Dictionary Compression Using Zbuffer for Parallel Phrase Encoding
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Solution Overview
Problem
Traditional dictionary-based data compression techniques face challenges due to computational complexity and serial processing, which limits their efficiency in compressing and decompressing data effectively, especially in parallel compute environments.
Innovation Solution
The use of a Zbuffer and stencil buffer in conjunction with a dictionary-based approach to identify and encode overlapping occluded phrases, allowing for parallel processing and efficient compression and decompression by preassigning lowest-ranking phrases and storing wildcard phrases, enabling the encoding and decoding of data in a more efficient manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional dictionary-based compression techniques are used, then data compression is achieved, but computational complexity increases and processing becomes serial
Solution Approach 1:
The patent segments the data stream into multiple independent blocks that can be processed in parallel. Each block is divided into substrings that are independently encoded using dictionary-based compression, allowing simultaneous processing of multiple segments without inter-dependency, thus reducing serial processing constraints while maintaining compression effectiveness
Solution Approach 2:
The patent performs preliminary actions by pre-processing the data stream to identify and mark potential phrase boundaries before compression. This preliminary segmentation and preparation of data structures enables subsequent parallel processing stages to operate more efficiently without requiring complex runtime decisions, reducing overall computational complexity
2Speed
If traditional serial processing is used, then computational complexity is managed, but processing speed decreases
Solution Approach 1:
The patent divides the compression task into multiple independent segments that can be processed simultaneously using parallel computing resources. By segmenting the data stream and using multiple processing units to work on different segments at the same time, the overall processing speed increases while the time required for compression decreases proportionally to the number of parallel processors utilized
3Productivity
If overlapping phrases are processed, then compression efficiency improves, but computational complexity increases
Solution Approach 1:
The patent performs preliminary identification and marking of overlapping phrase candidates before the main compression process. By pre-processing the data to identify potential overlaps and preparing appropriate data structures in advance, the system can efficiently handle overlapping phrases during parallel processing without incurring excessive computational complexity during the actual compression stage
Data Source
AI summary
Approaches to dictionary-based data compression are disclosed, including those in which a Zbuffer is used to identify phrases that represent uncompressed data and/or to generate decompressed data by combining a set of overlapping occluded phrases.