Andreas Weinlich

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The design of new HEVC extensions comes with the need for careful analysis of internal HEVC codec decisions. Several bitstream analyzers have evolved for this purpose and provide a visualization of encoder decisions as seen from a decoder viewpoint. None of the existing solutions is able to provide actual insight into the encoder and its RDO decision(More)
Medical imaging in hospitals requires fast and efficient image compression to support the clinical work flow and to save costs. Least-squares 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(More)
We present a new method for data-adaptive compression of dense vector fields in dynamic medical volume data. Conventional block-based motion compensation used for temporal prediction in video compression cannot conveniently cope with deformable motion typically found in medical image sequences encoded over time. Based on an approximation of physiologic(More)
We present a new approach for efficient estimation and storage of tissue deformation in dynamic medical image data like 3-D+ t computed tomography reconstructions of human heart acquisitions. Tissue deformation between two points in time can be described by means of a displacement vector field indicating for each voxel of a slice, from which position in the(More)
The utility of medical image increases due to serious disease prediction such as brain stroke and lung cancer. The size of medical image required large amount of memory and bandwidth for storage and transmission. For the lossless compression of medical image some standard algorithm are used such as JPEG, composite and Wavelet based. These entire algorithms(More)
Predictive coding is applied in many state-of-the-art lossless image compression algorithms like JPEG-LS, CALIC, or least-squares-based methods. We propose a new approach for accurate intensity prediction in pixel-predictive coding of computed tomography (CT) images. Exploiting their particular edge characteristic, the method only relies on a small(More)
Pixelwise linear prediction using backward-adaptive least-squares or weighted least-squares estimation of prediction coefficients is currently among the state-of-the-art methods for lossless image compression. While current research is focused on mean intensity prediction of the pixel to be transmitted, best compression requires occurrence probability(More)
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