arXiv:2403.10406v1 Announce Type: new
Abstract: 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.
Analysis of Image Super-Resolution Quality Assessment
Image super-resolution (SR) is a technique used to enhance the resolution and details of low-resolution images. As the demand for high-quality images continues to grow, there is a need for efficient quality assessment algorithms for SR. This article focuses on the use of deep learning techniques, specifically dual-branch algorithms, to automatically evaluate the visual quality of SR images.
The concept of dual-branch algorithms is an interesting one, as it involves using two separate processes: producing SR images and evaluating their closeness to the corresponding high-resolution (HR) references. This approach recognizes the fact that the evaluation process and the SR generation process are distinct and should be treated as such.
To address the challenge of lack of interactions between the branches in existing SR image quality assessment (IQA) metrics, the authors propose a novel full-reference IQA method called BiAtten-Net. This deep Bi-directional Attention Network dynamically deepens visual attention to distortions in both processes, mimicking the human visual system (HVS).
This research has significant implications in the field of multimedia information systems, as it combines concepts from computer vision, deep learning, and image processing. The multi-disciplinary nature of this work highlights the need for collaboration across different domains.
Furthermore, this work is related to the wider field of animations, artificial reality, augmented reality, and virtual realities. SR techniques are often used in these fields to enhance the visual quality of images and videos. The ability to automatically assess the quality of SR images is crucial for ensuring optimal user experiences in these applications.
The experiments conducted in this study demonstrate the superiority of the proposed BiAtten-Net over existing quality assessment methods. The visualization results and ablation study provide additional evidence of the effectiveness of the bi-directional attention approach.
In conclusion, this article presents a novel approach to image super-resolution quality assessment using deep learning techniques and bi-directional attention. The findings of this research have implications not only in the field of image processing but also in the broader context of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.