Intensity-Sensitive Similarity Indexes for Image Quality Assessment

TL;DR

This paper introduces two new intensity-sensitive image quality assessment methods tailored for low-information images, outperforming traditional SSIM in high-intensity regions and matching SSIM performance in low-intensity areas, with applications demonstrated across various image types.

Contribution

The paper proposes novel IQA methods that enhance sensitivity to intensity variations in low-information images, addressing limitations of existing SSIM-based metrics.

Findings

New IQA methods outperform SSIM in high-intensity regions.

Methods perform comparably to SSIM in low-intensity regions.

Applications include natural, medical, and astronomical images.

Abstract

The importance of Image quality assessment (IQA) is ever increasing due to the fast paced advances in imaging technology and computer vision. Among the numerous IQA methods, Structural SIMilarity (SSIM) index and its variants are better matched to the perceived quality of the human visual system. However, SSIM methods are insufficiently sensitive, when images contain low information, where the important information only occupies a low proportion of the image while most of the image is noise-like, which is common in scientific data. Therefore, we propose two new IQA methods, InTensity Weighted SSIM index and Low-Information Similarity Index, for such low information images. In addition, auxiliary indexes are proposed to assist with the assessment. The application of these new IQA methods to natural images and field-specific images, such as radio astronomical images, medical images, and remote sensing images, are also demonstrated. The results show that our IQA methods perform better than state-of-the-art SSIM methods for differences in high-intensity parts of the input images and have similar performance to that of the original and gradient-based SSIM for differences in low-intensity parts. Different similarity indexes are suitable for different applications, which we demonstrate in our results.