RAHT Coefficient Quantization for Weight-Aware Point Cloud Compression

The patent applies different quantization step sizes to different RAHT coefficients based on their weights. Specifically, coefficients with higher weights (which represent more important spatial information) use smaller quantization step sizes to preserve accuracy, while coefficients with lower weights use larger step sizes. This local differentiation resolves the contradiction by maintaining high decoding accuracy where needed while accepting coarser quantization elsewhere, thus achieving a balance between complexity and precision.

The patent changes the quantization parameter (step size) based on the weight parameter of each RAHT coefficient. The quantization step size Δ is determined as a function of the coefficient weight w, such as Δ = K/√w or Δ = K/w, where K is a constant. This parameter adaptation allows the system to achieve high decoding accuracy by using appropriate quantization levels for different coefficient importance levels, while the functional relationship keeps the complexity manageable compared to fully adaptive methods.

The patent applies local quality by using different quantization step sizes for different regions of the point cloud data based on coefficient weights. Important regions (with higher weight coefficients) receive finer quantization to preserve quality, while less important regions use coarser quantization. This approach maintains processing simplicity by using a systematic weight-based rule rather than complex region segmentation, while significantly improving compression efficiency and reducing bandwidth requirements compared to uniform quantization.