No-reference transform-based quality assessment for imaging applications

V. Voronin; M. Zhdanova; A. Zelensky; S. Agaian

doi:10.1117/12.2588000

12 April 2021 No-reference transform-based quality assessment for imaging applications

V. Voronin, M. Zhdanova, A. Zelensky, S. Agaian

Author Affiliations +

Proceedings Volume 11734, Multimodal Image Exploitation and Learning 2021; 1173405 (2021) https://doi.org/10.1117/12.2588000
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

This paper aims to introduce a new transform domain no-reference (NR) image quality assessment that predicts the perceptual quality for improving imaging systems performance. The main idea is that enhancing the contrast of diverse real-world digital images by creating more high-frequency content in the improved image than the original image. The proposed measure uses different fast orthogonal transforms, such as Fourier and Fibonacci. A generalized transform-based model of local transform-based coefficients is derived and transformed the model parameters into features used for perceptual image quality score prediction. To test the performance of the proposed algorithm, we use the well-known and publicly available database TID2008. The Pearson correlation coefficient is utilized to measure and compare the proposed quality measures performance with state-of-the-art approaches.

Conference Presentation

Citation Download Citation

V. Voronin, M. Zhdanova, A. Zelensky, and S. Agaian "No-reference transform-based quality assessment for imaging applications", Proc. SPIE 11734, Multimodal Image Exploitation and Learning 2021, 1173405 (12 April 2021); https://doi.org/10.1117/12.2588000

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