Image quality assessment with manifold and machine learning

Abstract

A crucial step in image compression is the evaluation of its performance, and more precisely the available way to measure the final quality of the compressed image. In this paper, a machine learning expert, providing a final class number is designed. The quality measure is based on a learned classification process in order to respect the one of human observers. Instead of computing a final note, our method classifies the quality using the quality scale recommended by the UIT. This quality scale contains 5 ranks ordered from 1 (the worst quality) to 5 (the best quality). This was done constructing a vector containing many visual attributes. Finally, the final features vector contains more than 40 attibutes.Unfortunatley, no study about the existing interactions between the used visual attributes has been done. A feature selection algorithm could be interesting but the selection is highly related to the further used classifier. Therefore, we prefer to perform dimensionality reduction instead of feature selection. Manifold Learning methods are used to provide a low-dimensional new representation from the initial high dimensional feature space.The classification process is performed on this new low-dimensional representation of the images. Obtained results are compared to the one obtained without applying the dimension reduction process to judge the efficiency of the method.

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

“…However, the subjective method is too complex, time‐consuming and not suitable in real‐time applications. In addition, the resulted assessment from the subjective method might vary significantly due to many factors such as, the lighting condition and the choice of subjects [5]. Therefore, the faster alternative way is the objective method that can predict the quality of distorted image automatically with respect to human perception.…”