Adaptive No-Reference Image Quality Assessment Based on Multi-Scale Pyramid Pooling

In the Image Quality Assessment (IQA), no-reference quality assessment methods have demonstrated significant application value and development potential for managing distorted images in real-world scenarios. However, real-world distorted images exhibit high diversity and complexity, which make designing relevant evaluation algorithms more difficult. In recent years, deep learning technology has achieved remarkable success in various subfields of image processing, such as image classification, object detection, and image segmentation. These advancements have motivated researchers to introduce Deep Neural Network (DNN) technology into IQA. Owing to their outstanding feature extraction and learning capabilities, DNNs have provided innovative solutions and made significant progress in the quality assessment of distorted images in real-world environments. Despite these advancements, existing methods still have certain limitations in describing the image quality in real-world scenes, particularly when handling diverse image content. Additionally, many DNN-based IQA methods require the input images to be scaled or cropped to a fixed resolution, which often compromises the original structure and content of the images, thereby affecting the accuracy and generalizability of the quality assessment. To address these issues, this paper proposes an adaptive No-Reference IQA (NR-IQA) method based on Multi-Scale Pyramid Pooling (MSPP-IQA). This method does not require preprocessing and can assess the quality of an image in its original size. Furthermore, by introducing content understanding and attention modules, MSPP-IQA can mimic the working principles of the Human Visual System (HVS), simultaneously perceiving global high-level and local low-level features. Experimental results demonstrate that, compared to current mainstream methods, MSPP-IQA performs well on both real-world and synthetic distortion datasets. These results validate the effectiveness and superiority of MSPP-IQA in addressing the challenges in assessing the quality of real-world distorted images.