Deep Bi-directional Attention Network for Image Super-Resolution Quality Assessment
CoRR(2024)
摘要
There has emerged a growing interest in exploring efficient quality
assessment algorithms for image super-resolution (SR). However, employing deep
learning techniques, especially dual-branch algorithms, to automatically
evaluate the visual quality of SR images remains challenging. Existing SR image
quality assessment (IQA) metrics based on two-stream networks lack interactions
between branches. To address this, we propose a novel full-reference IQA
(FR-IQA) method for SR images. Specifically, producing SR images and evaluating
how close the SR images are to the corresponding HR references are separate
processes. Based on this consideration, we construct a deep Bi-directional
Attention Network (BiAtten-Net) that dynamically deepens visual attention to
distortions in both processes, which aligns well with the human visual system
(HVS). Experiments on public SR quality databases demonstrate the superiority
of our proposed BiAtten-Net over state-of-the-art quality assessment methods.
In addition, the visualization results and ablation study show the
effectiveness of bi-directional attention.
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