Learning-Based Quality Assessment for Image Super-Resolution

• URL: http://arxiv.org/abs/2012.08732v1
• Date: Wed, 16 Dec 2020 04:06:27 GMT
• Title: Learning-Based Quality Assessment for Image Super-Resolution
• Authors: Tiesong Zhao, Yuting Lin, Yiwen Xu, Weiling Chen, Zhou Wang
• Abstract summary: We build a large-scale SR image database using a novel semi-automatic labeling approach. The resulting SR Image quality database contains 8,400 images of 100 natural scenes. We train an end-to-end Deep Image SR Quality (DISQ) model by employing two-stream Deep Neural Networks (DNNs) for feature extraction, followed by a feature fusion network for quality prediction.
• Score: 25.76907513611358
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image Super-Resolution (SR) techniques improve visual quality by enhancing the spatial resolution of images. Quality evaluation metrics play a critical role in comparing and optimizing SR algorithms, but current metrics achieve only limited success, largely due to the lack of large-scale quality databases, which are essential for learning accurate and robust SR quality metrics. In this work, we first build a large-scale SR image database using a novel semi-automatic labeling approach, which allows us to label a large number of images with manageable human workload. The resulting SR Image quality database with Semi-Automatic Ratings (SISAR), so far the largest of SR-IQA database, contains 8,400 images of 100 natural scenes. We train an end-to-end Deep Image SR Quality (DISQ) model by employing two-stream Deep Neural Networks (DNNs) for feature extraction, followed by a feature fusion network for quality prediction. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics and achieves promising generalization performance in cross-database tests. The SISAR database and DISQ model will be made publicly available to facilitate reproducible research.

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