Sensor Data Compression with Fast Random-Access Decompression
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Solution Overview
Problem
Current data compression techniques face challenges in ensuring a bounded data size for random data samples, providing fast decompression, handling different data types, and ensuring random access within a deterministic time interval, particularly in advanced driver assistance systems and gaming applications using DDR memory.
Innovation Solution
A method for dynamically configurable online lossless compression and decompression of rasterized sensor data, where binary sensor data is divided into data groups, and differences are calculated with precedent values to generate Compression Information Packets (CIPs) for lossless recovery, allowing single clock cycle decompression and random access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If lossless compression techniques are used to ensure precise duplication of data, then data integrity is improved, but compression speed deteriorates compared to lossy techniques
Solution Approach 1:
The patent segments sensor data into fixed-size data groups (e.g., 8-bit groups) and processes each group independently with uniform compression rules. This segmentation enables parallel processing and predictable timing, resolving the contradiction by maintaining lossless integrity through systematic group-by-group differential encoding while achieving fast compression through standardized group handling without complex adaptive algorithms.
Solution Approach 2:
The patent changes the parameter representation by encoding data groups as differences from a precedent value rather than storing raw values. This parameter transformation enables more compact representation while maintaining lossless reconstruction capability, achieving both data integrity and improved compression ratio with deterministic timing.
2Quantity of substance
If compressed data is stored to reduce storage size, then storage capacity utilization is improved, but random access time increases
Solution Approach 1:
The patent organizes compressed data into fixed-size data groups with predetermined boundaries and uniform encoding schemes. This segmentation enables direct calculation of group positions and offsets, allowing random access to any data group without sequential decoding of preceding groups, thus maintaining fast random access while achieving compression.
Solution Approach 2:
The patent performs preliminary organization of data into fixed-size groups with predetermined compression parameters before storage. This preliminary structuring enables direct addressing and random access to any group without requiring decompression of previous data, resolving the contradiction between compression and random access speed.
3Quantity of substance
If complex compression algorithms are used to achieve higher compression ratios, then data size is reduced, but device complexity increases
Solution Approach 1:
The patent applies a consistent parameter transformation (differential encoding from precedent value) across all data groups with fixed parameters. This uniform parameter change approach achieves reliable compression without requiring complex adaptive algorithms, resolving the contradiction by maintaining simplicity while achieving effective compression through systematic differential encoding.
Solution Approach 2:
The patent uses a simple copying mechanism where the first value of each data group is copied as the precedent value for subsequent groups. This simple copying approach, combined with differential encoding, achieves effective compression without complex algorithms, resolving the contradiction between compression ratio and device complexity.
4Measurement precision
If lossless compression is applied to ensure no information loss, then data accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments data into fixed-size groups processed independently with uniform lossless differential encoding. This segmentation enables parallel processing and predictable timing, achieving both data accuracy through lossless reconstruction and reduced processing time through standardized group handling without complex sequential analysis.
Solution Approach 2:
The patent applies deterministic parameter transformation (differential encoding) that can be rapidly computed and reversed. This parameter change approach maintains full data accuracy while enabling fast processing through simple arithmetic operations, resolving the contradiction between accuracy and processing time.
Data Source
AI summary
A method of data compression includes obtaining binary sensor data having rows with multi-bit data samples. The rows are divided into data groups each including two or more samples. A precedent value is selected for the rows or respective precedent values are selected for each data group. A compressed row of compressed sensor data is generated from each row by calculating differences between the data sample and the precedent value for its associated data groups. A Compression Information Packet (CIP) is generated for each row including information for returning the binary sensor data that includes a compressed predicate indicating whether each data group is stored compressed, a data group size being a multi-bit value that stores a group size used for row compression, and a compressed word size that stores a dynamic range of the row compression. The compressed rows are stored as stored compressed data along with the CIPs.