Improvement of entropy coding performance for medical images using 3D predictors

The lower limit of the optimal code length for a compressed data is determined by the entropy of the data. In this context, mapping the data to a space having lower entropy by using a suitable predictor provides a significant contribution to the performance of the compression. In this study, it was aimed to reduce the entropy of medical images by using linear volumetric predictors which are utilizing inter-frame correlations. In this way, the compression lower bound is reduced and the source coding algorithms achieve higher performance. Within this study, two and three-dimensional predictors were applied to two data sets, computerized tomography images and natural images. Simulations have shown that volumetric predictors provide much higher compression ratios as they reduce entropy more than two-dimensional estimators using only intra-frame correlations.

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