Synergistic dual and efficient additive attention network for No-Reference Image Quality Assessment

Abstract

No-Reference Image Quality Assessment (NR-IQA) aims to evaluate the perceptual quality of images in alignment with human subjective judgments. However, most existing NR-IQA methods, while striving for high accuracy, often neglect computational complexity. To address this challenge, we propose a Synergistic Spatial and Channel and Efficient Additive Attention Network for NR-IQA. In our approach, we first employ a feature extraction module to derive features rich in both distortion and semantic information. Subsequently, we introduce a spatial-channel synergistic attention mechanism to enhance feature representations across spatial and channel dimensions. This attention module focuses on the most salient regions of the image and modulates feature responses accordingly, enabling the network to emphasize critical distortions and semantic features pertinent to perceptual quality assessment. Specifically, the spatial attention mechanism identifies significant regions that substantially contribute to quality perception, while the channel attention mechanism adjusts the importance of each feature channel, ensuring effective utilization of spatial and channel-specific information. Furthermore, to enhance the model’s robustness, we incorporate an Efficient Additive Attention mechanism alongside a Multi-scale Feed-forward Network, designed to reduce computational costs without compromising performance. Finally, a dual-branch structure for patch-weighted quality prediction is employed to derive the final quality score based on the weighted scores of individual patches. Extensive experimental evaluations on four widely used benchmark datasets demonstrate that the proposed method surpasses several state-of-the-art NR-IQA approaches in both performance and computational efficiency.