Transform Domain Temporal Prediction for Dynamic Point Cloud Compression

With the advent of immersive multimedia technologies, interest in dynamic point cloud compression standards, such as video-based point cloud compression (V-PCC), has been steadily growing. At the core of V-PCC, conventional video codecs such as High Efficiency Video Coding are employed for encoding the geometry video, attribute video and the occupancy map. Standard video codecs perform temporal prediction using motion compensation by simple pixel-copying from the reference frame. However, conventional pixel-domain prediction does not exploit spatial correlation within the block. This is highly sub-optimal especially due to large homogeneous regions in the geometry video. In this paper, spatial decorrelation is first achieved via the Discrete Cosine Transform. Prediction is then performed in the transform domain, where the observed variation in temporal correlation across frequencies is exploited for a more accurate prediction. Design of the correlation filters required for this procedure poses challenges. The inherent instability in closed-loop design is tackled using the Asymptotic Closed Loop paradigm which uses an iterative open-loop algorithm that asymptotically converges to closed-loop operation. Experimental results on geometry videos generated from common test conditions show significant bitrate savings of up to 5.8% (about 3% on average) and highlight the efficacy of our approach.