Perceptual Quality Optimization of Image Super-Resolution
Wei Zhou, Yixiao Li, Hadi Amirpour, Xiaoshuai Hao, Jiang Liu, Peng Wang, Hantao Liu

TL;DR
This paper introduces Efficient-PBAN, a novel deep learning framework that optimizes image super-resolution for human perceptual quality by learning a perceptual quality metric aligned with subjective judgments and integrating it into the training process.
Contribution
The paper presents a new perceptual quality metric and an efficient network that jointly predicts and optimizes for human-perceived image quality in super-resolution tasks.
Findings
Efficient-PBAN correlates strongly with human subjective judgments.
The integrated perceptual loss improves the perceptual quality of super-resolved images.
The approach outperforms existing methods in perceptual quality metrics.
Abstract
Single-image super-resolution (SR) has achieved remarkable progress with deep learning, yet most approaches rely on distortion-oriented losses or heuristic perceptual priors, which often lead to a trade-off between fidelity and visual quality. To address this issue, we propose an \textit{Efficient Perceptual Bi-directional Attention Network (Efficient-PBAN)} that explicitly optimizes SR towards human-preferred quality. Unlike patch-based quality models, Efficient-PBAN avoids extensive patch sampling and enables efficient image-level perception. The proposed framework is trained on our self-constructed SR quality dataset that covers a wide range of state-of-the-art SR methods with corresponding human opinion scores. Using this dataset, Efficient-PBAN learns to predict perceptual quality in a way that correlates strongly with subjective judgments. The learned metric is further integrated…
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Taxonomy
TopicsImage and Video Quality Assessment · Advanced Image Processing Techniques · Advanced Image Fusion Techniques
