Point Cloud Chroma Sampling for Lower-Precision Color 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 coefficients at a lower spatial precision than luma coefficients, 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 attribute representation accuracy is improved, but data size and compression difficulty increase
Solution Approach 1:
The patent applies different spatial precision levels to different color components: luma coefficients are encoded at a first spatial precision level while chroma coefficients are encoded at a lower second spatial precision level. This local differentiation allows the system to maintain high accuracy for luminance information while reducing data volume for chrominance information, which typically has lower visual sensitivity and fewer high-frequency components.
Solution Approach 2:
The patent changes the spatial precision parameter between different color components by transforming chroma information at a lower spatial precision than luma information. This parameter change enables effective compression of chroma data while preserving the essential visual quality, as human vision is more sensitive to luminance variations than chrominance variations.
2Productivity
If chroma information is encoded at lower spatial precision, then data compression efficiency is improved, but color detail and spatial frequency information are lost
Solution Approach 1:
The patent extracts and processes chroma information separately from luma information, applying a lower spatial precision transformation specifically to chroma coefficients. This extraction allows the system to remove high-frequency chroma components that contribute less to visual quality while preserving the essential color information at the reduced precision level.
Solution Approach 2:
The patent applies partial action by transforming only the chroma components at a lower spatial precision rather than applying the same transformation to all color information. This selective approach maintains sufficient color detail for visual quality while achieving compression efficiency, as the luma components retain their full spatial precision.
Data Source
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.