Multiresolution Gauss Markov random field models
Multiresolution Gauss Markov random field models
JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG 2000: Image Compression Fundamentals, Standards and Practice
Optimal bit allocation for biorthogonal wavelet coding
DCC '96 Proceedings of the Conference on Data Compression
Journal of Visual Communication and Image Representation
A new method of robust image compression based on the embedded zerotree wavelet algorithm
IEEE Transactions on Image Processing
High performance scalable image compression with EBCOT
IEEE Transactions on Image Processing
Hyperspectral Image Compression: Adapting SPIHT and EZW to Anisotropic 3-D Wavelet Coding
IEEE Transactions on Image Processing
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
Low bit-rate scalable video coding with 3-D set partitioning in hierarchical trees (3-D SPIHT)
IEEE Transactions on Circuits and Systems for Video Technology
Optimal 3-D coefficient tree structure for 3-D wavelet video coding
IEEE Transactions on Circuits and Systems for Video Technology
Efficient, low-complexity image coding with a set-partitioning embedded block coder
IEEE Transactions on Circuits and Systems for Video Technology
Image Wavelet Coding Systems: Part II of Set Partition Coding and Image Wavelet Coding Systems
Foundations and Trends in Signal Processing
Resolution scalable image coding with dyadic complementary rational wavelet transforms
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
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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.