A new quality assessment index for compressed remote sensing image
Liang Zhai, Xinming Tang, Guo Zhang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
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.