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Hyperspectral Imagery Compression Using Three Dimensional Discrete Transforms

Hyperspectral Imagery Compression Using Three Dimensional Discrete Transforms. Tong Qiao ( t.qiao@strath.ac.uk ) Supervisor: Dr. Jinchang Ren 04 / 07 / 2013. Structure. Introduction to hyperspectral imagery 3D discrete wavelet transform (DWT) based compression

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Hyperspectral Imagery Compression Using Three Dimensional Discrete Transforms

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  1. Hyperspectral Imagery Compression Using Three Dimensional Discrete Transforms Tong Qiao (t.qiao@strath.ac.uk) Supervisor: Dr.JinchangRen 04/07/2013

  2. Structure • Introduction to hyperspectral imagery • 3D discrete wavelet transform (DWT) based compression • 3D discrete cosine transform (DCT) based compression • Performance comparison • Conclusion

  3. Hyperspectral Imagery • High definition electro-optic images with hundreds of spectral bands • Applications: • Remote sensing • Military surveillance • Food quality analysis • Pharmaceutical Fig.1: Hyperspectral image acquired over Moffett Field (CA, USA)

  4. Hyperspectral Imagery • Problems • Huge amount of data • High cost for storage and transmission • Therefore, COMPRESSION is needed.

  5. Principles of Compression • Compression • Lossless (Compression ratio of 3:1) • Lossy (Compression ratio of 50:1 or more) • Transform coding • Transform coding • DWT based compression • JPEG 2000 standard • DCT based compression • JPEG standard

  6. 3D DWT Based Compression Fig.2: The 3D discrete wavelet transform

  7. 3D DWT Based Compression • Wavelet filter • Cohen-Daubechies-Feauveau (CDF) 9/7-tap filter (lossy compression) • CDF 5/3-tap filter (lossless compression) Fig.3: 3D dyadic DWT with 2 decomposition levels

  8. 3D DWT Based Compression • Encoding stage • 3D SPIHT ( Set Partitioning in Hierarchical Trees) • No child at the root node in the highest level • Each of other 7 nodes has a 2 x 2 x 2 child cube directing to the same spatial orientation in the same level • Except at highest and lowest levels, a pixel will have 8 offspring in the next level. Fig.4: 3D parent-child relationships between subbands of a 3D DWT

  9. 3D DWT Based Compression • 3D SPIHT algorithm • Initialisation • List of Insignificant Sets (LIS) • List of Insignificant Pixels (LIP) • List of Significant Pixels (LSP) • Coding passes • Sorting pass • Refinement pass • Coefficients and trees are stored in lists processed in sequence

  10. 3D DWT Based Compression • Entropy encoding • But only a little improvement • This step is left out.

  11. 3D DCT Based Compression • Adapted from JPEG standard • Equation: • Block diagram Coding Tables Quantisation Table Lossy Compressed Data 8 x 8 x 8 block DCT Entropy Encoder Quantiser

  12. 3D DCT Based Compression • Quantisation • Dequantisation

  13. 3D DCT Based Compression • Quantisation table for hyperspectral images • k: [0, 8] • Weak inter-band correlation: lower k • Strong inter-band correlation: higher k

  14. 3D DCT Based Compression • Quality level (q) • q: [1,99]

  15. 3D DWT Based Compression • Encoding stage • Huffman encoder • DC coefficients • Differential coding • Diff = DCi– DCi-1 • AC coefficients • 3D zig-zag scanning order • Run-length coding Fig.5: The differential coding of DC coefficients

  16. Performance Comparison • Four datasets Fig.6: Moffett field Fig.7: Indian pines and its ground truth Fig.8: Salinas valley and its ground truth Fig.9: Pavia University and its ground truth

  17. Performance Comparison • Subjective assessment • Compression bit rate = 0.1 bpppb • Left: DWT, right: DCT

  18. Performance Comparison • Subjective assessment • Compression bit rate: 0.2, 0.5, 0.8 and 1 bpppb • Top: DWT, bottom: DCT

  19. Performance Comparison • Objective assessment • Rate-distortion measurement • SNR (Signal-to-Noise Ratio) vs. bit rate

  20. Performance Comparison • Objective assessment

  21. Performance Comparison • Objective assessment

  22. Performance Comparison • Objective assessment

  23. Performance Comparison • Quality-assured assessment • SVM (Support Vector Machine) • 50% for training and 50% for testing • Optimal models are learnt from original images, then applied to reconstructed images

  24. Performance Comparison • Quality-assured assessment

  25. Performance Comparison • Quality-assured assessment

  26. Performance Comparison • Quality-assured assessment

  27. Conclusion • 3D DCT has great potential to produce better compression than 3D DWT • 3D DCT based compression of hyperspectral imagery at a bit rate of no less than 0.5 bpppb is feasible

  28. Thank you! Questions?

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