Conventional RAHT processes whole point-cloud frames sequentially; block-level intra prediction enables parallel coding and real-time processing.
Meta-learning adapts shared and rate-dependent weights so one neural image compression model can provide smooth bitrate control.
Mesh subgroups and signaling let receivers decode only user-selected 3D regions, reducing data volume, latency, and processing load.
Layered bitstreams separate base and enhancement data to reduce point cloud encoding and decoding complexity for VR, AR, and self-driving services.
Inter-channel codeword encoding groups quantized coefficients across color channels to improve image compression without further information loss.
Separating quantization parameters from lifting-transform syntax supports displacement values without the transform, with no performance loss.
Azimuth intervals tailor entropy context models to coordinate-transformation residuals, improving point-cloud encoding efficiency.
Preset neighbor settings limit prediction diversity; adaptive spatial associations select neighbors to improve point-cloud decoding efficiency.
Combining two point clouds can increase decoding complexity; origin flags in attribute data help identify each source efficiently.
Encoding point positions in smaller units and sharing common size information enables earlier output without waiting for a complete 3D data unit.