No-Reference Image Quality Assessment via Feature Fusion and Multi-Task Learning

• URL: http://arxiv.org/abs/2006.03783v1
• Date: Sat, 6 Jun 2020 05:04:10 GMT
• Title: No-Reference Image Quality Assessment via Feature Fusion and Multi-Task Learning
• Authors: S. Alireza Golestaneh, Kris Kitani
• Abstract summary: Blind or no-reference image quality assessment (NR-IQA) is a fundamental, unsolved, and yet challenging problem. We propose a simple and yet effective general-purpose no-reference (NR) image quality assessment framework based on multi-task learning. Our model employs distortion types as well as subjective human scores to predict image quality.
• Score: 29.19484863898778
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Blind or no-reference image quality assessment (NR-IQA) is a fundamental, unsolved, and yet challenging problem due to the unavailability of a reference image. It is vital to the streaming and social media industries that impact billions of viewers daily. Although previous NR-IQA methods leveraged different feature extraction approaches, the performance bottleneck still exists. In this paper, we propose a simple and yet effective general-purpose no-reference (NR) image quality assessment (IQA) framework based on multi-task learning. Our model employs distortion types as well as subjective human scores to predict image quality. We propose a feature fusion method to utilize distortion information to improve the quality score estimation task. In our experiments, we demonstrate that by utilizing multi-task learning and our proposed feature fusion method, our model yields better performance for the NR-IQA task. To demonstrate the effectiveness of our approach, we test our approach on seven standard datasets and show that we achieve state-of-the-art results on various datasets.

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