1. INTRODUCTION

1. LOSSLESS COMPRESSION STUDIES OF INTERFEROMETER DATA FOR NOAA GOES-R HYPERSPECTRAL ENVIRONMENTAL SUITE Bormin Huang1, Alok Ahuja1, Yagneswaran Sriraja1, Hung-Lung Huang1, Mitchell D. Goldberg2, Timothy J. Schmit2, and Roger W. Heymann2 1Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison, 2NOAA, National Environmental Satellite, Data, and Information Service 3. LOSSLESS COMPRESSION STUDIES CIMSS-Developed New Lossless Compression Methods 1. INTRODUCTION • Lossless PCA (Principal Component Analysis) compression: (Huang et al. 2004). • To support GOES-R data compression studies for the possible interferometer-type HES sounder, we investigate various lossless compression methods on the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounder Testbed-Interferometer (NAST-I) data. • These data compression techniques are applicable for GOES-R rebroadcast. • We show that an average lossless compression ratio of 5.10 is achievable for the NAST-I ultraspectral sounder data • CIMSS’sBias-Adjusted Reordering (BAR) data preprocessing scheme:(Huang et al. 2004) significantly improves the performance of existing state-of-the-art compression methods • 2D JPEG2000:A new International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) compression standard. Successor of the DCT (discrete cosine transform)-based JPEG algorithm. Block diagram of the JPEG2000 encoder • 2D Context-based Adaptive Lossless Image Codec (CALIC):Among the nine proposals in the initial ISO/JPEG evaluation in July 1995, CALIC was ranked first. It is considered the benchmark for lossless compression of continuous-tone images. • Predictive Partitioned Vector Quantization (PPVQ)(Huang et al. 2004) 2. NAST-I DATA FOR LOSSLESS COMPRESSION STUDIES • NAST-I flight instrument specifications • Aircraft platform • Spectral Resolution: 0.25 cm-1 • Spectral Range: • Longwave: 645 - 1300 cm -1 • Midwave: 1290 - 2000 cm-1 • Shortwave: 1980 - 2700 cm-1 • Spatial Resolution: 2.6 km field of view @ 20 km altitude • Scan Width +/- 48o • Number of Elements/Scan Line:13 earth spots 4. SUMMARY Schematic description of the CALIC encoder • CIMSS’s BAR data preprocessing scheme significantly improves the compression ratios of such state-of-the-art compression methods as 2D JPEG2000 (Part 1), 2D CALIC, and 2D JPEG-LS, as well as arithmetic coding and GZIP for NAST-I data . • After applying the BAR preprocessing scheme, the standard state-of-the-art compression methods perform almost equally well !! • CIMSS-developed algorithms (Lossless PCA, PPVQ) show better results • on interferometer data than standard compression methods, achieving • net lossless compression ratio > 5 • 2D JPEG-LS: Published in 1999 as a lossless compression standard of the ISO/IEC. • NAST-I Interferogram Data Format • Longwave, midwave, and shortwave interferograms stored separately. • Test dataset has two scenes: • Scene 1.Winter case off the East Coast of the U.S • Scene 2. Summer case off the Coast of Italy (ADREIX) • Each scene has 100 scan lines each containing 15 views • (2 blackbody views + 13 earth views) • Each longwave, midwave, and shortwave interferogram has • 3957 points • Real and imaginary parts of interferogram data stored separately Schematic description of the JPEG-LS encoder Acknowledgement: This work is prepared in support of National Oceanic & Atmospheric Administration (NOAA) GOES-R data compression research under grant NA07EC0676. 5. REFERENCES [1] B. Huang, A. Ahuja, H.-L. Huang, "Lossless Compression of Ultraspectral Sounder Data," Hyperspectral Data Compression, pp. 75-106, G. Motta, F. Rizzo, and J. Storer, Ed., Springer, 2006. [2] B. Huang, A. Ahuja, Y. Sriraja, H.-L. Huang, and M. D. Goldberg, “Lossless compression studies for NOAA GOES-R Hyperspectral Environmental Suite,” 86th AMS Annual Meeting, 2006. [3] A. Ahuja, B. Huang, and M. D. Goldberg, “Comparison of minimum spanning tree reordering with bias-adjusted reordering for lossless compression of ultraspectral sounder data,” SPIE International Symposium on Defense and Security, Orlando, 17 – 21 April 2006. [4] B. Huang, A. Ahuja, H.-L. Huang, T. J. Schmit, and R. W. Heymann, “Fast precomputed VQ with optimal bit allocation for lossless compression of ultraspectral sounder data,” IEEE Data Compression Conference (DCC) 2005, 408-417, 2005. [5] B. Huang, A. Ahuja, H.-L. Huang, T. J. Schmit, and R. W. Heymann, “Ultraspectral sounder data compression using error-correcting reversible variable-length coding” SPIE Annual Meeting, San Diego, 31 July – 4 August 2005, Proc. SPIE, 5889, 167-176, 2005. [6] B. Huang, A. Ahuja, H.-L. Huang, T. J. Schmit, and R. W. Heymann, “Lossless compression of 3D hyperspectral sounding data using context-based adaptive lossless image codec with Bias-Adjusted Reordering,” Optical Engineering, Vol. 43, No. 9, pp. 2071-2079, 2004. [7] B. Huang, A. Ahuja, H.-L. Huang, T. J. Schmit, and R. W. Heymann, “Effects of Starting Channel for Spectral Reordering on Lossless Compression of 3D Ultraspectral Sounder Data,” SPIE 4th Int. Asia-Pacific Environmental Remote Sensing Symposium, Honolulu, Hawaii, 8-11 November 2004. [8] B. Huang, A. Ahuja, H.-L. Huang, T. J. Schmit, and R. W. Heymann, “Predictive Partitioned Vector Quantization for Hyperspectral Sounder Data Compression,” SPIE Annual Meeting, Denver, 2-6 August 2004, Proc. SPIE, 5548, 70-77, 2004. Compression Results with and without BAR preprocessing scheme for Winter case off the East Coast of the U.S Compression Results with and without BAR preprocessing scheme for Summer case off the Coast of Italy (ADREIX) Longwave Spectral Region Shortwave Spectral Region Midwave Spectral Region