20 May 2013 LIDAR data processing for scalable compression
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Compression of LIDAR point cloud offers many challenges to the signal processing community. Compression schemes must preserve both the numerical and geometrical aspects of the data, while dealing with the sparsely distributed threedimensional nature of it. Very few effective compression methods have been developed for this type of data, and only a handful of those methods offer the advantages of scalability. The focus of this research and development activity was to design and implement a series of preprocessing techniques that address the common obstacles found when pursuing scalable LIDAR point cloud compression. Three main areas being addressed are spatial scalability by means of effective indexing techniques; range reduction and redundancy exploitation; and resolution scalability by means of sub-band decomposition and sampling. These techniques will be combined with two different entropy encoding schemes –namely LZW and MQ encoding, yielding scalable 12:1 compression rates.
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Ruben D. Nieves, Ruben D. Nieves, "LIDAR data processing for scalable compression", Proc. SPIE 8731, Laser Radar Technology and Applications XVIII, 87310B (20 May 2013); doi: 10.1117/12.2029451; https://doi.org/10.1117/12.2029451

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