A class-conditioned lossless wavelet-based predictive multispectral image compressor

We present a nonlinear lossless compressor designed for multispectral images consisting of few bands and having greater spatial than spectral correlation. Our compressor is based on a 2-D integer wavelet transform that reduces spatial correlation. Different models for the statistical dependences of...

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Detalles Bibliográficos
Autores principales: Ruedin, A., Acevedo, D.
Formato: JOUR
Materias:
Classification
Lossless compression
Multispectral
Prediction
Wavelet coefficients
Arithmetic coders
Band ordering
Detail coefficients
Integer wavelet transforms
Lossless
Multi-spectral
Multispectral images
New mechanisms
Prediction errors
Random access
Spatial correlations
Spectral correlation
Statistical dependence
Volumetric data
Compressors
Forecasting
Image compression
Volumetric analysis
Wavelet transforms
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_1545598X_v7_n1_p166_Ruedin
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
Descripción
Sumario:We present a nonlinear lossless compressor designed for multispectral images consisting of few bands and having greater spatial than spectral correlation. Our compressor is based on a 2-D integer wavelet transform that reduces spatial correlation. Different models for the statistical dependences of wavelet detail coefficients are analyzed and tested to perform linear inter/intraband predictions. Band, class, scale, and orientation are used as conditioning contexts to calculate predictions, as well as to encode prediction errors with an adaptive arithmetic coder. A new mechanism is proposed for band ordering, based on wavelet fine detail coefficients. Our compressor CLWP outperforms state-of-the-art lossless compressors. It has random access capability and can be applied to compress volumetric data having similar characteristics. © 2009 IEEE.

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