Abstract
It is a challenging work to find an efficient metric of image quality assessment, which is an important problem for many image processing tasks. In this paper, we propose a novel wavelet-based directional structural distortion model for image quality assessment, which explores the geometric structural features of natural image. The experimental results upon image database show that our proposed method is in accordance with characteristic of human visual system and has better consistency with the subjective assessment of human beings than current image quality assessment algorithms.
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Guangquan Cheng is a PhD student at the National University of Defense Technology. He received his BS and MS degrees in computational mathematics from the National University of Defense Technology in 2003 and 2005, respectively. Now he is also a joint PhD student at Department of Mathematics and Center for Wavelets, Approximation and Information Processing, National University of Singapore. His current research interests include.
Lizhi Cheng is a professor at the National University of Defense Technology. He received his BS degree from the Hunan Normal University in 1984, and MS and PhD degrees from the National University of Defense Technology in 1988, and 2002, respectively. His current research interests include image and video processing, computational harmonic analysis.
Ying Li is a senior lecturer and PhD student at the National University of Defense Technology. She He received his BS degree and MS degrees in mathematics from the Xiangtang University in 1999 and 2001, respectively. Her current research interests include image processing.
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Cheng, G., Cheng, L. & Li, Y. Wavelet-based directional structural distortion model for image quality assessment. Pattern Recognit. Image Anal. 20, 286–292 (2010). https://doi.org/10.1134/S1054661810030041
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DOI: https://doi.org/10.1134/S1054661810030041