Zerotree coding of wavelet coefficients for image data on arbitrarily shaped support

Kawanaka, Akira; Algazi, V. Ralph

doi:10.1109/dcc.1999.785691

Cited by 4 publications

(5 citation statements)

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“…In 2000, Li and Li [8] proposed a simple algorithm for shape-adaptive wavelet transform (SAWT), which works seamlessly with objects of arbitrary geometry. At about the same time, various algorithms were developed [9], [10] to efficiently encode the coefficients produced by SAWT, with very promising results. We used some of these tools for the compression of multispectral images in [6] and [7], where, unlike in [1], the focus was on the encoding of compact individual regions rather than classes and only global spectral transforms were used.…”

Section: Improved Class-based Coding Of Multispectral Images With Shamentioning

confidence: 98%

“…Class-adaptive KLT will not be changed as it stands at the core of the class-oriented approach. The major innovations take place in the two spatial-domain coding blocks, where DCT (i.e., data linearization followed by 1-D-DCT) is replaced by SAWT and scalar quantization by shape-adaptive set partitioning in hierarchical trees (SPIHT) coding (SA-SPIHT) [9], which is a version of the well-known bit-plane coder SPIHT [12], adapted to work on arbitrary-geometry support. Although both techniques are already described in the literature, there are some points that merit discussion.…”

Section: Wavelet-based Cbcmentioning

confidence: 99%

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Improved Class-Based Coding of Multispectral Images With Shape-Adaptive Wavelet Transform

Cagnazzo¹,

Parrilli²,

Poggi³

et al. 2007

IEEE Geosci. Remote Sensing Lett.

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In this letter, we improve the class-based transformcoding scheme proposed by Gelli and Poggi for the compression of multispectral images. The original spatial-coding tools, 1-D discrete cosine transform and scalar quantization, are replaced by shape-adaptive wavelet transform and set partitioning in hierarchical trees. Numerical experiments show that the improved technique outperforms the original one for medium-to highquality compression and is consistently superior to all reference techniques.

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Section: Improved Class-based Coding Of Multispectral Images With Shamentioning

confidence: 98%

Section: Wavelet-based Cbcmentioning

confidence: 99%

Improved Class-Based Coding of Multispectral Images With Shape-Adaptive Wavelet Transform

Cagnazzo¹,

Parrilli²,

Poggi³

et al. 2007

IEEE Geosci. Remote Sensing Lett.

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“…Especially important for our needs, it can be readily modified to encode images of arbitrary geometry after a shape-adaptive WT. In our own implementation [43] (similar to that formerly proposed in [44] and further refined in [45]), we introduce only two major changes with respect to the basic algorithm. First, only active nodes, that is nodes belonging to the support of the SA-WT of the object, are considered while scanning a spatial orientation tree.…”

Section: Region Coding and Shape-adaptive Spihtmentioning

confidence: 99%

Region-Based Transform Coding of Multispectral Images

Cagnazzo

Poggi

Verdoliva

2007

IEEE Trans. on Image Process.

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Abstract-We propose a new efficient region-based scheme for the compression of multispectral remote-sensing images. The region-based description of an image comprises a segmentation map, which singles out the relevant regions and provides their main features, followed by the detailed (possibly lossless) description of each region. The map conveys information on the image structure and could even be the only item of interest for the user; moreover, it enables the user to perform a selective download of the regions of interest, or can be used for high-level data mining and retrieval applications. This approach, with the multiple pieces of information required, may seem inherently inefficient. The goal of this research is to show that, by carefully selecting the appropriate segmentation and coding tools, region-based compression of multispectral images can be also effective in a rate-distortion sense, thus providing an image description that is both insightful and efficient. To this end, we define a generic coding scheme, based on Bayesian image segmentation and on transform coding, where several key design choices, however, are left open for optimization, from the type of transform, to the rate allocation procedure, and so on. Then, through an extensive experimental phase on real-world multispectral images, we gain insight on such key choices, and finally single out an efficient and robust coding scheme, with Bayesian segmentation, class-adaptive Karhunen-Loève spectral transform, and shape-adaptive wavelet spatial transform, which outperforms state-of-the-art and carefully tuned conventional techniques, such as JPEG-2000 multicomponent or SPIHT-based coders.

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“…To this end, in modern embedded wavelet coders, one employs a SA-DWT [13] that transforms only opaque regions in the image. For shape-adaptive SPIHT (SA-SPIHT) coding, transparent regions are considered to be permanently insignificant, as in [15]. In this case, zerotree structures aggregate large regions of transparent coefficients into zerotree symbols along with the opaque insignificant coefficients.…”

Section: A Shape-adaptive Codingmentioning

confidence: 99%

Spectral-decorrelation strategies for the compression of hyperspectral imagery

Tamhankar

Fowler

2007

2007 IEEE International Geoscience and Remote Sensing Symposium

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Abstract-Several linear transforms with constructions more general than that of principal component analysis are considered for spectral decorrelation in the compression of hyperspectral imagery. Specifically, orthogonal nonnegative matrix factorization, generalized principal component analysis, and principal component analysis coupled with explicit segmentation based on spectral angle mapping are considered. These spectraldecorrelation techniques are employed in conjunction with wavelet-based spatial decorrelation for hyperspectral compression using a 3D version of the well-known SPIHT algorithm. A shape-adaptive wavelet transform and shape-adaptive SPIHT coder are used in the case of the latter two spectral-decorrelation techniques which segment the hyperspectral dataset into multiple distinct pixel classes. Experimental results reveal that, despite their general formulation, the proposed techniques fail to offer spectral-decorrelation performance superior to that of traditional principal component analysis.

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Zerotree coding of wavelet coefficients for image data on arbitrarily shaped support

Cited by 4 publications

References 0 publications

Improved Class-Based Coding of Multispectral Images With Shape-Adaptive Wavelet Transform

Improved Class-Based Coding of Multispectral Images With Shape-Adaptive Wavelet Transform

Region-Based Transform Coding of Multispectral Images

Spectral-decorrelation strategies for the compression of hyperspectral imagery

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