Abstract:


Abstract: Authentic distortion-oriented no-reference image quality assessment is still challenging due to the variety of distortion types， complexity， and localized noise. As various regions within an image contribute differently to the overall perception of quality， it is problematic to characterize image distortion solely through global feature representation. A no-reference image quality assessment method with multi-scale interaction and cross-channel attention is proposed. Firstly， multi-scale interaction blocks are constructed based on the Transformer that utilizes a convolutional attention mechanism， to enhance the exchange of local and global information among features of varying scales. Meanwhile， a multi-scale dynamic connectivity approach is designed to avoid underutilization or overutilization of multi-scale features. Secondly， a cross-channel attention module is employed to further facilitate the information complementarity and integration of cross-scale features at the channel level. Finally， quality scores are obtained through regression. The proposed method is evaluated against widely recognized no-reference image quality assessment methods across five publicly available image quality evaluation datasets， and the test results demonstrate a potential improvement of 2% in performance compared to the best-performing method. Furthermore， the number of parameter is decreased by 3% and 74%， while the computational complexity by 11% and 74%， respectively， in comparison to the methods based on self-attention and transpositional attention mechanisms. The result demonstrates that the proposed approach achieves advanced quality assessment performance and strong generalization capabilities while keeping the complexity low.

Key words: Key words: no-reference image quality assessment, authentic distortion, multi-scale interaction, attention mechanism, Transformer