Layered Point Cloud Coding for Lower Bit-Rate Reconstruction
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Solution Overview
Problem
Existing technologies face challenges in efficiently compressing dynamic point clouds for distribution while maintaining high quality and reducing bit-rate consumption, which is crucial for applications like immersive worlds and autonomous vehicles.
Innovation Solution
A two-layer-based encoding and decoding method for point clouds, where the base layer provides a lossy representation and the enhancement layer enhances quality, allowing for efficient compression and reconstruction of dynamic point clouds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If point clouds are compressed for distribution, then bit-rate consumption is reduced, but quality of experience deteriorates
Solution Approach 1:
The patent divides the point cloud data into multiple layers (base layer and enhancement layers) with different quality levels. The base layer provides essential information at lower bit-rate, while enhancement layers add incremental quality improvements. This segmentation allows receivers to reconstruct point clouds at different quality levels according to available bandwidth, resolving the contradiction between bit-rate reduction and quality maintenance.
Solution Approach 2:
The patent changes the quality parameter of point cloud reconstruction by providing multiple reconstruction quality levels through different layers. The base layer uses coarser reconstruction parameters while enhancement layers refine these parameters, enabling adaptive quality adjustment based on bit-rate constraints without completely sacrificing quality of experience.
2Quantity of substance
If static point clouds are used for VR distribution, then data size is manageable, but dynamic interaction capability is lost
Solution Approach 1:
The patent introduces temporal dynamics to point cloud representation by organizing data into frames with motion compensation techniques. Instead of static point clouds, the system processes sequences of point cloud frames with motion vectors and prediction, enabling dynamic interaction while maintaining manageable data sizes through temporal redundancy removal.
Solution Approach 2:
The patent performs preliminary motion estimation and compensation on point cloud sequences before transmission. By predicting motion between frames and encoding only the differences, the system reduces the effective data size required to represent dynamic scenes, thereby enabling dynamic interaction capability while keeping transmitted data volume manageable.
3Manufacturing precision
If high-quality point cloud reconstruction is achieved, then quality of experience is maintained, but bit-rate consumption increases
Solution Approach 1:
The patent applies partial reconstruction by transmitting only the most significant components of point cloud data in the base layer, then adding incremental details through enhancement layers. This partial action approach allows receivers to achieve acceptable quality with minimal bit-rate by selecting only necessary reconstruction levels, rather than transmitting complete high-quality data.
Solution Approach 2:
The patent structures point cloud data in a nested hierarchical format where the base layer contains essential reconstruction information and enhancement layers contain nested additional details. Each layer is self-contained and can be independently decoded, allowing progressive quality improvement without requiring retransmission of entire data sets, thus managing bit-rate consumption effectively.
Data Source
AI summary
At least one embodiment relates to methods for encoding, signaling and decoding a 3D point cloud with different layers associated with Point Local Reconstruction information and modes. A layer may be associated with its own information and modes or associated metadata may comprise indexes to information or modes encoded or decoded in relation with a different layer.