EONSS: Blind Quality Assessment of Multiply Distorted Images using Deep Neural Networks

Zhongling Wang, Shahrukh Athar and Zhou Wang “Blind Quality Assessment of Multiply Distorted Images Using Deep Neural Networks”, 16th International Conference on Image Analysis and Recognition, Waterloo, Ontario, Canada, August 27-29, 2019.

1. Introduction

We propose a CNN based approach to build a blind IQA model for multiply distorted images, namely End-to-end Optimized deep neural Network using Synthetic Scores (EONSS).

1.2 Network

network

1.3 Results

srcc

1.4 Citation

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@InProceedings{wang2019blind,
author="Wang, Zhongling and Athar, Shahrukh and Wang, Zhou",
title="Blind Quality Assessment of Multiply Distorted Images Using Deep Neural Networks",
booktitle="International Conference on Image Analysis and Recognition",
year="2019",
pages="89--101"
}

2. Code