You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
30 June 1993Adaptive context coding for lossless compression of medical grayscale images.
Models for the prediction error distribution in losslessly encoded grayscale images are explored. The prediction error distribution results from the use of linear predictors but the techniques used in the paper may also be applied to distributions which arise from the use of other methods to encode losslessly digital images. A compact method for representing the prediction error distribution for 12-bit greyscale images is used and the trade-off between space required for a distribution and the use of multiple distributions is investigated. Models considered include zeroth and first order Markov models. The variation of the prediction error distribution over the image is considered and shown to be important in achieving better compression. Choosing the predictor formula adaptively is also investigated.
The alert did not successfully save. Please try again later.
Anthony John Maeder, Peter E. Tischer, Roderick T. Worley, "Adaptive context coding for lossless compression of medical grayscale images.," Proc. SPIE 1897, Medical Imaging 1993: Image Capture, Formatting, and Display, (30 June 1993); https://doi.org/10.1117/12.146972