Three-Dimensional SPIHT Coding of Volume Images with Random Access and Resolution Scalability

End users of large volume image datasets are often interested only in certain features that can be identified as quickly as possible. For hyperspectral data, these features could reside only in certain ranges of spectral bands and certain spatial areas of the target. The same holds true for volume m...

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Veröffentlicht in:EURASIP journal on image and video processing Jg. 2008; H. 1; S. 1 - 13
Hauptverfasser: Christophe, Emmanuel, Pearlman, WilliamA
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.01.2008
Springer Nature B.V
SpringerOpen
Schlagworte:
Biometrics
Engineering
Image Processing and Computer Vision
Pattern Recognition
Research Article
Signal,Image and Speech Processing
Image Dataset
Image Compression
Random Access
Compression Algorithm
Hyperspectral Image
ISSN:1687-5176, 1687-5281
Online-Zugang:Volltext
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Zusammenfassung:End users of large volume image datasets are often interested only in certain features that can be identified as quickly as possible. For hyperspectral data, these features could reside only in certain ranges of spectral bands and certain spatial areas of the target. The same holds true for volume medical images for a certain volume region of the subject's anatomy. High spatial resolution may be the ultimate requirement, but in many cases a lower resolution would suffice, especially when rapid acquisition and browsing are essential. This paper presents a major extension of the 3D-SPIHT (set partitioning in hierarchical trees) image compression algorithm that enables random access decoding of any specified region of the image volume at a given spatial resolution and given bit rate from a single codestream. Final spatial and spectral (or axial) resolutions are chosen independently. Because the image wavelet transform is encoded in tree blocks and the bit rates of these tree blocks are minimized through a rate-distortion optimization procedure, the various resolutions and qualities of the images can be extracted while reading a minimum amount of bits from the coded data. The attributes and efficiency of this 3D-SPIHT extension are demonstrated for several medical and hyperspectral images in comparison to the JPEG2000 Multicomponent algorithm.
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ISSN:1687-5176
1687-5281
DOI:10.1155/2008/248905