Dynamic Point Cloud Layering for Bit-Rate and Quality Control
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Solution Overview
Problem
Existing technologies face challenges in efficiently compressing dynamic point clouds for distribution while maintaining quality and reducing bit-rate consumption, particularly in applications like virtual reality and autonomous vehicles, where point clouds are often large and require real-time processing.
Innovation Solution
A two-layer-based encoding structure is employed, comprising a base layer for lossy geometry representation and an enhancement layer for lossless or higher-quality representation of isolated points, using existing video codecs to convert point cloud data into video sequences, and incorporating metadata for interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If point cloud data is compressed for distribution, then bit-rate consumption is reduced, but quality of experience deteriorates
Solution Approach 1:
The point cloud data is divided into multiple layers: a base layer containing essential geometry information for acceptable quality, and enhancement layers containing additional details for improved quality. This segmentation allows receivers to decode at different quality levels based on available bit-rate, resolving the contradiction between compression and quality.
Solution Approach 2:
The encoding system dynamically adjusts encoding parameters such as point sampling density, color information precision, and geometry accuracy based on the target bit-rate. By changing these parameters, the system achieves optimal compression while maintaining acceptable quality of experience.
2Volume of stationary object
If point cloud data is compressed for distribution, then storage space is reduced, but quality of experience deteriorates
Solution Approach 1:
The multi-layer structure enables progressive decomposition of storage requirements. The base layer occupies minimal storage space for basic functionality, while enhancement layers provide incremental quality improvement. Users can choose storage allocation based on their quality requirements, resolving the contradiction between storage efficiency and quality.
Solution Approach 2:
The system encodes only the necessary portion of point cloud data at full quality (base layer), and provides optional enhancement layers for additional quality. This partial encoding approach reduces overall storage requirements while maintaining acceptable quality for basic applications.
3Speed
If real-time processing is implemented for dynamic point clouds, then responsiveness is improved, but processing complexity increases
Solution Approach 1:
The layered structure enables progressive processing where the base layer can be decoded and displayed immediately, providing real-time responsiveness. Enhancement layers can be processed progressively in the background to improve quality over time, resolving the contradiction between speed and complexity.
Solution Approach 2:
The point cloud data is pre-encoded into multiple quality layers before distribution. This preliminary action allows receivers to quickly decode the base layer for immediate display without complex real-time compression, reducing both processing complexity and improving responsiveness.
Data Source
AI summary
Described are methods and devices for adding at least one 3D sample to a point cloud frame and for assigning a color-coding mode to said at least one 3D sample, said color-coding mode indicating if color information associated with said at least one 3D sample is explicitly encoded in a bitstream or if said color information is implicit.