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.

• EONSS: Blind Quality Assessment of Multiply Distorted Images using Deep Neural Networks
  • 1. Introduction
    • 1.2 Network
    • 1.3 Results
    • 1.4 Citation
  • 2. Code

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

1.3 Results

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

• Testing code and pretrained model [GitHub] [zip]