Hybrid Algorithm for Medical Image Sequences using Super-Spatial Structure Prediction with LZ8

The necessity in medical image compression continuously grows during the last decade. In advanced medical life large number of medical images is processed in hospitals and medical centers around the world. These images are in the form of sequences which are much correlated and are of great importance. Hence lossless image compression is needed to reproduce the original quality of the image without any loss of information. To exploit the correlation a new algorithm is proposed in this paper. The proposed compression method combines Super-Spatial Structure Prediction with motion estimation and motion compensation to achieve higher compression ratio. This is applied with a simple block-matching process Binary Tree Search. Results are compared in terms of Compression Ratio and Peak Signal-to-Noise Ratio. The proposed methodology provides better CR and PSNR than the other state-of-the-art algorithm.