A New Spectral Compression Method based on the Minimization of the Color Difference and Root Mean Square Error

Abstract

This paper describes two new interim connection spaces (ICSs), P-2OC and P-3OC, for spectral image compression and reconstruction. For this type of ICS, the weighting table or compression matrix H is modelled as a variable. The associated reconstruction matrix N is chosen as the Weiner estimation matrix. The objective function f(H) is the combination of the averages of the CIEDE2000 color difference (ΔE00) and the root mean square error (RMSE) between the original and reconstructed reflectance values. Hence, the compression matrix H is determined by solving the nonlinear minimization problem based on the Munsell training dataset. The proposed ICSs, P-2OC and P-3OC, were tested and compared, respectively, with ICS-2SI and ICS-3SI developed by Zhang et al. (JOSAA, Vol. 29, pp. 1027–1034) in 2012 using the NCS dataset, and 2 spectral images. Performance tests showed that the proposed P-2OC and P-3OC ICSs are better than the ICS-2SI and ICS-3SI ICSs, respectively, in terms of RMSE, goodness of fit coefficient (GFC), and ΔE00 under CIE illuminants D65, A, C and F11. Therefore, it is expected that the P-2OC and P-3OC ICSs can find applications in spectral image compression and cross-media reproduction.