A Nonlinear Quantitative Measure for Accessing the Performances of Hyperspectral Image Compression

Image compression is one of the most important processing techniques in hyperspectral image applications. A nonlinear quantitative measure for objectively accessing the performances of image compression techniques is proposed based on the scheme that image compression methods with better performances will maintain more information when compressing images with the same compression ratio, and the information correlation between source image and compressed image is stronger. using the nonlinear correlation coefficient (NCC) to accurately describe the general relationship between the source image and the compressed image, the performances of different image compression methods can be directly compared. The proposed nonlinear correlation based measure can be considered as an objective enforcement and complementary to peak signal-to-noise ratio (PSNR) and mean square error (MSE) measure, which are widely used and based on the differences between source image and compressed image