short-paper

Perceptual quality assessment based on visual attention analysis

Authors:

Junyong You,

Andrew Perkis,

Miska M. Hannuksela,

Moncef GabboujAuthors Info & Claims

MM '09: Proceedings of the 17th ACM international conference on Multimedia

Pages 561 - 564

https://doi.org/10.1145/1631272.1631356

Published: 19 October 2009 Publication History

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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.

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Daryanavard Chounchenani MShahbahrami AHassanpour RGaydadjiev G(2025)Deep Learning Based Image Aesthetic Quality Assessment- A ReviewACM Computing Surveys10.1145/371682057:7(1-36)Online publication date: 21-Feb-2025
https://dl.acm.org/doi/10.1145/3716820
Zhang KZhu DMin XGao ZZhai G(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
https://doi.org/10.1109/TCSVT.2023.3303933
Wu X(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
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Index Terms

Perceptual quality assessment based on visual attention analysis
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Image representations

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Information

Published In

MM '09: Proceedings of the 17th ACM international conference on Multimedia

October 2009

1202 pages

ISBN:9781605586083

DOI:10.1145/1631272

General Chairs:
Wen Gao
Peking University, China
,
Yong Rui
Microsoft, China
,
Alan Hanjalic
Delft University of Technology, The Netherlands
,
Program Chairs:
Changsheng Xu
Institute of Automation, Chinese Academy of Sciences, China
,
Eckehard Steinbach
Technical University of Munich, Germany
,
Abdulmotaleb El Saddik
University of Ottawa, Canada
,
Michelle Zhou
IBM T. J. Watson Research Center, USA

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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Published: 19 October 2009

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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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Daryanavard Chounchenani MShahbahrami AHassanpour RGaydadjiev G(2025)Deep Learning Based Image Aesthetic Quality Assessment- A ReviewACM Computing Surveys10.1145/371682057:7(1-36)Online publication date: 21-Feb-2025
https://dl.acm.org/doi/10.1145/3716820
Zhang KZhu DMin XGao ZZhai G(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
https://doi.org/10.1109/TCSVT.2023.3303933
Wu X(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
https://dl.acm.org/doi/10.1145/3490099.3511155
Hewage CAhmad AMallikarachchi TBarman NMartini M(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
https://doi.org/10.1109/ACCESS.2022.3180491
Zhang ZHu Z(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
https://doi.org/10.1109/ICCRD51685.2021.9386597
Bodini M(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
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Garcia Freitas PDa Eira LSantos SFarias M(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
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Bo YYu JZhang K(2018)Computational aesthetics and applicationsVisual Computing for Industry, Biomedicine, and Art10.1186/s42492-018-0006-11:1Online publication date: 5-Sep-2018
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Freitas PAlamgeer SAkamine WFarias MCesar PZink MMurray N(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
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Stereoscopic Images Quality Assessment with Visual Attention

Stereoscopic Images Quality Assessment with Visual Attention

Image quality assessment metrics combining structural similarity and image fidelity with visual attention

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