Improved prediction methods for scalable predictive animated mesh compression

Animated meshes represented as sequences of static meshes sharing the same connectivity require efficient compression. Among the compression techniques, layered predictive coding methods efficiently encode the animated meshes in a structured way such that the successive reconstruction with an adaptable quality can be performed. The decoding quality heavily depends on how well the prediction is performed in the encoder. Due to this fact, in this paper, three novel prediction structures are proposed and integrated into a state of the art layered predictive coder. The proposed structures are based on weighted spatial prediction with its weighted refinement and angular relations of triangles between current and previous frames. The experimental results show that compared to the state of the art scalable predictive coder, up to 30% bitrate reductions can be achieved with the combination of proposed prediction schemes depending on the content and quantization level.

► Layered predictive animated mesh compression achieves high compression efficiency with scalability support.
► Improving compression efficiency for the layered predictive structure studied.
► We introduce weighting for spatial prediction and its refinement to improve compression efficiency.
► We propose a novel predictor based on angular relations of triangles between current and previous frames.
► Combination of proposed predictions achieve up to 30% bitrate reduction.