Dehazed Image Quality Evaluation: From Partial Discrepancy to Blind Perception

TL;DR

This paper introduces a novel blind image quality assessment method for dehazed images, leveraging hierarchical features inspired by human perception, and demonstrates its superiority over existing models.

Contribution

The paper proposes a new no-reference dehazed image quality metric based on partial discrepancy, extending a reduced-reference approach, with improved performance on quality databases.

Findings

Outperforms state-of-the-art quality assessment models

Effectively guides parameter tuning for dehazing algorithms

Works without reference images, relying on perceptual features

Abstract

Image dehazing aims to restore spatial details from hazy images. There have emerged a number of image dehazing algorithms, designed to increase the visibility of those hazy images. However, much less work has been focused on evaluating the visual quality of dehazed images. In this paper, we propose a Reduced-Reference dehazed image quality evaluation approach based on Partial Discrepancy (RRPD) and then extend it to a No-Reference quality assessment metric with Blind Perception (NRBP). Specifically, inspired by the hierarchical characteristics of the human perceiving dehazed images, we introduce three groups of features: luminance discrimination, color appearance, and overall naturalness. In the proposed RRPD, the combined distance between a set of sender and receiver features is adopted to quantify the perceptually dehazed image quality. By integrating global and local channels from dehazed images, the RRPD is converted to NRBP which does not rely on any information from the references. Extensive experiment results on several dehazed image quality databases demonstrate that our proposed methods outperform state-of-the-art full-reference, reduced-reference, and no-reference quality assessment models. Furthermore, we show that the proposed dehazed image quality evaluation methods can be effectively applied to tune parameters for potential image dehazing algorithms.