NR-IQA with Gaussian Derivative Filter, Convolutional Block Attention Module, and Spatial Pyramid Pooling

Gaussian derivatives offer valuable capabilities for analyzing image characteristics such as structure, edges, texture, and features, which are essential aspects in the assessment of image quality. Recently, Convolutional Neural Networks (CNNs) have gained in importance in computer vision applications and also in the image quality assessment domain. Due to the characteristics of Gaussian derivatives that perform a major role in assessing image quality, this work seeks to combine these characteristics with CNNs to better extract features for assessing the quality of an image. While CNNs have demonstrated their ability to handle distortion effectively, they are limited in their capacity to capture features at different scales, making them inadequate in dealing with significant variations in object size. Consequently, the concept of spatial pyramid pooling (SPP) is introduced to address this limitation in image quality assessment (IQA). SPP involves pooling the spatial feature maps from the highest convolutional layers into a feature representation of fixed length. Additionally, through the utilization of a convolutional block attention module (CBAM), a module designed for the interpretation of images, and local importance pooling (LIP), we propose method for no-reference image quality assessment has demonstrated improved accuracy, generalization, and efficiency on the IVC database compared to conventional or traditional IQA methods, while achieving competitive performance on other datasets.