Point Cloud Compression Using Occupancy Map Segmentation
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Solution Overview
Problem
Current technologies face challenges in efficiently compressing and decompressing point clouds, which are large datasets used for 3D representations, especially in real-time communications and applications like virtual reality, due to high data volume and complexity.
Innovation Solution
The proposed solution involves using video-coding techniques for point cloud compression (V-PCC) that leverage existing video codecs to compress geometry, occupancy, and texture of dynamic point clouds as separate video sequences, with additional metadata compressed efficiently to reduce overall bitstream size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video-coding techniques are used for point cloud compression, then compression efficiency is improved, but background portion distortions may degrade overall quality
Solution Approach 1:
The patent applies local quality by differentiating treatment between foreground and background portions of the point cloud. The occupancy map identifies which regions contain actual point cloud data (foreground) versus empty space (background). During rate-distortion optimization, the encoder selectively applies different distortion thresholds and coding parameters to these regions, ensuring high fidelity for foreground while allowing greater compression for background, thus resolving the contradiction between compression efficiency and quality preservation.
Solution Approach 2:
The patent changes coding parameters dynamically based on the occupancy map information. The rate-distortion optimization process adjusts quantization parameters, transformation parameters, and prediction parameters according to whether a coding block falls in the foreground or background portion. This parameter adaptation allows the system to achieve both high compression efficiency for background regions and high quality preservation for foreground regions simultaneously.
2Manufacturing precision
If rate-distortion optimization is performed considering all blocks, then quality is maintained, but computational complexity increases
Solution Approach 1:
The patent segments the point cloud data into foreground and background portions using an occupancy map before performing rate-distortion optimization. This segmentation allows the encoder to apply simplified optimization criteria to background blocks (which can tolerate higher distortion) while maintaining full optimization for foreground blocks. The segmentation strategy reduces the overall computational complexity by avoiding exhaustive optimization across all blocks while preserving quality where it matters most.
Solution Approach 2:
The patent applies partial action by performing full rate-distortion optimization only on foreground portions of the point cloud, while using simplified or skipped optimization for background portions. Since background regions contribute less to perceived quality, the encoder can afford to use faster, less computationally intensive coding methods for these areas. This selective application of optimization reduces overall computational complexity while maintaining adequate quality across the entire point cloud.
Data Source
AI summary
Aspects of the disclosure provide methods and apparatuses for point cloud compression and decompression. In some examples, an apparatus for point cloud compression/decompression includes processing circuitry. For example, the processing circuitry in the apparatus for point cloud encoding receives an occupancy map for a point cloud. The occupancy map is indicative of a background portion and a foreground portion for a coding block in an image that is generated based on the point cloud. Then, the processing circuitry devaluates distortions in the background portion of the coding block during an optimization process that results a coding option for the coding block, and encodes the coding block according to the coding option.