DTSN: No-Reference Image Quality Assessment via Deformable Transformer and Semantic Network

Abstract

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Feature maps with varying resolutions usually serve different functions. High-resolution feature maps contain abundant texture and color information. Low-resolution feature maps provide significant semantic information. All of this information is crucial for Image Quality Assessment (IQA). Hence, it is challenging to evaluate an image’s quality using only one type of feature map. In this paper, a No-reference Image Quality Assessment (NR-IQA) based on Deformable Transformer and semantic network (DTSN) is proposed. DTSN efficiently learns distortion information across different feature layers in images. Simultaneously, it utilizes semantic information in images to identify which parts of an image significantly impact the image quality score. Experimental results indicate that excellent results can be achieved with only a few sampling points.