Comparison-based Image Quality Assessment for Parameter Selection

TL;DR

This paper introduces a comparison-based image quality assessment (C-IQA) method that leverages multiple distorted images for better parameter selection, outperforming existing no-reference IQA methods and significantly reducing computational costs.

Contribution

The paper proposes a novel comparison-based IQA framework that combines advantages of FR and NR IQA, applicable in parameter selection scenarios without needing the original image.

Findings

C-IQA outperforms state-of-the-art NR-IQA methods in parameter selection.

Using C-IQA reduces iterative image reconstruction computation by up to 80%.

C-IQA is effective in practical applications with multiple distorted images.

Abstract

Image quality assessment (IQA) is traditionally classified into full-reference (FR) IQA and no-reference (NR) IQA according to whether the original image is required. Although NR-IQA is widely used in practical applications, room for improvement still remains because of the lack of the reference image. Inspired by the fact that in many applications, such as parameter selection, a series of distorted images are available, the authors propose a novel comparison-based image quality assessment (C-IQA) method. The new comparison-based framework parallels FR-IQA by requiring two input images, and resembles NR-IQA by not using the original image. As a result, the new comparison-based approach has more application scenarios than FR-IQA does, and takes greater advantage of the accessible information than the traditional single-input NR-IQA does. Further, C-IQA is compared with other state-of-the-art NR-IQA methods on two widely used IQA databases. Experimental results show that C-IQA outperforms the other NR-IQA methods for parameter selection, and the parameter trimming framework combined with C-IQA saves the computation of iterative image reconstruction up to 80%.