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Wavelet Transform-Based Data Compression and its Application to the Meteorological Data Sets

Wavelet Transform-Based Data Compression and its Application to the Meteorological Data Sets Dr. Ning Wang and Dr. Renate Brummer 7 May 2003. Wavelet Transform-Based Data Compression and its Application to the Meteorological Data Sets. Why : - increased data volume Objectives :

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Wavelet Transform-Based Data Compression and its Application to the Meteorological Data Sets

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  1. Wavelet Transform-Based Data Compression and its Application to the Meteorological Data Sets Dr. Ning Wang and Dr. Renate Brummer7 May 2003

  2. Wavelet Transform-Based Data Compression and its Application to the Meteorological Data Sets Why: - increased data volume Objectives: - ability to control precision - lower space resolution and higher dimensionality

  3. Compression:Original 1:1

  4. Compression:20:1

  5. Compression:50:1

  6. Compression:100:1

  7. Compression:Original 1:1

  8. Our Compression v.s. JPEG (I) original 30:1 wavelet compression

  9. Our Compression v.s. JPEG (II) original JPEG compressed

  10. JPEG 2000: Main Features • An image compression format • Each image is comprised of a number of component(s). Grayscale -- 1, RGB -- 3 • Inter-component transform: RGB-->YCrCb • Intra-component transform: Wavelet • Quantizer: scalar quantization with a deadzone • Entropy encoder: arithmetic encoder

  11. Pros and Cons of JPEG2000 • International standard • Error resilience • Less efforts for developing and distributing the decoder (Maybe)

  12. Pros and Cons of JPEG2000 • Not flexible • Primitive quantization and rate control techniques • Limited to 2D transform (w/o extension) • The standards for extension (part 2) and conformance testing (part 4) not been officially established and recognized

  13. Grid Data Compression • Why? - increased data volume • What makes grid data compression different from image compression ? - lower space resolution and higher dimensionality - importance of controlling the precision of data

  14. Procedure for Grid Data Compression

  15. Temperature Precision Control with MaxError < 0.125 K ETA 12 from 14 May 2002

  16. Temperature Average Error (K) for Previous Compression Factors ETA 12 from 14 May 2002

  17. Relative Humidity Precision Control with Max Error < 1.0 Percent ETA 12 from 14 May 2002

  18. Relative Humidity Average Error for Previous Compression Factors ETA 12 from 14 May 2002

  19. Example: Eta 12 Model Size of the original data sets: 12 GB Bandwidth for transmission: 1.4 Mbit/sec Transmisison time : 25 hours Compressed data size (50:1): 240 MB Transmisison time : 30 minutes

  20. Summary • Visible satellite imagery compression 20:1 to 50:1 • Grid data compression with precision control (our quasi-lossless example) 20:1 to 100:1 Note: The user can define the acceptable maximum or/and average error • Average storage and transmission time reduction 95% to 98%

  21. Future Direction • Fast wavelet transform code • Fast quantizer with less memory consumptions • Precision controlled grid data compression

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