Comparison of minimum spanning tree reordering with bias-adjusted reordering for lossless compression of 3D ultraspectral sounder data

Alok Ahuja; Bormin Huang; Mitchell D. Goldberg

doi:10.1117/12.666799

8 May 2006 Comparison of minimum spanning tree reordering with bias-adjusted reordering for lossless compression of 3D ultraspectral sounder data

Alok Ahuja, Bormin Huang, Mitchell D. Goldberg

Author Affiliations +

Proceedings Volume 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII; 62332D (2006) https://doi.org/10.1117/12.666799
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

The ultraspectral sounder data features strong correlations in disjoint spectral regions due to the same type of absorbing gases. This paper compares the compression performance of two robust data preprocessing schemes, namely Bias-Adjusted reordering (BAR) and Minimum Spanning Tree (MST) reordering, in the context of entropy coding. Both schemes can take advantage of the strong correlations for achieving higher compression gains. The compression methods consist of the BAR or MST preprocessing schemes followed by linear prediction with context-free or context-based arithmetic coding (AC). Compression experiments on the NASA AIRS ultraspectral sounder data set show that MST without bias-adjustment produces lower compression ratios than BAR and bias-adjusted MST for both context-free and context-based AC. Biasadjusted MST outperforms BAR for context-free arithmetic coding, whereas BAR outperforms MST for context-based arithmetic coding. BAR with context-based AC yields the highest average compression ratios in comparison to MST with context-free or context-based AC.

Citation Download Citation

Alok Ahuja, Bormin Huang, and Mitchell D. Goldberg "Comparison of minimum spanning tree reordering with bias-adjusted reordering for lossless compression of 3D ultraspectral sounder data", Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 62332D (8 May 2006); https://doi.org/10.1117/12.666799

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