Simulating Visual Mechanisms by Sequential Spatial-Channel Attention for Image Quality Assessment

Abstract

As a subjective concept, image quality assessment (IQA) is significantly affected by perceptual mechanisms. Two mutually influenced mechanisms, namely spatial attention and contrast sensitivity, are particularly important for IQA. This paper aims to explore a deep learning approach based on transformer for the two mechanisms. By converting contrast sensitivity to attention representation, a unified multi-head attention module is performed on spatial and channel features in transformer encoder to simulate the two mechanisms in IQA. Sequential spatial-channel self-attention is proposed to avoid expensive computation in the classical Transformer model. In addition, as image rescaling can potentially affect perceived quality, zero-padding and masking with assigning special attention weights are performed to handle arbitrary image resolutions without requiring image rescaling. The evaluation results on publicly available large-scale IQA databases have demonstrated outstanding performance and generalization of the proposed IQA model.