Lossy compression algorithm of remotely sensed multispectral images based on YCrCb transform and IWT

According to the correlated characteristic of remotely sensed multispectral images (RSMI) in the spectral and spatial domains, an effective and lossy YCrCb+IWT compression algorithm is proposed. The algorithm combines YCrCb transform with integer wavelet transform (IWT) to compress data, and data redundance of spectral and spatial domains is removed respectively. The important degree of the each subband is determined according to the energy of the each subband. Furthermore, each subband is quantified using adaptive threshold according to their important degree, then fixed bit-plane coding and Run Length Encoding are individually used to the quantified data of every subband and important graph. When implementing compression algorithm, in order to ensure better quality of reconstructed image, the compression with little distortion is utilized for luminance information Y. Simultaneously, in order to obtain higher compression ratio, the compression with biggish distortion is carried out for chrominance information Cr and Cb. The simulation experiment indicates that this algorithm can receive good compression performance of average CR >= 7 and average PSNR >= 33dB for RSMI of different content and texture. In addition, the algorithm requires small storage and is easy to be realized in hardware, so it is suitable for space-borne application.