REGION-ORIENTED COMPRESSION OF MULTISPECTRAL IMAGES BY SHAPE-ADAPTIVE WAVELET TRANSFORM AND SPIHT (WA-L3)

Author(s) : Marco Cagnazzo (Università Federico II di Napoli, Italy) Giovanni Poggi (Università Federico II di Napoli, Italy) Luisa Verdoliva (Università Federico II di Napoli, Italy) Andrea Zinicola (LABCOM - CNIT, Italy)

Abstract : We present a new technique for the compression of remote-sensing hyperspectral images based on wavelet transform and zerotree coding of coefficients. In order to improve encoding efficiency, the image is first segmented in a small number of regions with homogeneous texture. Then, a shape-adaptive wavelet transform is carried out on each region, and the resulting coefficients are finally encoded by a shape-adaptive version of SPIHT. Thanks to the segmentation map (sent as a side information) region boundaries are faithfully preserved, and selective encoding strategies can be easily implemented. Moreover, also the region textures can be represented more efficiently, if they are homogeneous enough.

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