Hyperspectral Image Compression Method Using Linear Prediction and Arithmetic Coding

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Resource Overview

A hyperspectral image compression technique based on linear prediction and arithmetic coding, implementing efficient data reduction through predictive residual encoding and entropy compression algorithms. The method employs linear regression for spatial-spectral correlation modeling and adaptive arithmetic coding for optimal bit allocation.

Detailed Documentation

In hyperspectral image compression research, we propose a novel method combining linear prediction with arithmetic coding. This approach utilizes linear prediction to model spatial and spectral correlations in hyperspectral data, generating prediction residuals that are subsequently compressed using arithmetic coding. The implementation typically involves calculating prediction coefficients through least squares optimization, followed by context-adaptive arithmetic encoding of the residual values. This dual-process technique achieves high compression efficiency while maintaining reconstruction quality. We believe this methodology provides valuable reference for research and applications in hyperspectral image compression, particularly for implementations requiring balanced compression ratios and computational efficiency.

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