A Shift-insensitive Full Reference Image Quality Assessment Model Based on Quadratic Sum of Gradient Magnitude and LOG signals

TL;DR

This paper introduces a shift-insensitive full reference image quality assessment model based on the quadratic sum of gradient magnitude and LOG signals, demonstrating robustness and simplicity across diverse distortions and shifts.

Contribution

The paper proposes a novel shift-insensitive FR-IQA model combining GM and LOG signals, outperforming existing metrics like CW-SSIM in robustness and efficiency.

Findings

Works well on large-scale subjective IQA databases.

Outperforms CW-SSIM in shift-insensitive scenarios.

Maintains state-of-the-art performance across various distortions.

Abstract

Image quality assessment that aims at estimating the subject quality of images, builds models to evaluate the perceptual quality of the image in different applications. Based on the fact that the human visual system (HVS) is highly sensitive to structural information, the edge information extraction is widely applied in different IQA metrics. According to previous studies, the image gradient magnitude (GM) and the Laplacian of Gaussian (LOG) operator are two efficient structural features in IQA tasks. However, most of the IQA metrics achieve good performance only when the distorted image is totally registered with the reference image, but fail to perform on images with small translations. In this paper, we propose an FR-IQA model with the quadratic sum of the GM and the LOG signals, which obtains good performance in image quality estimation considering shift-insensitive property for not well-registered reference and distortion image pairs. Experimental results show that the proposed model works robustly on three large scale subjective IQA databases which contain a variety of distortion types and levels, and stays in the state-of-the-art FR-IQA models no matter for single distortion type or across whole database. Furthermore, we validated that the proposed metric performs better with shift-insensitive property compared with the CW-SSIM metric that is considered to be shift-insensitive IQA so far. Meanwhile, the proposed model is much simple than the CW-SSIM, which is efficient for applications.