Analysis of Spiht Algorithm for Satellite Image Compression

Wavelets offer an elegant technique for representing the levels of details present in an image. When an image is decomposed using wavelets, the high pass component carry less information, and vice-versa. The possibility of elimination of the high pass components gives higher compression ratio in the case of wavelet based image compression. To achieve higher compression ratio, various coding schemes have been used. Some of the well known coding algorithms are EZW (Embedded Zero-tree Wavelet), SPIHT (Set Partitioning in Hierarchical Tree) and EBCOT (Embedded Block Coding with Optimal Truncation). SPIHT has been one of the popular schemes used for image compression. In this paper the performance of the SPIHT (Set Partitioning in Hierarchical Trees) compression technique for satellite images are studied. The satellite rural and urban images have been used for the present analysis. The standard Lena image is used for the purpose of comparison. For a given compression ratio, the PSNR (peak signal to noise ratio) values are computed to evaluate the quality of the reconstructed image. The analysis carried out clearly suggests that the PSNR values increases with the level of decomposition. For the satellite images the PSNR values achievable are less compared to that of Standard Lena Image and the SPIHT Algorithm are better suited for compression of Satellite urban Images.