Geometry Compression Artifact Removal for V-PCC over a Wide Bitrate Range

In video-based point cloud compression (V-PCC), point clouds are generated as videos via patch projection to be compressed using video coding techniques. However, a large number of filled empty pixels in the videos creates a fake context, which reduces the noise prediction accuracy in compression artifact removal. Moreover, mean square error (MSE)-based trained models perform better on low-bitrates than on high-bitrates due to the unbalanced parameter updates. This paper proposes an learning-based geometry compression artifact removal for V-PCC over a wide range of bitrates. Firstly, an occupancy map-based contextual feature extraction is proposed to eliminate the interference of empty pixels on the neighboring non-empty pixels. Secondly, an incremental Peak Signal to Noise Ratio (PSNR)-based training scheme is presented to balance the error differences. Experimental results show the effectiveness of the proposed method.