Lidar Frame Entropy Coding for Variable Return Counts
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Solution Overview
Problem
Existing lidar data compression methods are inefficient in handling the varying number of return signals from emitted laser rays, leading to suboptimal data compression and increased storage and transmission requirements.
Innovation Solution
A method for encoding lidar data frames that involves checking for the presence or absence of lidar return signals for each emitted ray and entropy encoding the results using distinct context models for each possible index value, thereby efficiently compressing the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If traditional entropy encoding is used for lidar data, then data compression is achieved, but the compression efficiency is insufficient when handling varying numbers of return signals per ray
Solution Approach 1:
The patent applies local quality by using distinct context models for different index values (i=1, i=2, ..., i=Y) when entropy encoding lidar return signals. Each context model is tailored to the specific statistical characteristics of return signals at that index position, allowing the encoding to adapt to local variations in data distribution. This resolves the contradiction by improving compression efficiency through localized optimization rather than uniform encoding, thereby achieving better compression ratios without sacrificing data integrity.
2Productivity
If distinct context models are used for each index value, then compression efficiency is improved, but encoding complexity increases
Solution Approach 1:
The patent segments the encoding process by dividing the lidar return signal data into distinct groups based on index values (i=1, i=2, ..., i=Y). Each segment is processed independently using its own context model, which captures the specific statistical properties of that segment. This segmentation approach improves compression efficiency by treating each segment optimally while keeping the complexity manageable through modular processing of divided data segments.
3Quantity of substance
If lidar data is compressed before transmission and storage, then storage and transmission requirements are reduced, but the handling of varying return signal counts becomes more difficult
Solution Approach 1:
The patent implements dynamics by making the encoding process adaptive to the actual number of return signals received for each ray. The system dynamically selects and applies appropriate context models based on the index values present in the data, rather than using a fixed encoding scheme. This dynamic approach allows the compression to efficiently handle varying return signal counts while maintaining adaptability to different scene complexities and lidar configurations.
Data Source
AI summary
Encoding and decoding of lidar data frames is presented, and in particular to entropy coding of lidar data wherein the context models used depend on the order in which the lidar sensor receives the lidar return signals. For example, an indication whether a lidar return signal with a particular index having a value i, 1≤i≤Y, corresponding to a sequential order based on a time of arrival of lidar return signals of the emitted ray, may be encoded as an entropy coded bit using a distinct context model for each possible value i of the index.