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. Present days Convolutional Neural Networks (CNN) gained its importance in all computer vision applications and also in image quality assessment domain. Because of these characteristics of gaussian derivative that plays a major role in assessing image quality, this work is carried by combining these characteristics with the CNNs to better extract the 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) has been 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 convolutional block attention module (CBAM) a module designed for the interpretation of images and local importance pooling (LIP) proposed method for No-reference image quality assessment has demonstrated improved accuracy, generalization, and efficiency compared to conventional (or) traditional IQA methods.