Colored Point Cloud Chroma Subsampling for Attribute Compression
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Solution Overview
Problem
Point cloud data, due to its large size, poses challenges for efficient storage and transmission, necessitating effective compression techniques that balance data reduction with visual quality, especially in applications requiring lossless compression.
Innovation Solution
Encoding chroma information at a lower spatial precision than luma information in point cloud data, filtering out higher spatial frequencies of the chroma component, thereby improving compression efficiency without direct 3D sampling of color attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If chroma information is encoded at the same spatial precision as luma information, then color accuracy is maintained, but data size and compression complexity increase
Solution Approach 1:
The patent segments the color information into chroma and luma components with different encoding precision levels. Chroma information is encoded at a lower spatial precision than luma information, allowing selective data reduction in the chroma component while maintaining luma quality, thereby reducing overall data size while preserving visual quality.
Solution Approach 2:
The patent applies different quality levels to different components of the point cloud data. Luma information maintains high spatial precision for accurate brightness representation, while chroma information uses lower spatial precision since human vision is more sensitive to luminance than chrominance, achieving local optimization of quality versus data size.
2Productivity
If chroma information is encoded at lower spatial precision, then compression efficiency improves, but color detail is reduced
Solution Approach 1:
The patent changes the spatial precision parameter differently for chroma and luma components. By reducing the spatial precision parameter for chroma encoding while maintaining it for luma, the system achieves better compression efficiency without significantly impacting perceived color detail, as human vision is less sensitive to chroma high-frequency details.
3Measurement precision
If direct 3D sampling of color attributes is used, then color accuracy is maintained, but processing complexity and data size increase
Solution Approach 1:
The patent extracts and processes chroma and luma information separately from the original 3D point cloud data. By taking out the color attributes and transforming them into frequency domains with different sampling rates, the system reduces processing complexity and data size while maintaining acceptable color accuracy through the separable processing approach.
Data Source
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AI summary
Systems, apparatuses, methods, and computer-readable media are described for determining and/or coding color attribute information in a colored point cloud frame. Chroma information may be transformed into chroma coefficients at a first level of spatial precision and luma information may be transformed into luma coefficients at a second level of spatial precision such that the chroma information is represented at a lower spatial precision than the luma information. The color attributes may be reconstructed based on decoding the chroma coefficients and the luma coefficients from the bitstream.