Semi-Dynamic Backend Coding for Low-Latency Data Compression
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Solution Overview
Problem
Existing data compression methods face challenges in balancing compression performance and latency, with static coders offering simple but non-optimized ratios and dynamic coders improving ratios but introducing latency and requiring extra bits and complex formats.
Innovation Solution
A semi-dynamic coding scheme that adapts statistics models in real-time based on compression/decompression performance data, using a two-stage compression and decompression system with parallel bit encoders to select optimal encoding techniques for each data chunk, reducing latency and optimizing compression ratios without additional metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a dynamic backend coder is used to improve compression ratio, then compression performance is improved, but latency increases due to statistics updating
Solution Approach 1:
The patent implements a semi-dynamic coder that transitions from static to dynamic encoding based on data characteristics. The system initially uses static encoding for fast processing, then switches to dynamic encoding when statistical patterns are detected, thereby optimizing compression ratio while minimizing latency through selective adaptation.
Solution Approach 2:
The patent divides the compression process into distinct stages: an initial static encoding phase and a subsequent dynamic encoding phase. This segmentation allows the system to benefit from both approaches - the speed of static encoding and the efficiency of dynamic encoding - by applying each method at the appropriate stage of data processing.
2Manufacturing precision
If a dynamic backend coder is used to improve compression ratio, then compression performance is improved, but device complexity increases due to extra bits and complex formats
Solution Approach 1:
The patent extracts only the essential statistical information needed for dynamic encoding while discarding unnecessary data. By selectively capturing and storing only the most relevant statistical patterns from the input data, the system achieves improved compression without requiring complex metadata structures or excessive additional bits.
Solution Approach 2:
The patent changes the encoding parameters dynamically based on the statistical characteristics of the input data. Instead of using fixed encoding schemes, the system adapts parameters such as symbol frequencies and probability distributions to match the actual data patterns, thereby improving compression ratio without requiring complex file formats to store encoding instructions.
3Productivity
If a static backend coder is used, then processing speed is maintained, but compression ratio is not optimized
Solution Approach 1:
The patent implements a semi-dynamic coder that transitions from static to dynamic encoding based on data characteristics. The system initially uses static encoding for fast processing, then switches to dynamic encoding when statistical patterns are detected, thereby optimizing compression ratio while minimizing latency through selective adaptation.
Solution Approach 2:
The patent performs preliminary statistical analysis on the input data to determine whether dynamic encoding would be beneficial. By pre-assessing the data characteristics and preparing appropriate encoding strategies in advance, the system can quickly switch to optimized encoding modes without significant processing delays, thus maintaining high processing speed while improving compression ratio.
Data Source
AI summary
Methods and systems are provided for the compression and decompression of data. The compression and decompression of data may include partitioning the data into chunks, analyzing the individual chunks to determine the best compression and decompression encoders to utilize for the next data chunk of a data file. In compressing and decompressing using the mentioned technique, the data is delivered to the requesting client in an efficient and speedy manner.