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This paper introduces a novel compression method for plenoptic point clouds (PPCs) that leverages a Karhunen-Loève transform followed by advanced attribute coders with intra prediction capabilities. The approach integrates seamlessly with the MPEG geometry-based PCC (G-PCC) standard, enhancing the efficiency of PPC attribute compression. Experimental results demonstrate that this method achieves superior compression performance compared to existing RAHT-based and video-based solutions, establishing it as the new state of the art in the field.
Compression of plenoptic point clouds just got a major upgrade, outperforming traditional methods and setting a new benchmark in the field.
Plenoptic point clouds (PPC) are novel data structures that represent the light from different viewing directions in order to provide a higher degree of realism to regular point clouds. This is achieved by associating each point to multiple colors instead of a single one. Here, we present a method to efficiently compress the attributes of a PPC, consisting of a Karhunen-Loève transform over the color attributes followed by multiple attribute coders with intra prediction capability. This compression scheme can be incorporated within the MPEG's geometry-based PCC (G-PCC) standard, using any of G-PCC's existing solutions for attribute coding. Compression performance assessment using PPCs of different spatial resolutions reveals competitive results in comparison to existing methods, such as RAHT-based or video-based PCC solutions. We believe our coder to be the new state of the art.