No-reference Image Quality Assessment based on Singular Value Decomposition without Learning

Abstract

Recently no-reference image quality assessment (NR-IQA) methods take advantages of machine learning techniques. However, machine learning approaches need a number of human scored images and cause database dependency. In this paper, we propose a simple NR-IQA method that can estimate quality of distorted images without learning, producing comparable performance to learning based approaches. We employ singular value decomposition (SVD) since we have observed that singular values are commonly affected by various distortions. In detail, a decreasing rate of singular values is highly correlated to a degree of distortions regardless of their type. From the observation, our approach utilizes the decreasing rate of singular values to model a simple and reliable NR-IQA method. Experimental results show that the proposed method has reasonably high correlation to human scores. And the proposed method can secure simplicity and database independence.