No-reference image quality metric based on multiple deep belief networks

The last decade has witnessed great advances in digital images. These images are subjected to many processing stages during storing, transmitting, or sharing over a network connection. Unfortunately, these processing stages could potentially add visual degradation to original image. These degradations reduce the perceived visual quality which leads to an unsatisfactory experience for human viewers. Therefore, image quality assessment (IQA) has become a topic of high interest and intense research over the last decade. This study mainly focuses on the most challenging category of IQA general-purpose No-Reference Image Quality Assessment (NR-IQA), where the goal is to assess the quality of images without information about the reference images and without prior knowledge about the types of distortions in the tested image. A novel NR-IQA approach is presented, by utilizing multiple deep belief networks (DBNs) with multiple regression models. It consists of four DBNs. Each DBN is associated with one type of distortion. The authors have evaluated the performance of the proposed and some existing models on a fair basis. The obtained results show that their model gives better results and yield a significant improvement.