Spatial Data Compression Using Space-Filling Curve Encoding
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Solution Overview
Problem
Conventional compression algorithms fail to effectively leverage spatial coherence in spatial data, leading to inefficient compression due to misalignment of data arrangement in memory compared to its spatial distribution.
Innovation Solution
The technique arranges spatial data in memory using space-filling curves like Hilbert traversal to preserve spatial coherence, iteratively splitting and encoding data blocks to exploit similarities, employing test values and modifications to enhance compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional compression algorithms are used on spatial data, then the data can be compressed, but the compression efficiency is poor because spatial coherence is not leveraged
Solution Approach 1:
The patent transforms spatial data from its original multi-dimensional arrangement into a one-dimensional sequence using space-filling curves (Hilbert, Morton, or snake traversal). This dimensional transformation preserves spatial coherence by ensuring that points close in space remain close in the one-dimensional sequence, enabling conventional compression algorithms to effectively exploit spatial relationships that were previously distributed across multiple dimensions.
Solution Approach 2:
The patent divides the spatial data into multiple blocks or segments, applies space-filling curve transformation to each block independently, and then processes them through compression iterations. This segmentation allows the algorithm to handle large spatial datasets efficiently while maintaining local spatial coherence within each block, improving overall compression efficiency.
2Ease of operation
If spatial data is arranged in memory according to conventional methods, then data access is straightforward, but spatial coherence is lost and compression efficiency deteriorates
Solution Approach 1:
The patent introduces a transformation layer that converts multi-dimensional spatial coordinates into one-dimensional memory indices using space-filling curves. This allows data to be stored in conventional one-dimensional memory structures while preserving spatial relationships, achieving both ease of operation (standard memory access) and improved compression efficiency (spatial coherence preservation).
3Loss of information
If the number of compression iterations is increased to improve compression ratio, then more bits are required for encoding, but the overall efficiency decreases
Solution Approach 1:
The patent performs space-filling curve transformation and data reorganization as a preliminary step before compression. This preliminary action structures the data to maximize spatial coherence, which enables the compression algorithm to achieve high compression ratios in fewer iterations, thereby reducing the total encoding time while maintaining or improving compression ratio.
Data Source
AI summary
This document relates to the compressing and/or decompressing of spatial data. A block of spatial data can be compressed by iteratively performing compression iterations on portions of the block and splitting the portions into further portions until compression is completed. The compressed data can include first encoded values indicating whether matches were obtained for comparisons to test values during the compression iterations. The compressed data can also include second encoded values reflecting results of one or more modifications performed on the test values during the compression iterations.