Point Cloud Color Compression via Palette Coding
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Solution Overview
Problem
Existing methods for compressing point cloud data, particularly those using Light Detection And Ranging (LIDAR) data, face challenges in efficiently processing and preserving the vast amount of color information, leading to practical limitations in data processing.
Innovation Solution
A method involving the generation of a color palette using K-clustering, where each point in the cloud is assigned an index based on minimum Euclidean distance to a palette color, followed by entropy coding of these indexes for efficient compression and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing compression methods are used on point cloud data, then data processing can be performed, but visual quality deteriorates with distortions like JPEG transform quantization and color leaking
Solution Approach 1:
The patent transforms the color representation from continuous RGB values to discrete palette indexes. By changing the parameter space from continuous color values to discrete cluster labels, the method avoids transform quantization artifacts and color leaking while maintaining visual quality. The K-means clustering groups similar colors into discrete palettes, and entropy coding efficiently represents these discrete indexes.
2Productivity
If color palette is generated using K-clustering and entropy coding is applied, then compression efficiency improves by up to 7 dB at the same bit rate, but computational complexity increases
Solution Approach 1:
The patent performs K-means clustering to generate a color palette in advance, before the actual compression of point cloud data. This preliminary action creates a fixed reference palette that can be reused, avoiding repeated clustering computations during compression. The entropy coder then efficiently encodes indexes to this pre-generated palette, achieving high compression efficiency.
Solution Approach 2:
Instead of directly compressing continuous RGB color values, the patent creates discrete copies of color representations through clustering. Each unique color in the original data is represented by a copy of a cluster center from the palette, significantly reducing the information that needs to be transmitted while preserving visual appearance.
3Loss of information
If discrete palette indexes are used instead of continuous color values, then data compression is improved, but color gradient smoothness may be affected
Solution Approach 1:
The patent applies local quality by ensuring that discrete palette indexes represent locally similar colors through K-means clustering. Points with similar colors are grouped into the same cluster, so even though the representation is discrete, the local color relationships are preserved. This maintains the appearance of smooth color gradients while enabling efficient compression.
Data Source
AI summary
A method of compression of the color data of point clouds is described herein. A palette of colors that best represent the colors existing in the cloud is generated. Clustering is utilized for generating the palette. Once the palette is generated, an index to the palette is found for each point in the cloud. The indexes are coded using an entropy coder afterwards. A decoding process is then able to be used to reconstruct the point clouds.