A No-Reference Screen Content Image Quality Assessment Method Based on Regional Distortion Perception

Screen Content Image Quality Assessment (SCIQA) plays a significant role in computer vision. Current No-Reference SCIQA (NR-SCIQA) methods often struggle to effectively represent both textual and pictorial regions in Screen Content Images (SCIs), which hinders accurate quality prediction. This paper presents a NR-SCIQA method based on regional distortion perception. A key contribution is that we propose two feature perception modules: Textual Feature Perception Module (TFPM) and Pictorial Feature Perception Module (PFPM). The TFPM uses Discrete Wavelet Transform (DWT) and Laplace operators to refine structural and edge information, and the PFPM combines depthwise separable convolution with Discrete Cosine Transform (DCT) for joint spatial-frequency feature extraction. Another contribution is the Text-Picture Feature Fusion Module (TPFFM) which incorporates attention and adaptive mechanisms to integrate perceived features from both regions. Experimental results on the SIQAD and SCID datasets show that our proposed method significantly outperforms existing NR-SCIQA methods.