Comparison of wavelet image coding schemes for seismic data compression

Vassiliou, Anthony; Wickerhauser, Mladen Victor

doi:10.1190/1.1885651

Cited by 21 publications

(18 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Seismic data has an extensive oscillatory nature. More than that, compression of seismic data which is based on multiresolution decomposition using traditional wavelet basis and quantization which utilizes the correlation among scales as in regular multimedia images (see [2], [5], [18], and [19]) is outperformed by other methods as we can see from [11]- [13], [20]- [23] and from Table II (Method 1, column 1). This is especially true when the data is very oscillatory such as our four benchmark data sets which we called 1 to 4.…”

Section: Introductionmentioning

confidence: 92%

“…The zero bin width is assigned 1.4 times the value of the nonzero bin width and the reconstructed value from each subband nonzero quantization bin corresponds to a value slightly smaller than the bin midpoint value [20], [21]. The bin widths are estimated iteratively.…”

Section: Methods 2: Fast Wavelet Compression With Uniform Quantizmentioning

confidence: 99%

“…This compression, which is based on uniform (scalar) quantization [7], [20], [21], [25], has three steps.…”

Section: Methods 2: Fast Wavelet Compression With Uniform Quantizmentioning

confidence: 99%

See 2 more Smart Citations

Low bit-rate efficient compression for seismic data

Averbuch

Meyer

Strömberg

et al. 2001

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

Abstract-Compression is a relatively new introduced technique for seismic data operations. The main drive behind the use of data compression in seismic data is the very large size of seismic data acquired. Some of the most recent acquired marine seismic data sets exceed 10 Tbytes, and in fact there are currently seismic surveys planned with a volume of around 120 Tbytes. Thus, the need to compress these very large seismic data files is imperative. Nevertheless, seismic data are quite different from the typical images used in image processing and multimedia applications. Some of their major differences are the data dynamic range exceeding 100 dB in theory, very often it is data with extensive oscillatory nature, the and directions represent different physical meaning, and there is significant amount of coherent noise which is often present in seismic data. Up to now some of the algorithms used for seismic data compression were based on some form of wavelet or local cosine transform, while using a uniform or quasiuniform quantization scheme and they finally employ a Huffman coding scheme. Using this family of compression algorithms we achieve compression results which are acceptable to geophysicists, only at low to moderate compression ratios. For higher compression ratios or higher decibel quality, significant compression artifacts are introduced in the reconstructed images, even with high-dimensional transforms. The objective of this paper is to achieve higher compression ratio, than achieved with the wavelet/uniform quantization/Huffman coding family of compression schemes, with a comparable level of residual noise. The goal is to achieve above 40 dB in the decompressed seismic data sets. Several established compression algorithms are reviewed, and some new compression algorithms are introduced. All of these compression techniques are applied to a good representation of seismic data sets, and their results are documented in this paper. One of the conclusions is that adaptive multiscale local cosine transform with different windows sizes performs well on all the seismic data sets and outperforms the other methods from the SNR point of view. All the described methods cover wide range of different data sets. Each data set will have his own best performed method chosen from this collection. The results were performed on four different seismic data sets. Special emphasis was given to achieve faster processing speed which is another critical issue that is examined in the paper. Some of these algorithms are also suitable for multimedia type compression.

show abstract

Section: Introductionmentioning

confidence: 92%

Section: Methods 2: Fast Wavelet Compression With Uniform Quantizmentioning

confidence: 99%

See 1 more Smart Citation

Low bit-rate efficient compression for seismic data

Averbuch

Meyer

Strömberg

et al. 2001

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

show abstract

“…Wavelet transforms were very successful with compression of still images. Therefore the efforts to use these techniques for seismic compression were quite natural [3,6,10]. However, results turned out much less impressive than in image compression because of the great variability of seismic data and the noisy background inherent in it even within the same volume.…”

Section: Introductionmentioning

confidence: 99%

Compression of Segmented 3d Seismic Data

Zheludev

Kosloff

Ragoza³

2004

Int. J. Wavelets Multiresolut Inf. Process.

View full text Add to dashboard Cite

show abstract

“…A discussion of how wave atoms compare and contrast with other directional transforms can be found in Demanet (2006). For other wavelet-based alternatives for seismic data compression, see Wavelet Packets in Wu et al (2006);Wang et al (2010), high-dimensional Wavelets in Villasenor et al (1996), Dreamlets in Geng et al (2009), Seislets in Fomel (2006, and also Duval and Nguyen (1999); Vassiliou and Wickerhouser (1997); Averbuch et al (2001).…”

Section: Introductionmentioning

confidence: 99%

High-dimensional wave atoms and compression of seismic data sets

Leinonen

Hewett²,

Zhang³

et al. 2013

SEG Technical Program Expanded Abstracts 2013

View full text Add to dashboard Cite

SUMMARYWave atoms are a low-redundancy alternative to curvelets, suitable for high-dimensional seismic data processing. This abstract extends the wave atom orthobasis construction to 3D, 4D, and 5D Cartesian arrays, and parallelizes it in a sharedmemory environment. An implementation of the algorithm for NVIDIA CUDA capable graphics processing units (GPU) is also developed to accelerate computation for 2D and 3D data. The new transforms are benchmarked against the Fourier transform for compression of data generated from synthetic 2D and 3D acoustic models.

show abstract

Comparison of wavelet image coding schemes for seismic data compression

Cited by 21 publications

References 9 publications

Low bit-rate efficient compression for seismic data

Low bit-rate efficient compression for seismic data

Compression of Segmented 3d Seismic Data

High-dimensional wave atoms and compression of seismic data sets

Contact Info

Product

Resources

About