相似性(Multiple-scale Structural SIMilarity, MSSIM)图像质量评价算法对图像信息不确定部分度量能力的不足，结合人类视觉系统(HVS)，提出基于内在生成机制(internal generative mechanism)的iMSSIM算法。首先采用基于逐段式自回归(Piecewise AutoRegressive, PAR)模型的内在生成机制将失真图像和原始图像分解成采用MSSIM算法评分的图像内容预测部分和采用PSNR评分的图像信息不确定部分；然后采用均方误差(MSE)进行加权来联合这两部分评分获得最终结果。在基准数据库上完成的对比实验表明：该算法不仅在不同失真类型上性能最好，且在6个公开数据库上的性能优于现有算法。
- 图像质量评价 /
- 多尺度结构相似性 /
- 内在生成机制 /
- 逐段式自回归模型
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.
- Image quality assessment /
- Multi-scale Structural SIMilarity (MSSIM) /
- Internal Generative Mechanism (IGM) /
- Piecewise AutoRegressive (PAR) model

HTML全文

参考文献(23)

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.

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.

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.

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.

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.

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.

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.

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.

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.

FRISTON K. The free-energy principle: a unified brain theory?[J]. Nature Reviews Neuroscience, 2010, 11(2): 127-138.

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.

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.

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.

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.

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.

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.

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.

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.

施引文献

资源附件(0)

访问统计