Blind Image Quality Assessment Using Convolutional Neural Network

Jaishree, M and Reddy, Subba N V (2018) Blind Image Quality Assessment Using Convolutional Neural Network. In: International Conference on Computational Systems and Information Technology for Sustainable Solutions 2018, 20/12/2018, Bangalore.

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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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Image Quality Assessment, Deep Learning, Transfer Learning, Convolutional Neural Network, Tensorflow, Spearman Rank-Order Correlation Coefficient (SROCC)
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Depositing User: MIT Library
Date Deposited: 09 Jan 2019 09:16
Last Modified: 09 Jan 2019 09:16

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