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Screen Content Picture Quality Evaluation by Colorful Sparse Reference Information

  • Conference paper
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Digital TV and Wireless Multimedia Communication (IFTC 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1181))

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Abstract

With the rapid development of multimedia interactive applications, the processing volume of the screen content (SC) images is increasing day by day. The research on image quality assessment is the basis of many other applications. The focus of general image quality assessment (QA) research is natural scene (NS) images, now for the quality assessment research of SC images becomes very urgent and has received more and more attention. Accurate quality assessment of SC images helps improve the user experience. Based on these, this paper proposes an improved method using very sparse reference information for accurate quality assessment of SC images. Specifically, the proposed method extracts macroscopic, microscopic structure and color information respectively, and measures the differences in terms of macroscopic, microscopic features and color information between the original SC image and its distorted version, and finally calculates the overall quality score of the distorted SC image. The quality assessment model we built uses a dimension reduction histogram and only needs to transmit very sparse reference information. Experiments show that the proposed method has obvious superiority over the state-of-the-art relevant quality metrics in the visual quality assessment of SC images.

This work is supported by the Major Science and Technology Program for Water Pollution Control and Treatment of China (2018ZX07111005), Natural Science Research of Jiangsu Higher Education Institutions under grant (18KJB52).

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References

  1. Yang, X.K., Ling, W.S., Lu, Z.K., Ong, E.P., Yao, S.S.: Just noticeable distortion model and its applications in video coding. Sig. Process. Image Commun. 20, 742–752 (2005)

    Google Scholar 

  2. Yang, X.K., Lin, W.S., Lu, Z.K., Ong, E.P., Yao, S.S.: Motion-compensated residue preprocessing in video coding based on just-noticeable-distortion profile. IEEE Trans. Circ. Syst. Video Technol. 15(6), 742–752 (2005)

    Article  Google Scholar 

  3. Zhai, G., Cai, J., Lin, W., Yang, X., Zhang, W., Etoh, M.: Cross-dimensional perceptual quality assessment for low bit-rate videos. IEEE Trans. Multimed. 10(7), 1316–1324 (2008)

    Article  Google Scholar 

  4. Zhai, G., Zhang, W., Yang, X., Lin, W., Xu, Y.: Efficient image deblocking based on postfiltering in shifted windows. IEEE Trans. Circ. Syst. Video Technol. 18(1), 122–126 (2008)

    Article  Google Scholar 

  5. Zhu, W., Ding, W., Xu, J., Shi, Y., Yin, B.: Screen content coding based on HEVC framework. IEEE Trans. Multimed. 16(5), 1316–1326 (2014)

    Article  Google Scholar 

  6. Gu, K., Zhai, G., Yang, X., Zhang, W., Chen, C.W.: Automatic contrast enhancement technology with saliency preservation. IEEE Trans. Circ. Syst. Video Technol. 25(9), 1480–1494 (2015)

    Article  Google Scholar 

  7. Gu, K., Tao, D., Qiao, J., Lin, W.: Learning a no-reference quality assessment model of enhanced images with big data. IEEE Trans. Neural Netw. Learn. Syst. 29(4), 1301–1313 (2018)

    Article  Google Scholar 

  8. Zhu, X., Milanfar, P.: Automatic parameter selection for denoising algorithms using a no-reference measure of image content. IEEE Trans. Image Process. 19(12), 3116–3132 (2010)

    Article  MathSciNet  Google Scholar 

  9. Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)

    Article  MathSciNet  Google Scholar 

  10. Wu, H.R., Reibman, A.R., Lin, W., Pereira, F., Hemami, S.S.: Perceptual visual signal compression and transmission. Proc. IEEE 101(9), 2025–2043 (2013)

    Article  Google Scholar 

  11. Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)

    Article  MathSciNet  Google Scholar 

  12. Liu, A., Lin, W., Narwaria, M.: Image quality assessment based on gradient similarity. IEEE Trans. Image Process. 21(4), 1500–1512 (2012)

    Article  MathSciNet  Google Scholar 

  13. Yue, G., Hou, C., Gu, K., Mao, S., Zhang, W.: Biologically inspired blind quality assessment of tone-mapped images. IEEE Trans. Ind. Electron. 65(3), 2525–2536 (2018)

    Article  Google Scholar 

  14. Gu, K., Zhai, G., Yang, X., Zhang, W.: Using free energy principle for blind image quality assessment. IEEE Trans. Multimed. 17(1), 50–63 (2015)

    Article  Google Scholar 

  15. Zhai, G., Wu, X., Yang, X., Lin, W., Zhang, W.: A psychovisual quality metric in free-energy principle. IEEE Trans. Image Process. 21(1), 41–52 (2012)

    Article  MathSciNet  Google Scholar 

  16. Gu, K., Zhai, G., Lin, W., Liu, M.: The analysis of image contrast: from quality assessment to automatic enhancement. IEEE Trans. Cybern. 46(1), 284–297 (2016)

    Article  Google Scholar 

  17. Gu, K., Lin, W., Zhai, G., Yang, X., Zhang, W., Chen, C.W.: No-reference quality metric of contrast-distorted images based on information maximization. IEEE Trans. Cybern. 47(12), 4559–4565 (2017)

    Article  Google Scholar 

  18. Gu, K., Zhou, J., Qiao, J., Zhai, G., Lin, W., Bovik, A.C.: No-reference quality assessment of screen content pictures. IEEE Trans. Image Process. 26(8), 4005–4018 (2017)

    Article  MathSciNet  Google Scholar 

  19. Yang, H., Fang, Y., Lin, W.: Perceptual quality assessment of screen content images. IEEE Trans. Image Process. 24(11), 4408–4421 (2015)

    Article  MathSciNet  Google Scholar 

  20. Gu, K., et al.: Saliency-guided quality assessment of screen content images. IEEE Trans. Multimed. 18(6), 1–13 (2016)

    Article  Google Scholar 

  21. Wang, S., et al.: Subjective and objective quality assessment of compressed screen content images. IEEE J. Emerg. Sel. Top. Circ. Syst. 6(4), 532–543 (2016)

    Article  Google Scholar 

  22. Wang, S., Gu, K., Zhang, X., Lin, W., Ma, S., Gao, W.: Reduced-reference quality assessment of screen content images. IEEE Trans. Circ. Syst. Video Technol. 28(1), 1–14 (2018)

    Article  Google Scholar 

  23. Jakhetiya, V., Gu, K., Lin, W., Li, Q., Jaiswal, S.P.: A prediction backed model for quality assessment of screen content and 3-D synthesized images. IEEE Trans. Ind. Inf. 14(2), 652–660 (2018)

    Article  Google Scholar 

  24. Ni, Z., Ma, L., Zeng, H., Cai, C., Ma, K.: Gradient direction for screen content image quality assessment. IEEE Signal Process. Lett. 23(10), 1394–1398 (2016)

    Article  Google Scholar 

  25. Zhang, L., Shen, Y., Li, H.: VSI: a visual saliency induced index for perceptual image quality assessment. IEEE Trans. Image Process. 23(10), 4270–4281 (2014)

    Article  MathSciNet  Google Scholar 

  26. Gu, K., Li, L., Lu, H., Min, X., Lin, W.: A fast reliable image quality predictor by fusing micro- and macro-structures. IEEE Trans. Ind. Electron. 64(5), 3903–3912 (2017)

    Article  Google Scholar 

  27. Min, X., Zhai, G., Gu, K., Yang, X., Guan, X.: Objective quality evaluation of dehazed images. IEEE Trans. Intell. Transp. Syst. 20, 1–14 (2018)

    Google Scholar 

  28. Li, Q., Wang, Z.: Reduced-reference image quality assessment using divisive normalization-based image representation. IEEE J. Sel. Top. Signal Process. 3(2), 202–211 (2009)

    Article  Google Scholar 

  29. Wu, J., Lin, W., Shi, G., Liu, A.: Reduced-reference image quality assessment with visual information fidelity. IEEE Trans. Multimed. 15(7), 1700–1705 (2013)

    Article  Google Scholar 

  30. Wang, Z., Simoncelli, E.P.: Reduced-reference image quality assessment using a wavelet-domain natural image statistic model. In: Proceedings of the SPIE Human Vision and Electronic Imaging X, vol. 5666, pp. 149–159 (2005)

    Google Scholar 

  31. Narwaria, M., Lin, W., McLoughlin, I.V., Emmanuel, S., Chia, L.: Fourier transform-based scalable image quality measure. IEEE Trans. Image Process. 21(8), 3364–3377 (2012)

    Article  MathSciNet  Google Scholar 

  32. Gu, K., Zhai, G., Yang, X., Zhang, W.: A new reduced-reference image quality assessment using structural degradation model. In: 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013), pp. 1095–1098, May 2013

    Google Scholar 

  33. Min, X., Zhai, G., Gu, K., Liu, Y., Yang, X.: Blind image quality estimation via distortion aggravation. IEEE Trans. Broadcast. 64(2), 508–517 (2018)

    Article  Google Scholar 

  34. Min, X., Gu, K., Zhai, G., Liu, J., Yang, X., Chen, C.W.: Blind quality assessment based on pseudo-reference image. IEEE Trans. Multimed. 20(8), 2049–2062 (2018)

    Article  Google Scholar 

  35. Xia, Z., Gu, K., Wang, S., Liu, H., Kwong, S.T.W.: Towards accurate quality estimation of screen content pictures with very sparse reference information. IEEE Trans. Ind. Electron. 67(3), 2251–2261 (2019)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Huiqing Zhang, Donghao Li & Shuo Li

  2. Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China

    Huiqing Zhang, Donghao Li & Shuo Li

  3. The State Information Center of China, Beijing, 100045, China

    Zhifang Xia

  4. Jiangsu Vocational College of Business, Jiangsu, 226000, China

    Lijuan Tang

Authors
  1. Huiqing Zhang
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  2. Donghao Li
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  3. Shuo Li
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  4. Zhifang Xia
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  5. Lijuan Tang
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Corresponding author

Correspondence to Donghao Li .

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Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

  2. Shanghai Jiao Tong University, Shanghai, China

    Jun Zhou

  3. Shanghai Jiao Tong University, Shanghai, China

    Hua Yang

  4. Shanghai University, Shanghai, China

    Ping An

  5. Shanghai Jiao Tong University, Shanghai, China

    Xiaokang Yang

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Zhang, H., Li, D., Li, S., Xia, Z., Tang, L. (2020). Screen Content Picture Quality Evaluation by Colorful Sparse Reference Information. In: Zhai, G., Zhou, J., Yang, H., An, P., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2019. Communications in Computer and Information Science, vol 1181. Springer, Singapore. https://doi.org/10.1007/978-981-15-3341-9_24

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  • DOI: https://doi.org/10.1007/978-981-15-3341-9_24

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3340-2

  • Online ISBN: 978-981-15-3341-9

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