In this paper a novel Hierarchical Sum of Double Difference metric, HSDD, was introduced. It was shown, as opposed to conventional Sum of Absolute Difference (SAD) metric, how this embedded-coding aware metric can jointly constrain the motion vector searching in both temporal and spatial (quad-tree) directions under multiresolution motion estimation (MRME) framework. The temporal-spatial co-optimization concept from HSDD brings us the motion compensation pyramid with better shape. The reward is that fewer bits are spent later for describing the isolated zeros. The compression performance of HSDD easily exceeds the performance of its competitors, especially when high compression ratios are used.

The use of computers for handling image data in the healthcare is growing. The amount of data produced by modern image generating techniques, such as CT and MR, is vast. The amount of data might be a problem from a storage point of view or when the data is sent over a network. To overcome these problems data compression techniques adapted to these applications are needed. There are an abundant number of compression schemes for the compression of images, but few that are geared towards compression of image stacks. Though these 2D techniques can be applied to the image stacks they do not fully exploit the three-dimensional nature of the stacks. In this thesis ideas from the image compression area are extended to exploit the 3D nature of image stacks. Attention is directed to MR and CT stacks. The compression schemes in this thesis are based on the relatively new notion of wavelets, used in the JPEG2000 standard. The schemes also providea quality progressive transmission, which allows intermediate low-quality results to be constructed. Two different coders are presented in this thesis both developed for the compression of image stacks containing medical data. They are evaluated and compared and a top performer is chosen. Advantages and drawbacks of the schemes and future enhancements are also discussed.

<p>The use of computers for handling image data in the healthcare is growing. The amount of data produced by modern image generating techniques, such as CT and MR, is vast. The amount of data might be a problem from a storage point of view or when the data is sent over a network. To overcome these problems data compression techniques adapted to these applications are needed. There are an abundant number of compression schemes for the compression of images, but few that are geared towards compression of image stacks. Though these 2D techniques can be applied to the image stacks they do not fully exploit the three-dimensional nature of the stacks. In this thesis ideas from the image compression area are extended to exploit the 3D nature of image stacks. Attention is directed to MR and CT stacks. The compression schemes in this thesis are based on the relatively new notion of wavelets, used in the JPEG2000 standard. The schemes also providea quality progressive transmission, which allows intermediate low-quality results to be constructed. Two different coders are presented in this thesis both developed for the compression of image stacks containing medical data. They are evaluated and compared and a top performer is chosen. Advantages and drawbacks of the schemes and future enhancements are also discussed.</p>