Motion estimation and filtered prediction for dynamic point cloud attribute compression
This addresses the problem of compressing dynamic point cloud color attributes more efficiently for applications like 3D video and virtual reality, representing a strong specific improvement.
The paper tackles the challenge of exploiting temporal redundancy in dynamic point cloud attribute compression by proposing an efficient block-based inter-coding scheme with integer-precision motion estimation and adaptive graph-based in-loop filtering. The result is significant coding gain over state-of-the-art methods, as demonstrated experimentally.
In point cloud compression, exploiting temporal redundancy for inter predictive coding is challenging because of the irregular geometry. This paper proposes an efficient block-based inter-coding scheme for color attribute compression. The scheme includes integer-precision motion estimation and an adaptive graph based in-loop filtering scheme for improved attribute prediction. The proposed block-based motion estimation scheme consists of an initial motion search that exploits geometric and color attributes, followed by a motion refinement that only minimizes color prediction error. To further improve color prediction, we propose a vertex-domain low-pass graph filtering scheme that can adaptively remove noise from predictors computed from motion estimation with different accuracy. Our experiments demonstrate significant coding gain over state-of-the-art coding methods.