Content-partitioned structural similarity index for image quality assessment

Abstract

The assessment of image quality is important in numerous image processing applications. Two prominent examples, the Structural Similarity Image (SSIM) index and Multi-scale Structural Similarity (MS-SSIM) operate under the assumption that human visual perception is highly adapted for extracting structural information from a scene. Results in large human studies have shown that these quality indices perform very well relative to other methods. However, the performance of SSIM and other Image Quality Assessment (IQA) algorithms are less effective when used to rate blurred and noisy images. We address this defect by considering a four-component image model that classifies image local regions according to edge and smoothness properties. In our approach, SSIM scores are weighted by region type, leading to modified versions of (G-)SSIM and MS-(G-)SSIM, called four-component (G-)SSIM (4-(G-)SSIM) and four-component MS-(G-)SSIM (4-MS-(G-)SSIM). Our experimental results show that our new approach provides results that are highly consistent with human subjective judgment of the quality of blurred and noisy images, and also deliver better overall performance than (G-)SSIM and MS-(G-)SSIM on the LIVE Image Quality Assessment Database.