Skip to main content

Multidimensional Seismic Compression by Hybrid Transform with Multiscale-Based Coding

  • Living reference work entry
  • First Online:
Handbook of Geomathematics

Abstract

We present algorithms that compress two- and three-dimensional seismic data arrays. These arrays are piecewise smooth in the horizontal directions and have oscillating events in the vertical direction. The transform part of the compression process is an algorithm that combines wavelet and the local cosine transform (LCT). The quantization and the entropy coding parts of the compression were taken from wavelet-based coding such as set partitioning in hierarchical trees (SPIHT) and embedded zerotree wavelet (EZW) encoding/decoding schemes that efficiently use the multiscale (multiresolution) structure of the wavelet coefficients. To efficiently apply these codecs (SPIHT or EZW) to a mixed coefficient array, reordering of the LCT coefficients takes place. This algorithm outperforms other algorithms that are based only on the 2D wavelet transforms such as JPEG 2000 standard. The algorithms retain fine oscillatory events including texture even at low bit rate. The wavelet part in the mixed transform of the hybrid algorithm utilizes the library of Butterworth wavelet transforms that outperforms the commonly used 9/7 biorthogonal wavelets. In the 3D case, the 2D wavelet transform is applied to horizontal planes while the LCT is applied to its vertical direction. After reordering the LCT coefficients, the 3D coding (SPIHT or EZW) is applied. In addition, a 3D compression method for visualization is described. For this, the data cube is decomposed into relatively small cubes, which are processed separately.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

References

  • Antonini M, Barlaud M, Mathieu P, Daubechies I (1992) Image coding using wavelet transform. IEEE Trans Image Process 1(2):205–220

    Article  Google Scholar 

  • Averbuch A, Aharoni G, Coifman R, Israeli M (1993) Local cosine transform – a method for the reduction of blocking effects in JPEG. J Math Imaging Vis Spec Issue Wavelets 3:7–38

    Article  MathSciNet  MATH  Google Scholar 

  • Averbuch A, Braverman L, Coifman R, Israeli M, Sidi A (2000) Efficient computation of oscillatory integrals via adaptive multiscale local Fourier bases. Appl Comput Harmon Anal9(1):19–53

    Google Scholar 

  • Averbuch AZ, Meyer F, Stromberg J-O Coifman R Vassiliou A (2001a) Efficient compression for seismic data. IEEE Trans Image Process 10(12):1801–1814

    Article  MathSciNet  MATH  Google Scholar 

  • Averbuch AZ, Pevnyi AB, Zheludev VA (2001b) Butterworth wavelet transforms derived from discrete interpolatory splines: recursive implementation. Signal Process 81:2363–2382

    Article  MATH  Google Scholar 

  • Averbuch A, Braverman L, Coifman R, Israeli M (2001c) On efficient computation of multidimensional oscillatory integrals with local Fourier bases. J Nonlinear Anal 47:3491–3502

    Article  MathSciNet  MATH  Google Scholar 

  • Averbuch, Averbuch AZ, Zheludev VA (2007) Wavelet and frame transforms originated from continuous and discrete splines. In: Astola J, Yaroslavsky L (eds) Advances in signal transforms: theory and applications. Hindawi Publishing, New York, pp 1–56

    Google Scholar 

  • Brislawn CM (1996) Classification of nonexpansive symmetric extension transforms for multirate filter banks. Appl Comput Harmon Anal 3(4):337–357

    Article  MATH  Google Scholar 

  • Brislawn CM (2002) The FBI fingerprint image compression specification. In: Topivala P (ed) Wavelet image and video compression. The International Series in Engineering and Computer Science, Springer, Boston, 450, pp 271–288

    Chapter  Google Scholar 

  • Cohen A, Daubechies I, Feauveau J-C (1992) Biorthogonal bases of compactly supported wavelets. Commun Pure Appl Math 45:485–560

    Article  MathSciNet  MATH  Google Scholar 

  • Coifman RR, Meyer Y (1991) Remarques sur l’analyse de Fourier a fenêtre. C R Acad Sci 312(3):259–261

    MathSciNet  MATH  Google Scholar 

  • Daubechies I (1992) Ten lectures on wavelets. SIAM, Philadelphia

    Book  MATH  Google Scholar 

  • Donoho PL, Ergas RA, Villasenor JD (1995) High-performance seismic trace compression. In: 65th annual international meeting of the society of exploration geophysics, expanded abstracts, Houston, pp 160–163

    Google Scholar 

  • Du Q, Fowler JE (2007) Hyperspectral image compression using JPEG2000 and principal component analysis. IEEE Geosci Remote Sens Lett 4:201–205

    Article  Google Scholar 

  • Feig E, Winograd S (1992) Fast algorithms for the discrete cosine transform. IEEE Trans Signal Process 40:2174–2193

    Article  MATH  Google Scholar 

  • Fowler JE, Rucker JT (2007) Three-dimensional wavelet-based compression of hyperspectral imagery. In: Chang C-I (ed) Hyperspectral data exploitation. Wiley, Hoboken, pp 379–407

    Chapter  Google Scholar 

  • Jeong Y-A, Cheong C-K (1998) A DCT-based embedded image coder using wavelet structure of DCT for very low bit rate video codec. IEEE Trans Consum Electron 44(3):500–508

    Google Scholar 

  • JPEG 2000 Standard (2000).http://www.jpeg.org/jpeg2000/

  • Khéne MF, Abdul-Jauwad SH (2000) Efficient seismic compression using the lifting scheme. In: 70th annual international meeting of the society of exploration geophysics, expanded abstracts, Calgary pp 2052–2054

    Google Scholar 

  • Kim B-J, Xiong Z, Pearlman WA (2000) Low bit-rate scalable video coding with 3D set partitioning in hierarchical trees (3D SPIHT). IEEE Trans Circuits Syst Video Technol 10: 1374–1387

    Article  Google Scholar 

  • Mallat S (2008) A Wavelet Tour of Signal Processing, 3rd ed. Academic Press.

    Google Scholar 

  • Malvar HS (2000) Fast progressive image coding without wavelets. In: Storer JA, Cohn M (eds) Proceedings of the IEEE data compression conference, Snowbird, Mar 2000, pp 243–252

    Google Scholar 

  • Matviyenko G (1996) Optimized local trigonometric bases. Appl Comput Harmon Anal 3:301–323

    Article  MathSciNet  MATH  Google Scholar 

  • Meyer FG (1999) Fast compression of seismic data with local trigonometric bases. In: Aldroubi A, Laine AF, Unser MA, (eds) Wavelet applications in signal and image processing VII. Proceedings of SPIE, vol 3813. SIAM, Bellingham, pp 648–658

    Chapter  Google Scholar 

  • Meyer FG (2002) Image compression with adaptive local cosines: a comparative study. IEEE Trans Image Process 11(6):616–629

    Article  Google Scholar 

  • Motta G, Rizzo F, Storer JA (eds) (2006) Hyperspectral data compression. Springer Academic, pp 273–308

    Google Scholar 

  • Oppenheim AV, Schafer RW (1989) Discrete-time signal processing. Prentice Hall, Englewood Cliffs/New York

    MATH  Google Scholar 

  • Penna B, Tillo T, Magli E, Olmo G (2006) Progressive 3-D coding of hyperspectral images based on JPEG 2000. IEEE Geosci Remote Sens Lett 3(1):125–129

    Article  Google Scholar 

  • Rao KR, Yi P (1990) Discrete cosine transform. Academic, New York

    MATH  Google Scholar 

  • Said A, Pearlman WW (1996) A new, fast and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans Circuits Syst Video Technol 6:243–250

    Article  Google Scholar 

  • Shapiro JM (1993) Embedded image coding using zerotree of wavelet coefficients. IEEE Trans Signal Process 41:3445–3462

    Article  MATH  Google Scholar 

  • Sweldens W (1996) The lifting scheme: a custom design construction of biorthogonal wavelets. Appl Comput Harmon Anal 3(2):186–200

    Article  MathSciNet  MATH  Google Scholar 

  • Tang X, Pearlman WA (2006) Three-dimensional wavelet–based compression of hyperspectral images. In: Motta G, Rizzo F, Storer JA (eds) Hyperspectral data compression. Springer Science+Business Media, Inc., pp 273–308

    Google Scholar 

  • Tseng YH, Shih HK, Hsu PH (2000) Hyperspectral image compression using three-dimensional wavelet transformation. In: Proceedings of the 21th Asia conference on remote sensing, Taipie, pp 809–814

    Google Scholar 

  • Vassiliou A, Wickerhauser MV (1997) Comparison of wavelet image coding schemes for seismic data compression. In: 67th annual international meeting of the society of exploration geophysics, expanded abstracts, Dallas, pp 1334–1337

    Google Scholar 

  • Wang Y, Wu R-S (2000) Seismic data compression by an adaptive local cosine/sine transform and its effect on migration. Geophys Prospect 48:1009–1031

    Article  Google Scholar 

  • Wu W, Yang Z, Qin Q, Hu F (2006) Adaptive seismic data compression using wavelet packets. In: Geoscience and remote sensing symposium, 2006 (IGARSS 2006), Denver, pp 787–789

    Google Scholar 

  • Xiong Z, Guleryuz O, Orchard MT (1996) A DCT-based embedded image coder. IEEE Signal Process Lett 3:289–290

    Article  Google Scholar 

  • Xiong Z, Ramchadran K, Orchard MT, Zhang Y-Q (1999) A comparative study of DCT- and wavelet-based image coding. IEEE Trans Circuits Syst Video Technol 9:692–695

    Article  Google Scholar 

  • Zheludev VA, Kosloff D, Ragoza E (2002) Fast Kirchhoff migration in wavelet domain. Explor Geophys 33:23–27

    Article  Google Scholar 

  • Zheludev VA, Kosloff DD, Ragoza EY (2004) Compression of segmented 3D seismic data. Int J Wavelets Multiresolution Inf Process 2(3):269–281

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Amir Z. Averbuch .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this entry

Cite this entry

Averbuch, A.Z., Zheludev, V.A., Kosloff, D.D. (2013). Multidimensional Seismic Compression by Hybrid Transform with Multiscale-Based Coding. In: Freeden, W., Nashed, M., Sonar, T. (eds) Handbook of Geomathematics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27793-1_43-2

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-27793-1_43-2

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Online ISBN: 978-3-642-27793-1

  • eBook Packages: Springer Reference MathematicsReference Module Computer Science and Engineering

Publish with us

Policies and ethics