Staged Data Compression for Long-Range Redundancy With Lower Memory
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Solution Overview
Problem
Current long-range data compression techniques face scalability issues in systems with multiple communications terminals, requiring large memory caches at both compression and decompression ends, leading to resource inefficiencies and increased memory demands.
Innovation Solution
A staged data compression approach involving block-level, byte-level, and short-range compression stages, using hash tables and fingerprinting to identify and eliminate redundancies, allowing for efficient compression without the need for large caches, and optimizing memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If long-range data compression techniques are used to exploit redundancies in data streams, then compression performance is improved, but memory requirements and device complexity increase significantly
Solution Approach 1:
The patent segments the data compression process into multiple stages: block-level compression (processing fixed-size blocks of data), byte-level compression (processing individual bytes), and short-range compression (processing small sequences). Each stage operates with limited memory requirements, avoiding the need for large caches while maintaining effective exploitation of redundancies at different granularities.
Solution Approach 2:
The patent introduces a multi-dimensional compression approach by operating at different levels of data granularity (block level, byte level, short-range level) simultaneously. This dimensional segmentation allows the system to exploit redundancies across various scales without requiring proportionally large memory resources at any single level.
2Productivity
If large caches are used at both compression and decompression ends to enable long-range compression, then compression ratio is improved, but device complexity and resource consumption increase
Solution Approach 1:
The compression system is divided into independent modular stages (block-level compressor, byte-level compressor, short-range compressor), each with its own small working buffer. This segmentation eliminates the need for a single large cache while maintaining the ability to exploit long-range redundancies through coordinated operation of the stages.
Solution Approach 2:
The block-level compression stage performs preliminary compression by identifying and encoding redundant blocks before passing remaining data to subsequent stages. This preliminary action reduces the data volume that needs to be processed by later stages, effectively reducing overall memory requirements while maintaining compression effectiveness.
3Productivity
If large memory caches are deployed to support multiple communications terminals, then compression effectiveness is improved, but resource efficiency decreases
Solution Approach 1:
Each communications terminal runs independent instances of the multi-stage compressor with small local buffers, eliminating the need for large shared caches. This segmentation allows effective compression to be achieved with minimal memory resources per terminal, improving overall resource efficiency in multi-terminal systems.
Data Source
AI summary
Approaches for staged data compression are provided, where each stage reflects a progressive increase in granularity, resulting in a scalable approach that exhibits improved efficiency and compression performance. The first stage comprises a long-range block-level compressor that determines redundancies on a block-level basis (based on entire data blocks, as opposed to partial segments within data blocks). The second stage comprises a long-range byte-level compressor that compresses an uncompressed block based on byte segments within the block that match previously transmitted segments. The duplicate segments are replaced with pointers to matching segments within a decompressor cache. Nonmatching segments of the data block are left uncompressed and passed to a third stage short-range compressor (e.g., a grammar-based compressor). The staged progression in granularity provides advantages of maximizing the compression gain while minimizing processing and storage requirements of the compressor and decompressor.