SPQE: Structure-and-Perception-Based Quality Evaluation for Image Super-Resolution

• URL: http://arxiv.org/abs/2205.03584v1
• Date: Sat, 7 May 2022 07:52:55 GMT
• Title: SPQE: Structure-and-Perception-Based Quality Evaluation for Image Super-Resolution
• Authors: Keke Zhang, Tiesong Zhao, Weiling Chen, Yuzhen Niu, Jinsong Hu
• Abstract summary: Super-Resolution technique has greatly improved the visual quality of images by enhancing their resolutions. It also calls for an efficient SR Image Quality Assessment (SR-IQA) to evaluate those algorithms or their generated images. In emerging deep-learning-based SR, a generated high-quality, visually pleasing image may have different structures from its corresponding low-quality image.
• Score: 24.584839578742237
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The image Super-Resolution (SR) technique has greatly improved the visual quality of images by enhancing their resolutions. It also calls for an efficient SR Image Quality Assessment (SR-IQA) to evaluate those algorithms or their generated images. In this paper, we focus on the SR-IQA under deep learning and propose a Structure-and-Perception-based Quality Evaluation (SPQE). In emerging deep-learning-based SR, a generated high-quality, visually pleasing image may have different structures from its corresponding low-quality image. In such case, how to balance the quality scores between no-reference perceptual quality and referenced structural similarity is a critical issue. To help ease this problem, we give a theoretical analysis on this tradeoff and further calculate adaptive weights for the two types of quality scores. We also propose two deep-learning-based regressors to model the no-reference and referenced scores. By combining the quality scores and their weights, we propose a unified SPQE metric for SR-IQA. Experimental results demonstrate that the proposed method outperforms the state-of-the-arts in different datasets.

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