Blind Image Quality Assessment Using Convolutional Neural Network

M. Jaishree, N. V.Subba Reddy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we use Convolutional Neural Network for Blind Image Quality Assessment (BIQA) by utilizing its power to extract features from images and then learn a score or quality index for each image. The evaluation of the proposed model conducted on TID2013 database reveals that using CNN model is way more effective in assessing the quality of images with various distortions in comparison to the other existing assessment methods. The Spearman Rank-Order Correlation Coefficient, used to evaluate the performance of the model, has a very high value in comparison to other existing models, suggesting the efficiency of the proposed model.

Original languageEnglish
Title of host publicationProceedings 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages204-207
Number of pages4
ISBN (Electronic)9781538660782
DOIs
Publication statusPublished - 12-2018
Event3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018 - Bengaluru, India
Duration: 20-12-201822-12-2018

Publication series

NameProceedings 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018

Conference

Conference3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018
CountryIndia
CityBengaluru
Period20-12-1822-12-18

Fingerprint

neural network
Image quality
Neural networks
CNN
Quality assessment
efficiency
evaluation
performance

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Safety, Risk, Reliability and Quality
  • Communication
  • Information Systems and Management

Cite this

Jaishree, M., & Reddy, N. V. S. (2018). Blind Image Quality Assessment Using Convolutional Neural Network. In Proceedings 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018 (pp. 204-207). [8768768] (Proceedings 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSITSS.2018.8768768
Jaishree, M. ; Reddy, N. V.Subba. / Blind Image Quality Assessment Using Convolutional Neural Network. Proceedings 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 204-207 (Proceedings 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018).
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Jaishree, M & Reddy, NVS 2018, Blind Image Quality Assessment Using Convolutional Neural Network. in Proceedings 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018., 8768768, Proceedings 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018, Institute of Electrical and Electronics Engineers Inc., pp. 204-207, 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018, Bengaluru, India, 20-12-18. https://doi.org/10.1109/CSITSS.2018.8768768

Blind Image Quality Assessment Using Convolutional Neural Network. / Jaishree, M.; Reddy, N. V.Subba.

Proceedings 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 204-207 8768768 (Proceedings 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - In this paper, we use Convolutional Neural Network for Blind Image Quality Assessment (BIQA) by utilizing its power to extract features from images and then learn a score or quality index for each image. The evaluation of the proposed model conducted on TID2013 database reveals that using CNN model is way more effective in assessing the quality of images with various distortions in comparison to the other existing assessment methods. The Spearman Rank-Order Correlation Coefficient, used to evaluate the performance of the model, has a very high value in comparison to other existing models, suggesting the efficiency of the proposed model.

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Jaishree M, Reddy NVS. Blind Image Quality Assessment Using Convolutional Neural Network. In Proceedings 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 204-207. 8768768. (Proceedings 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018). https://doi.org/10.1109/CSITSS.2018.8768768