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Objective assessment method of image quality based on visual perception of image content

Yao Jun-Cai Liu Gui-Zhong

Objective assessment method of image quality based on visual perception of image content

Yao Jun-Cai, Liu Gui-Zhong
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  • Objective image quality assessment (IQA) plays a very important role in transmission, encoding, and quality of service (QoS) of the image and video data. However, the existing IQA methods often do not consider image content features and their visual perception, so there is a certain gap between the objective IQA sores and the subjective perception. To solve this problem, in the study, we propose an objective IQA method based on the visual perception of image content, which combines the complexity characteristics of image content, and the properties of masking, contrast sensitivity and luminance perception nonlinearity of human visual system (HVS). In the proposed method, the image is first transformed using a nonlinear model of luminance perception to obtain the intensity perception image. Then, the intensity information is summed using the contrast sensitivity values of HVS and the average contrast values of the local image as a weighting factor of the intensity. The summed data information is taken as the content of human perceiving image, and an image perception model is constructed. Finally, the reference images and distorted images are perceived by simulating the HVS with this model. Moreover, the difference in intensity between two perceived images is calculated. Based on the intensity difference and peak signal-to -noise ratio model, an objective IQA model is constructed. Further, the simulation with 47 reference images and 1549 test images in the LIVE, TID2008, and CSIQ databases is conducted. Moreover, the experimental results are compared with those of four typical objective IQA models, namely SSIM, VSNR, FSIM, and PSNRHVS. In addition, we explore the factors that affect the IQA accuracy and a way to improve assessment accuracy by combining HVS characteristics, through analyzing the correlation between IQA results of the proposed model and the subjective mean opinion scores (MOSs) provided in the three image databases from the following two aspects. Namely, (1) all reference images in three image databases are distorted by multiple types, and the distorted images of each reference image are taken as a test sequence. Then, the proposed model is used to evaluate each test sequence to obtain the IQA scores. By analyzing the correlation between the IQA scores of each test sequence and the subjective MOSs and comparing them with the assessment results of SSIM, we explore the influence of the image content complexity on the objective IQA accuracy. (2) The test images which are distorted by each type and many distortion degrees are used as another sequence, and they are evaluated by the proposed IQA model. By analyzing the correlation between the subjective MOSs and the IQA results of each test sequence, and comparing them with assessment results of SSIM, we discuss the influence of image distortion mode on the IQA accuracy. The experimental results show that the coefficient values of Pearson linear correlation and Spearman rank order correlation between the objective IQA scores obtained by the proposed method and the subjective MOSs have been averagely improved by 9.5402% and 3.2852%, respectively, in comparison with IQA results from the SSIM method. Also, they are enhanced more significantly than those fom the PSNRHVS and VSNR methods. In summary, it is shown that the proposed IQA method is an effective and feasible method of objectively assessing the image quality; moreover, it is shown that in the objective assessment of image quality it is very helpful to improve the consistency of subjective and objective assessment of image quality by considering the content perception and complexity analysis of the images.
      Corresponding author: Liu Gui-Zhong, liugz@mail.xjtu.edu.cn
    • Funds: Project supported by the National Natural Science Foundation of China (Grant No. 61301237) and the Natural Science Foundation of Shaanxi Province, China (Grant No. 2015KJXX-42).
    [1]

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    [2]

    Zhuang J Y, Chen Q, He W J, Mao T Y 2016 Acta Phys. Sin. 65 040501 (in Chinese)[庄佳衍, 陈钱, 何伟基, 冒添逸 2016 物理学报 65 040501]

    [3]

    Wang Z, Bovik A C, Sheikh H R, Simoncelli E P 2004 IEEE Trans. Image Process. 13 600

    [4]

    Chandler D M, Hemami S S 2007 IEEE Trans. Image Process. 16 2284

    [5]

    Zhang L, Zhang L, Mou X, Zhang D 2011 IEEE Trans. Image Process. 20 2378

    [6]

    Xue W, Zhang L, Mou X, Bovik A C 2014 IEEE Trans. Image Process. 23 684

    [7]

    Zhang L, Shen Y, Li H 2014 IEEE Trans. Image Process. 23 4270

    [8]

    Paudyal P, Battisti F, Sjöström M, Olsson R, Carli M 2017 IEEE Trans. Broadcast. 63 507

    [9]

    Bae S H, Kim M 2016 IEEE Trans. Image Process. 25 2392.

    [10]

    Gu K, Wang S, Zhai G, Ma S, Yang X 2016 Signal Image Video Process. 10 803

    [11]

    Wang X L, Wu D W, Li X, Zhu H N, Chen K, Fang G 2017 Acta Phys. Sin. 66 230302 (in Chinese)[王湘林, 吴德伟, 李响, 朱浩男, 陈坤, 方冠 2017 物理学报 66 230302]

    [12]

    Akamine W Y L, Farias M C Q 2014 J. Electron. Imaging 23 061107

    [13]

    Li C F, Bovik A C 2010 J. Electron. Imaging 19 143

    [14]

    Guo J, Hu G, Xu W, Huang L 2017 J. Vis. Commun. Image Represent. 43 50

    [15]

    Hou W, Mei F H, Chen G J, Deng X W 2015 Acta Phys. Sin. 64 024202 (in Chinese)[侯旺, 梅风华, 陈国军, 邓喜文 2015 物理学报 64 024202]

    [16]

    Stephen W, Huw O, Vien C, Iain P S 2006 Color Res. Appl. 31 315

    [17]

    Nadenau M 2000 Ph. D. Dissertation (Lausanne:École Polytechnique Fédérale de Lausanne)

    [18]

    Yao J C, Shen J, Wang J H 2008 Acta Phys. Sin. 57 4034 (in Chinese)[姚军财, 申静, 王剑华 2008 物理学报 57 4034]

    [19]

    Sheikh H R, Sabir M F, Bovik A C 2006 IEEE Trans. Image Process. 15 3440

    [20]

    Nikolay P, Vladimir L, Alexander Z, Karen E, Jaakko A, Marco C, Federica B 2009 Adv. Modern Radioelectron. 10 30

    [21]

    Larson E C, Chandler D M 2010 J. Electron. Imaging 19 011006

    [22]

    Zhang F, Bull D R 2013 Proceedings of the 20th IEEE Interatinonal Conference on Image Processing (ICIP) Melbourne, Australia,September 15-18, 2013 p39

    [23]

    Gu K, Wang S, Zhai G, Lin W, Yang X, Zhang W 2016 IEEE Trans. Image Process. 62 446

  • [1]

    Wang Y Q 2014 J. Nanjing Univ. (Nat. Sci. Ed.) 50 361 (in Chinese)[王元庆 2014 南京大学学报(自然科学版) 50 361]

    [2]

    Zhuang J Y, Chen Q, He W J, Mao T Y 2016 Acta Phys. Sin. 65 040501 (in Chinese)[庄佳衍, 陈钱, 何伟基, 冒添逸 2016 物理学报 65 040501]

    [3]

    Wang Z, Bovik A C, Sheikh H R, Simoncelli E P 2004 IEEE Trans. Image Process. 13 600

    [4]

    Chandler D M, Hemami S S 2007 IEEE Trans. Image Process. 16 2284

    [5]

    Zhang L, Zhang L, Mou X, Zhang D 2011 IEEE Trans. Image Process. 20 2378

    [6]

    Xue W, Zhang L, Mou X, Bovik A C 2014 IEEE Trans. Image Process. 23 684

    [7]

    Zhang L, Shen Y, Li H 2014 IEEE Trans. Image Process. 23 4270

    [8]

    Paudyal P, Battisti F, Sjöström M, Olsson R, Carli M 2017 IEEE Trans. Broadcast. 63 507

    [9]

    Bae S H, Kim M 2016 IEEE Trans. Image Process. 25 2392.

    [10]

    Gu K, Wang S, Zhai G, Ma S, Yang X 2016 Signal Image Video Process. 10 803

    [11]

    Wang X L, Wu D W, Li X, Zhu H N, Chen K, Fang G 2017 Acta Phys. Sin. 66 230302 (in Chinese)[王湘林, 吴德伟, 李响, 朱浩男, 陈坤, 方冠 2017 物理学报 66 230302]

    [12]

    Akamine W Y L, Farias M C Q 2014 J. Electron. Imaging 23 061107

    [13]

    Li C F, Bovik A C 2010 J. Electron. Imaging 19 143

    [14]

    Guo J, Hu G, Xu W, Huang L 2017 J. Vis. Commun. Image Represent. 43 50

    [15]

    Hou W, Mei F H, Chen G J, Deng X W 2015 Acta Phys. Sin. 64 024202 (in Chinese)[侯旺, 梅风华, 陈国军, 邓喜文 2015 物理学报 64 024202]

    [16]

    Stephen W, Huw O, Vien C, Iain P S 2006 Color Res. Appl. 31 315

    [17]

    Nadenau M 2000 Ph. D. Dissertation (Lausanne:École Polytechnique Fédérale de Lausanne)

    [18]

    Yao J C, Shen J, Wang J H 2008 Acta Phys. Sin. 57 4034 (in Chinese)[姚军财, 申静, 王剑华 2008 物理学报 57 4034]

    [19]

    Sheikh H R, Sabir M F, Bovik A C 2006 IEEE Trans. Image Process. 15 3440

    [20]

    Nikolay P, Vladimir L, Alexander Z, Karen E, Jaakko A, Marco C, Federica B 2009 Adv. Modern Radioelectron. 10 30

    [21]

    Larson E C, Chandler D M 2010 J. Electron. Imaging 19 011006

    [22]

    Zhang F, Bull D R 2013 Proceedings of the 20th IEEE Interatinonal Conference on Image Processing (ICIP) Melbourne, Australia,September 15-18, 2013 p39

    [23]

    Gu K, Wang S, Zhai G, Lin W, Yang X, Zhang W 2016 IEEE Trans. Image Process. 62 446

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  • Received Date:  23 January 2018
  • Accepted Date:  07 March 2018
  • Published Online:  20 May 2019

Objective assessment method of image quality based on visual perception of image content

    Corresponding author: Liu Gui-Zhong, liugz@mail.xjtu.edu.cn
  • 1. School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China;
  • 2. School of Physics and Telecommunication Engineering, Shaanxi University of Technology, Hanzhong 723000, China
Fund Project:  Project supported by the National Natural Science Foundation of China (Grant No. 61301237) and the Natural Science Foundation of Shaanxi Province, China (Grant No. 2015KJXX-42).

Abstract: Objective image quality assessment (IQA) plays a very important role in transmission, encoding, and quality of service (QoS) of the image and video data. However, the existing IQA methods often do not consider image content features and their visual perception, so there is a certain gap between the objective IQA sores and the subjective perception. To solve this problem, in the study, we propose an objective IQA method based on the visual perception of image content, which combines the complexity characteristics of image content, and the properties of masking, contrast sensitivity and luminance perception nonlinearity of human visual system (HVS). In the proposed method, the image is first transformed using a nonlinear model of luminance perception to obtain the intensity perception image. Then, the intensity information is summed using the contrast sensitivity values of HVS and the average contrast values of the local image as a weighting factor of the intensity. The summed data information is taken as the content of human perceiving image, and an image perception model is constructed. Finally, the reference images and distorted images are perceived by simulating the HVS with this model. Moreover, the difference in intensity between two perceived images is calculated. Based on the intensity difference and peak signal-to -noise ratio model, an objective IQA model is constructed. Further, the simulation with 47 reference images and 1549 test images in the LIVE, TID2008, and CSIQ databases is conducted. Moreover, the experimental results are compared with those of four typical objective IQA models, namely SSIM, VSNR, FSIM, and PSNRHVS. In addition, we explore the factors that affect the IQA accuracy and a way to improve assessment accuracy by combining HVS characteristics, through analyzing the correlation between IQA results of the proposed model and the subjective mean opinion scores (MOSs) provided in the three image databases from the following two aspects. Namely, (1) all reference images in three image databases are distorted by multiple types, and the distorted images of each reference image are taken as a test sequence. Then, the proposed model is used to evaluate each test sequence to obtain the IQA scores. By analyzing the correlation between the IQA scores of each test sequence and the subjective MOSs and comparing them with the assessment results of SSIM, we explore the influence of the image content complexity on the objective IQA accuracy. (2) The test images which are distorted by each type and many distortion degrees are used as another sequence, and they are evaluated by the proposed IQA model. By analyzing the correlation between the subjective MOSs and the IQA results of each test sequence, and comparing them with assessment results of SSIM, we discuss the influence of image distortion mode on the IQA accuracy. The experimental results show that the coefficient values of Pearson linear correlation and Spearman rank order correlation between the objective IQA scores obtained by the proposed method and the subjective MOSs have been averagely improved by 9.5402% and 3.2852%, respectively, in comparison with IQA results from the SSIM method. Also, they are enhanced more significantly than those fom the PSNRHVS and VSNR methods. In summary, it is shown that the proposed IQA method is an effective and feasible method of objectively assessing the image quality; moreover, it is shown that in the objective assessment of image quality it is very helpful to improve the consistency of subjective and objective assessment of image quality by considering the content perception and complexity analysis of the images.

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