A Full-Reference Image Quality Assessment Method with Saliency and Error Feature Fusion

Ai, Da; Liu, Yunhong; Yang, Yong; Lu, Mingyue; Liu, Ying; Ling, Nam

doi:10.1109/iscas48785.2022.9937794

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…Later, this approach was further developed in [ 16 ] by replacing the HIK in comparing convolutional feature maps with a traditional image similarity metric. In [ 49 ], the authors used the error map calculated between the reference and distorted images and the subjective saliencies of the distorted images to train a CNN for perceptual image quality estimation.…”

Section: Related Workmentioning

confidence: 99%

An Optimization-Based Family of Predictive, Fusion-Based Models for Full-Reference Image Quality Assessment

Varga

2023

J. Imaging

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Given the reference (distortion-free) image, full-reference image quality assessment (FR-IQA) algorithms seek to assess the perceptual quality of the test image. Over the years, many effective, hand-crafted FR-IQA metrics have been proposed in the literature. In this work, we present a novel framework for FR-IQA that combines multiple metrics and tries to leverage the strength of each by formulating FR-IQA as an optimization problem. Following the idea of other fusion-based metrics, the perceptual quality of a test image is defined as the weighted product of several already existing, hand-crafted FR-IQA metrics. Unlike other methods, the weights are determined in an optimization-based framework and the objective function is defined to maximize the correlation and minimize the root mean square error between the predicted and ground-truth quality scores. The obtained metrics are evaluated on four popular benchmark IQA databases and compared to the state of the art. This comparison has revealed that the compiled fusion-based metrics are able to outperform other competing algorithms, including deep learning-based ones.

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Section: Related Workmentioning

confidence: 99%

An Optimization-Based Family of Predictive, Fusion-Based Models for Full-Reference Image Quality Assessment

Varga

2023

J. Imaging

View full text Add to dashboard Cite

show abstract

A Full-Reference Image Quality Assessment Method with Saliency and Error Feature Fusion

Cited by 1 publication

References 17 publications

An Optimization-Based Family of Predictive, Fusion-Based Models for Full-Reference Image Quality Assessment

An Optimization-Based Family of Predictive, Fusion-Based Models for Full-Reference Image Quality Assessment

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