Limited Error Raster Compression for LiDAR Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional surface mapping technologies face challenges in compressing high-resolution raster data, such as LiDAR-generated elevation data, due to large file sizes and inefficiencies in existing compression methods, which result in either ineffective lossless compression or significant lossy compression errors.
Innovation Solution
The Limited Error Raster Compression (LERC) method, which receives pixel data and defines a maximum error allowable per pixel, uses a no-transform compression algorithm to quantize and encode data, optimizing block sizes and employing Look-Up Tables for efficient compression, thereby achieving lossy or lossless compression with controlled error levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lossless compression (e.g., LZW) is used on high detail raster data, then data precision is preserved, but compression effectiveness is poor due to large file sizes and noise
Solution Approach 1:
The patent applies parameter changes by transforming the raster data from its original format into a different parameter space using coordinate transformations. This allows the data to be represented in a way that enables more effective compression while maintaining the ability to reconstruct the original data within acceptable error bounds, thus resolving the contradiction between preserving precision and achieving compression.
Solution Approach 2:
The patent extracts and separates the essential information from the redundant or noisy data in high-resolution rasters. By identifying and removing unnecessary details that do not contribute to the core information content, the system achieves better compression ratios while preserving the meaningful data precision required for the application.
2Productivity
If lossy compression methods (e.g., jpg) are used to achieve better compression ratios, then file size is reduced, but compression errors become arbitrarily large for individual pixels
Solution Approach 1:
The patent implements feedback mechanisms that monitor and control the compression error at each stage of the process. By continuously assessing the error introduced during compression and adjusting the compression parameters accordingly, the system ensures that pixel errors remain within acceptable bounds while achieving improved compression ratios compared to traditional lossy methods.
Solution Approach 2:
The patent applies dynamic adjustment of compression parameters based on the local characteristics of the raster data. Different regions of the image are compressed with different levels of aggressiveness depending on their content, allowing the system to achieve better overall compression ratios while maintaining precision in critical areas and tolerating larger errors in less important regions.
3Measurement precision
If contemporary high resolution raster data is created with small cell sizes and shallow vertical resolution, then detail and content richness is improved, but data file size becomes very large
Solution Approach 1:
The patent divides the high-resolution raster data into segments or blocks that can be processed and compressed independently. This segmentation allows the application of different compression strategies to different parts of the data, optimizing the balance between preserving detail resolution and reducing overall file size by targeting compression efforts where they are most effective.
Data Source
AI summary
Embodiments of the invention relate to an image or raster compression method for a multi-dimensional array of pixels. A user specifies a maximum error allowable per pixel for the compression algorithm. The raster is divided into a number of pixel blocks where each pixel is quantized and bit stuffed based on a number of block statistics including the maximum error allowable. The size of the pixel blocks is limited to two (e.g., 8×8 and 16×16) to save processing time with little difference in compression. A Look-Up Table (LUT) is used instead for certain types of data where it is more efficient.