A new quality assessment index for compressed remote sensing image

Liang Zhai; Xinming Tang; Guo Zhang

doi:10.1117/12.798834

5 September 2008 A new quality assessment index for compressed remote sensing image

Liang Zhai, Xinming Tang, Guo Zhang

Proceedings Volume 7075, Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications XI; 70750K (2008) https://doi.org/10.1117/12.798834
Event: Optical Engineering + Applications, 2008, San Diego, California, United States

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.

Citation Download Citation

Liang Zhai, Xinming Tang, and Guo Zhang "A new quality assessment index for compressed remote sensing image", Proc. SPIE 7075, Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications XI, 70750K (5 September 2008); https://doi.org/10.1117/12.798834

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