10695

Massively Parallel Lossless Compression of Medical Images Using Least-Squares Prediction and Arithmetic Coding

Andreas Weinlich, Johannes Rehm, Peter Amon, Andreas Hutter, Andre Kaup
Multimedia Communications and Signal Processing, Universitat Erlangen-Nurnberg, Erlangen, Germany
IEEE International Conference on Image Processing, 2013

@article{weinlich2013massively,

   title={MASSIVELY PARALLEL LOSSLESS COMPRESSION OF MEDICAL IMAGES USING LEAST-SQUARES PREDICTION AND ARITHMETIC CODING},

   author={Weinlich, Andreas and Rehm, Johannes and Amon, Peter and Hutter, Andreas and Kaup, Andr{‘e}},

   journal={training},

   volume={1},

   pages={x9},

   year={2013}

}

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Medical imaging in hospitals requires fast and efficient image compression to support the clinical work flow and to save costs. Leastsquares autoregressive pixel prediction methods combined with arithmetic coding constitutes the state of the art in lossless image compression. However, a high computational complexity of both prevents the application of respective CPU implementations in practice. We present a massively parallel compression system for medical volume images which runs on graphics cards. Image blocks are processed independently by separate processing threads. After pixel prediction with specialized border treatment, prediction errors are entropy coded with an adaptive binary arithmetic coder. Both steps are designed to match particular demands of the parallel hardware architecture. Comparisons with current image and video coders show efficiency gains of 3.3-13.6% while compression times can be reduced to a few seconds.
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