Improvements for Projection-based Point Cloud Compression

Point clouds for immersive media technology have received substantial interest in recent years. Such representation of three-dimensional (3D) scenery provides freedom of movement for the viewer. However, transmitting and/or storing such content requires large amount of data and it is not feasible on today's network technology. Thus, there is a necessity for having e cient compression algorithms in order to facilitate proper transmission and storage of such content. Recently, projection-based methods have been considered for compressing point cloud data. In these methods, the point cloud data are projected onto a 2D image plane in order to utilize the current 2D video coding standards for compressing such content. These coding schemes provide signi cant improvement over state-ofthe-art methods in terms of compression e ciency. However, the projection-based point cloud compression requires special handling of boundaries and sparsity in the 2D projections. This thesis work addresses these issues by proposing two methods which improve the compression performance of both intra-frame and inter-frame coding for 2D video coding of volumetric data and meanwhile reduce the coding artifacts. The conducted experiments illustrated that the bitrate requirements are reduced by around 26% and 29% for geometry and color attributes, respectively compared to the case that the proposed algorithms are not applied. In addition, the proposed techniques showed negligible complexity impact in terms of encoding and decoding runtimes

Improvements for Projection-based Point Cloud Compression

Point clouds for immersive media technology have received substantial interest in recent years. Such representation of three-dimensional (3D) scenery provides freedom of movement for the viewer. However, transmitting and/or storing such content requires large amount of data and it is not feasible on today's network technology. Thus, there is a necessity for having e cient compression algorithms in order to facilitate proper transmission and storage of such content. Recently, projection-based methods have been considered for compressing point cloud data. In these methods, the point cloud data are projected onto a 2D image plane in order to utilize the current 2D video coding standards for compressing such content. These coding schemes provide signi cant improvement over state-ofthe-art methods in terms of compression e ciency. However, the projection-based point cloud compression requires special handling of boundaries and sparsity in the 2D projections. This thesis work addresses these issues by proposing two methods which improve the compression performance of both intra-frame and inter-frame coding for 2D video coding of volumetric data and meanwhile reduce the coding artifacts. The conducted experiments illustrated that the bitrate requirements are reduced by around 26% and 29% for geometry and color attributes, respectively compared to the case that the proposed algorithms are not applied. In addition, the proposed techniques showed negligible complexity impact in terms of encoding and decoding runtimes