Point Cloud Compression Incorporating Region of Interest Coding

We introduce Region-of-Interest (ROI) coding for point cloud attributes, using an input-weighted distortion measure where the weights are determined by the ROI. In terms of coding, we use the Region Adaptive Hierarchical Transform (RAHT), which relies on a set of weights. We use a measure-theoretic interpretation of RAHT to show that we should set the weights of the transform to the weights of the distortion measure. The ROI is chosen as the 3D region of the face, which is detected from a set of 2D projections using the well-known Viola-Jones algorithm. Experimental results show subjectively meaningful improvements (7-8 dB PSNR) in a face ROI with subjectively insignificant degradations (under 1 dB PSNR) in the non-ROI.

• Machine Perception

• Algorithms and Theory