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Proceedings Paper

A new quality assessment index for compressed remote sensing image
Author(s): Liang Zhai; Xinming Tang; Guo Zhang
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Paper Abstract

Quality assessment for remote sensing image compression is of great significance in many practical applications. A comprehensive index based on muti-dimensional structure model was designed for image compression assessment, which consists of gray character distortion dimension, texture distortion dimension, loss of correlation dimension. Based on this model, a new comprehensive image quality index-Q was proposed. In order to assess the agreement between our comprehensive image quality index Q and human visual perception, we conducted subjective experiments in which observers ranked reconstructed images according to perceived distortion. For comparison, PSNR is introduced. The experiments showed that Q had a better consistency with subjective assessment results than conventional PSNR.

Paper Details

Date Published: 5 September 2008
PDF: 8 pages
Proc. SPIE 7075, Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications XI, 70750K (5 September 2008); doi: 10.1117/12.798834
Show Author Affiliations
Liang Zhai, Chinese Academy of Surveying and Mapping (China)
Xinming Tang, Chinese Academy of Surveying and Mapping (China)
Guo Zhang, Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 7075:
Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications XI
Mark S. Schmalz; Gerhard X. Ritter; Junior Barrera; Jaakko T. Astola, Editor(s)

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