Multi-disciplinary technologies are currently involved in orthognathic and dental surgery. By using 3D and CT scans, the surgery can be planned beforehand by making use of 3D image processing, visualization and planning tools. With 3D printing, accurate splints and wafers can be generated for the surgery. Nowadays, these tools are on-premises software and this makes it very hard for collaboration between several specialists. Therefore, we researched the possibility to create an online cloud-based platform to run the currently used surgical planning tools. We achieved multiple two-factor authentication user logins, simultaneous surgical planning sessions and lightweight multi-platform support.
Combining multiple cameras in a bigger multi-camera system give the opportunity to realize novel concepts (e.g. omnidirectional video, view interpolation) in real-time. The better the quality, the more data that is needed to be captured. As more data has a direct impact on storage space and communication bandwidth, it is preferable to reduce the load by compressing the size. This cannot come at the expense of latency, because the main requirement is real-time data processing for multi-camera video applications. Also, all the image details need to be preserved for improving the computational usage in a later stage. Therefore, this research is focused on predictive-corrective coding filters with entropy encoding (i.e. Huffman coding) and apply these on the raw image sensor data to compress the huge amount of data in a lossless manner. This technique does not need framebuffers, nor does it introduce any additional latency. At maximum, there will be some line-based latency, in order to combine multiple compressed pixels in one communication package. It has a lower compression factor as lossy image compression algorithms, but it does not remove human invisible image features that are crucial in disparity calculations, matching, video stitching and 3D model synthesis. This paper compares various existing predictive-corrective coding filters after they have been optimized to work on raw sensor data with a color filter array (i.e. Bayer pattern). The intention is to develop an efficient implementation for System-on-Chip (SoC) architectures to improve the computational multi-camera systems.
Computational video multi-camera systems allow novel applications such as stereo-vision and view interpolation. The computational-as well as communication and storage requirements for real-time multi-camera video are huge. High quality stereo-and view interpolation applications require the accurate combination of detailed image features in two or more cameras. The use of lossy video compression algorithms often lowers the accuracy of small details and textures that are probably not noticeable by a human viewer, but that are crucial in disparity calculations, matching, video stitching and 3D model synthesis. This paper makes a quantitative comparison of two lossless video compression methods. The intention is to use them for efficient implementation in System-on-Chip (SoC) architectures in computational camera systems. The methods compared are based on predictive-corrective compression and Huffman encoding as well as derived methods. For efficient hardware implementation alternative methods for the use of the Huffman coding are investigated. The comparison includes the use of Huffman encoding parameters from previous frames in the compression of current frames.
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