Domain Fingerprints for No-reference Image Quality Assessment

TL;DR

This paper introduces a novel no-reference image quality assessment method that leverages domain fingerprints to identify degradation sources and improve quality evaluation accuracy.

Contribution

It proposes the concept of domain fingerprint for NR-IQA and designs a domain-aware architecture for simultaneous degradation source identification and quality assessment.

Findings

DA-IQA outperforms most state-of-the-art NR-IQA methods.

The domain fingerprint effectively characterizes image degradation.

The approach improves accuracy in image quality assessment.

Abstract

Human fingerprints are detailed and nearly unique markers of human identity. Such a unique and stable fingerprint is also left on each acquired image. It can reveal how an image was degraded during the image acquisition procedure and thus is closely related to the quality of an image. In this work, we propose a new no-reference image quality assessment (NR-IQA) approach called domain-aware IQA (DA-IQA), which for the first time introduces the concept of domain fingerprint to the NR-IQA field. The domain fingerprint of an image is learned from image collections of different degradations and then used as the unique characteristics to identify the degradation sources and assess the quality of the image. To this end, we design a new domain-aware architecture, which enables simultaneous determination of both the distortion sources and the quality of an image. With the distortion in an image better characterized, the image quality can be more accurately assessed, as verified by extensive experiments, which show that the proposed DA-IQA performs better than almost all the compared state-of-the-art NR-IQA methods.