Enhancing image quality assessment with ResNet50 and global average pooling

This paper focuses on traditional deep learning-based no-reference (or reference-based) image quality assessment (IQA) methods, enhancing them from the perspective of image feature extraction. It replaces the VGG16 network with the ResNet50 network for feature extraction and uses the Global Average Pooling (GAP) layer instead of FC512. Subsequently, it computes the weighted average of quality scores for different parts of the image to obtain the overall image quality. Specifically, the paper first preprocesses images by cropping, flipping, mirroring, tilting, and other methods to expand the image dataset and make it more reflective of real-world scenarios. Then, it utilizes the ResNet50 network for feature extraction, showing superior performance compared to the VGG network. Finally, a weighted pooling method is employed to derive the ultimate image score. On the TID2013 and CLIVE datasets, the Pearson Linear Correlation Coefficient (PLCC) values are 0.877 and 0.7095, respectively, while the Spearman Rank Order Correlation Coefficient (SROCC) values are 0.8510 and 0.6956. These values surpass those obtained using traditional algorithms like SSIM and GSMD, indicating the superior predictive performance of the new algorithm. Moreover, the proposed algorithm demonstrates advantages in speed and accuracy, meeting real-time application requirements more effectively.