A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vector quantization and signal compression
Vector quantization and signal compression
Introduction to data compression (2nd ed.)
Introduction to data compression (2nd ed.)
JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG 2000: Image Compression Fundamentals, Standards and Practice
Lossless Compression of High-volume Numerical Data from Simulations
DCC '00 Proceedings of the Conference on Data Compression
An Efficient Algorithm for Lossless Compression of IEEE Float Audio
DCC '04 Proceedings of the Conference on Data Compression
Fast Lossless Compression of Scientific Floating-Point Data
DCC '06 Proceedings of the Data Compression Conference
Fast and Efficient Compression of Floating-Point Data
IEEE Transactions on Visualization and Computer Graphics
The alpha parallelogram predictor: A lossless compression method for motion capture data
Information Sciences: an International Journal
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Many scientific applications require that image data be stored in floating-point format due to the large dynamic range of the data. These applications pose a problem if the data needs to be compressed since modern image compression standards, such as JPEG2000, are only defined to operate on fixed-point or integer data. This paper proposes straightforward extensions to the JPEG2000 image compression standard which allow for the efficient coding of floating-point data. These extensions maintain desirable properties of JPEG2000, such as lossless and rate distortion optimal lossy decompression from the same coded bit stream, scalable embedded bit streams, error resilience, and implementation on low-memory hardware. Although the proposed methods can be used for both lossy and lossless compression, the discussion in this paper focuses on, and the test results are limited to, the lossless case. Test results on real image data show that the proposed lossless methods have raw compression performance that is competitive with, and sometime exceeds, current state-of-the-art methods.