Blind Image Quality Assessment Using Convolutional Neural Network

M. Jaishree; N. V.Subba Reddy

doi:10.1109/CSITSS.2018.8768768

Blind Image Quality Assessment Using Convolutional Neural Network

M. Jaishree, N. V.Subba Reddy

Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	204-207
Number of pages	4
ISBN (Electronic)	9781538660782
DOIs	https://doi.org/10.1109/CSITSS.2018.8768768
Publication status	Published - 12-2018
Event	3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018 - Bengaluru, India Duration: 20-12-2018 → 22-12-2018

Publication series

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

Conference

Conference	3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018
Country/Territory	India
City	Bengaluru
Period	20-12-18 → 22-12-18

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

Access to Document

10.1109/CSITSS.2018.8768768

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). Article 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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title = "Blind Image Quality Assessment Using Convolutional Neural Network",

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.",

author = "M. Jaishree and Reddy, {N. V.Subba}",

year = "2018",

month = dec,

doi = "10.1109/CSITSS.2018.8768768",

language = "English",

series = "Proceedings 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018",

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booktitle = "Proceedings 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018",

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note = "3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018 ; Conference date: 20-12-2018 Through 22-12-2018",

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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 proceeding › Conference contribution

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N2 - 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.

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

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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). doi: 10.1109/CSITSS.2018.8768768

Blind Image Quality Assessment Using Convolutional Neural Network

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