Multi-scale structural image quality assessment based on two-stage low-level features

Objective image quality assessment has been widely used in image processing for decades. Many researchers have been studying the objective quality assessment method based on human visual system. Recently, the single-scale feature-similarity index metric has been proposed to provide a good approximation to perceived image quality. However, this metric does not take into account the fact that features in a certain scale cannot reflect various distorted details in the image. To address this issue, this paper proposes a multi-scale structural image quality assessment based on two-stage low-level features, which supplies more flexible than previous single-scale method by incorporating the variations of viewing conditions and resolution. In this multi-scale framework, different weights are assigned to various scales with different levels of importance. Extensive experiments on the five public benchmark databases indicate that the proposed metric is more consistent with the subjective evaluations than all the other competing methods evaluated.