Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
