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Perceptual quality assessment based on visual attention analysis

Published: 19 October 2009 Publication History

Abstract

Most existing quality metrics do not take the human attention analysis into account. Attention to particular objects or regions is an important attribute of human vision and perception system in measuring perceived image and video qualities. This paper presents an approach for extracting visual attention regions based on a combination of a bottom-up saliency model and semantic image analysis. The use of PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural SIMilarity) in extracted attention regions is analyzed for image/video quality assessment, and a novel quality metric is proposed which can exploit the attributes of visual attention information adequately. The experimental results with respect to the subjective measurement demonstrate that the proposed metric outperforms the current methods.

References

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Winkler, S. 2005. Digital video quality: vision models and metrics, John Wiley&Sons Press.
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Pinson, M. and Wolf S. 2004. A new standardized method for objectively measuring video quality. IEEE Trans. Broadcasting, 50 (Sep. 2004), 312--322.
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Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P. 2004. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Processing, 13 (Apr. 2004), 600--612.
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Itti L. and Koch C. 2001. Computational modeling of visual attention, Nat. Rev. Neurosci., 2 (Mar. 2001), 194--203.
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Lu Z., Lin W., Yang X., et al. 2005. Modeling visual attention's modulatory aftereffects on visual sensitivity and quality evaluation. IEEE Trans. Image Processing, 14 (Nov. 2005), 1928--1942.
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Feng X., Liu T., Yang D., and Wang Y. 2008. Saliency based objective quality assessment of decoded video affected by packet losses. In Proceedings of IEEE Int. Conf. Image Processing (California, USA, Oct. 12--15, 2008), 2560--2563.
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SaliencyToolbox 2.1, http://www.saliencytoolbox.net.
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Sheikh H. R., Wang, Z., Cormack L., and Bovik A. C. LIVE Image Quality Assessment Database. http://live.ece.utexas.edu/research/quality.
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VQEG Sequence, ftp://ftp.crc.ca/crc/vqeg/TestSequences/.
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Cited By

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  • (2024)Synergetic Assessment of Quality and Aesthetic: Approach and Comprehensive Benchmark DatasetIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.3303933(1-1)Online publication date: 2024
  • (2022)Interpretable Aesthetic Analysis Model for Intelligent Photography Guidance SystemsProceedings of the 27th International Conference on Intelligent User Interfaces10.1145/3490099.3511155(661-671)Online publication date: 22-Mar-2022
  • (2022)Measuring, Modeling and Integrating Time-Varying Video Quality in End-to-End Multimedia Service Delivery: A Review and Open ChallengesIEEE Access10.1109/ACCESS.2022.318049110(60267-60293)Online publication date: 2022
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    cover image ACM Conferences
    MM '09: Proceedings of the 17th ACM international conference on Multimedia
    October 2009
    1202 pages
    ISBN:9781605586083
    DOI:10.1145/1631272
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 19 October 2009

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    Author Tags

    1. perceptual quality assessment
    2. quality metric
    3. visual attention

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    MM09
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    MM09: ACM Multimedia Conference
    October 19 - 24, 2009
    Beijing, China

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    Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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    Cited By

    View all
    • (2024)Synergetic Assessment of Quality and Aesthetic: Approach and Comprehensive Benchmark DatasetIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.3303933(1-1)Online publication date: 2024
    • (2022)Interpretable Aesthetic Analysis Model for Intelligent Photography Guidance SystemsProceedings of the 27th International Conference on Intelligent User Interfaces10.1145/3490099.3511155(661-671)Online publication date: 22-Mar-2022
    • (2022)Measuring, Modeling and Integrating Time-Varying Video Quality in End-to-End Multimedia Service Delivery: A Review and Open ChallengesIEEE Access10.1109/ACCESS.2022.318049110(60267-60293)Online publication date: 2022
    • (2021)Video Quality Assessment by Sparse Representation and Dynamic Atom Classification2021 IEEE 13th International Conference on Computer Research and Development (ICCRD)10.1109/ICCRD51685.2021.9386597(41-45)Online publication date: 5-Jan-2021
    • (2019)Will the Machine Like Your Image? Automatic Assessment of Beauty in Images with Machine Learning TechniquesInventions10.3390/inventions40300344:3(34)Online publication date: 28-Jun-2019
    • (2018)On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality AssessmentJournal of Imaging10.3390/jimaging41001144:10(114)Online publication date: 4-Oct-2018
    • (2018)Computational aesthetics and applicationsVisual Computing for Industry, Biomedicine, and Art10.1186/s42492-018-0006-11:1Online publication date: 5-Sep-2018
    • (2018)Referenceless image quality assessment by saliency, color-texture energy, and gradient boosting machinesJournal of the Brazilian Computer Society10.1186/s13173-018-0073-324:1Online publication date: 6-Aug-2018
    • (2018)Blind image quality assessment based on multiscale salient local binary patternsProceedings of the 9th ACM Multimedia Systems Conference10.1145/3204949.3204960(52-63)Online publication date: 12-Jun-2018
    • (2017)Image Aesthetic Assessment: An experimental surveyIEEE Signal Processing Magazine10.1109/MSP.2017.269657634:4(80-106)Online publication date: Jul-2017
    • Show More Cited By

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