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Full reference image quality assessment based on dual-space multi-feature fusion

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Abstract

At present, the majority of techniques for assessing image quality are limited to extracting features from an image in a single space. This paper proposes a new dual-space multi-feature fusion based method for full-reference image quality assessment. This method involves simultaneously extracting features from both the YIQ and L*a*b* color spaces. First, we extract the luminance, slope, chroma, and gradient features in the spatial domain of the image to describe the salient differences in the image. Second, based on contrast sensitivity characteristics, we extract spatial frequency features in the spatial domain of the image to represent frequency differences in the image. Next, merge the features extracted in the dual space to construct a quality perception feature vector. Finally, the feature vector is input into the Random Forest model for regression prediction to obtain the predicted score of the image. Many experiments have been carried out on the four public datasets, and contrasted with other methods. The experimental confirm that the proposed method predicts image quality more accurately. The MATLAB source code and dataset of this paper will be published on GitHub, and the corresponding author can be contacted if necessary.

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Data availability

All data generated or analyses during this study are included in this published paper. The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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Acknowledgements

This work is supported by Science and Technology Research Project of Jiangxi Provincial Department of Education (GJJ2200529).

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Authors and Affiliations

  1. School of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang, China

    Xinrong Wu & Zhiming Shi

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  1. Xinrong Wu
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Contributions

X.W wrote the main manuscript text, Z.S provide data support

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Correspondence to Zhiming Shi.

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The main content of this paper is the study of IQA methods, and there is very close relevance between the topic of the revised manuscript and the scope of the Computer Graphics Society’s Journals.

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Communicated by Q. Shen.

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Wu, X., Shi, Z. Full reference image quality assessment based on dual-space multi-feature fusion. Multimedia Systems 30, 151 (2024). https://doi.org/10.1007/s00530-024-01353-5

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