Blind Image Quality Assessment by Gaussian Mixture Distribution

In the field of image quality assessment (IQA), researchers have been studying the mean opinion score (MOS) of image quality for decades. They focus on developing IQA methods with the help of MOS without using the potential of the distribution of opinion scores (DOS). We find that the Gaussian mixture distribution (GMD) can more accurately describe the DOS of image quality on SJTU IQSD and KonIQ-10K databases compared to some traditional distributions. Therefore, this paper proposes a blind IQA method that predicts the MOS of image quality by learning the GMD-based image quality. The proposed method consists of a visual feature learning module and a GMD learning module. The visual feature learning module uses a multi-stage Swin Transformer model and a CLIP feature extractor to extract visual features from an image. The GMD learning module then maps the extracted visual features to the GMD-based image quality using a mixture density network, where the mean of the GMD represents the MOS of image quality. We not only use the MOS of image quality to train the proposed method, but also employ the DOS of image quality for auxiliary training to improve the prediction performance of the proposed method. To address the lack of DOS in some existing IQA databases, we introduce a pseudo DOS generation strategy to generate the DOS of image quality for training, which significantly improves the applicability of the proposed method. Numerous analyses show that the proposed method is superior to most state-of-the-art IQA methods in predicting both the MOS and the DOS, thus facilitating a deeper investigation into the DOS of image quality in IQA.