Limited Error Raster Compression for LiDAR Data
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Solution Overview
Problem
Conventional compression methods are ineffective for high-resolution raster data, such as LiDAR-generated elevation data, due to large file sizes and inefficiencies in lossless and lossy compression techniques, which hinder real-time processing and transfer of detailed geographic and scientific data.
Innovation Solution
The Limited Error Raster Compression (LERC) method, which divides pixel data into blocks, quantizes values based on user-defined error thresholds, and bit-stuffs unsigned integers into byte arrays, allowing for efficient lossy compression while maintaining acceptable error levels, thereby reducing file sizes and improving encoding speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If lossless compression (e.g., LZW) is used on high-resolution raster data, then data integrity is maintained, but compression effectiveness is poor and file sizes remain large
Solution Approach 1:
The patent applies parameter changes by transitioning from lossless compression parameters to lossy compression parameters, specifically by quantizing pixel values to controlled error levels. This allows achieving significant file size reduction while maintaining data quality within acceptable bounds for geographic and scientific applications.
Solution Approach 2:
The patent discards minimal pixel information through quantization that introduces controlled errors within user-defined thresholds. The essential spatial patterns and features are preserved while discarding insignificant high-frequency noise, achieving compression without losing meaningful data.
2Quantity of substance
If lossy compression methods (e.g., jpg) are used, then file sizes are reduced, but compression errors can be arbitrarily large for individual pixels
Solution Approach 1:
The patent implements feedback control by continuously monitoring pixel errors during compression and adjusting quantization levels to ensure errors remain within user-defined thresholds. This feedback mechanism prevents arbitrarily large compression errors while maintaining effective compression ratios.
Solution Approach 2:
The patent applies partial quantization action by selectively quantizing only those pixel values that exceed the error threshold, leaving other values unchanged. This partial action approach maintains precision where needed while achieving compression where permissible.
3Quantity of substance
If high cell size is used in conventional mapping, then data file size is reduced, but detail and content of above ground features are lost
Solution Approach 1:
The patent changes the resolution parameter from coarse to fine cell sizes, enabling capture of detailed above-ground features while applying lossy compression to manage file size. The compression operates on the fine-resolution data rather than forcing coarse resolution, preserving feature detail.
4Manufacturing precision
If small cell size and shallow vertical resolution are used to capture detailed features, then feature detail is improved, but data file size becomes very large
Solution Approach 1:
The patent applies parameter changes by transitioning from lossless to lossy compression modes, and by adjusting quantization error thresholds to balance file size and detail preservation. This allows maintaining fine cell sizes for feature detail while controlling file size through controlled information discarding.
Data Source
AI summary
Embodiments of the invention relate to an image or raster compression method that includes receiving pixel data for a raster comprising a two dimensional (2D) array of pixels where each pixel is associated with a data value. The method further includes receiving a user defined parameter defining a maximum error allowable per pixel for a compression algorithm. The raster can be divided into a number of pixel blocks where each pixel can be quantized and bit stuffed based on a number of block statistics including the maximum error allowable. The method further includes executing the compression algorithm wherein for each pixel, where an error caused by the compression algorithm is equal to or less than the maximum error allowable, and encoding the pixel data based on the compression algorithm. In certain embodiments, the compression algorithm is a non-transform compression algorithm.