A Visual Saliency-Based Neural Network Architecture for No-Reference Image Quality Assessment

Abstract

Deep learning has recently been used to study blind image quality assessment (BIQA) in great detail. Yet, the scarcity of high-quality algorithms prevents from developing them further and being used in a real-time scenario. Patch-based techniques have been used to forecast the quality of an image, but they typically award the picture quality score to an individual patch of the image. As a result, there would be a lot of misleading scores coming from patches. Some regions of the image are important and can contribute highly toward the right prediction of its quality. To prevent outlier regions, we suggest a technique with a visual saliency module which allows the only important region to bypass to the neural network and allows the network to only learn the important information required to predict the quality. The neural network architecture used in this study is Inception-ResNet-v2. We assess the proposed strategy using a benchmark database (KADID-10k) to show its efficacy. The outcome demonstrates better performance compared with certain popular no-reference IQA (NR-IQA) and full-reference IQA (FR-IQA) approaches. This technique is intended to be utilized to estimate the quality of an image being acquired in real time from drone imagery.