Context-Dependent Dictionary Encoding for Higher Compression Density
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Solution Overview
Problem
Transmitting large amounts of data consumes significant communication and networking resources, and existing methods are inefficient in optimizing data representation for reduced resource usage.
Innovation Solution
A computing system that determines context states in data files to identify and use context-dependent indexed dictionaries and transformation sets for encoding and decoding data, allowing for efficient data compression and transmission by selecting the most relevant dictionaries and transformations based on the data context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single fixed dictionary is used for encoding data, then the encoding process is simple and fast, but the compression efficiency is low because it cannot adapt to different data contexts
Solution Approach 1:
The patent implements dynamic dictionary selection by maintaining multiple indexed dictionaries and selecting the appropriate dictionary based on the context state determined from preceding data segments. This allows the encoding system to adapt to different data contexts dynamically, improving compression efficiency without requiring a completely static or manually configured system.
Solution Approach 2:
The system changes the parameter of dictionary selection based on context state. By determining context states from preceding data segments and using these states to select from multiple indexed dictionaries, the system optimizes compression efficiency for different data patterns while managing complexity through structured parameter-based selection.
2Quantity of substance
If context-dependent indexed dictionaries are used to improve compression efficiency, then more data can be compressed in less space, but the encoding and decoding process becomes more complex and computationally intensive
Solution Approach 1:
The patent segments the data into segments and determines context states for each segment based on preceding data. This segmentation approach allows the system to apply context-dependent dictionary selection at manageable intervals, improving compression density while controlling complexity by processing data in discrete units rather than requiring continuous complex analysis.
Solution Approach 2:
The system performs preliminary context state determination based on preceding data segments before selecting the appropriate dictionary for encoding the current segment. This preliminary action allows the encoder to prepare the optimal dictionary in advance, improving compression efficiency without adding significant runtime complexity during the actual encoding process.
3Productivity
If multiple indexed dictionaries are maintained for different contexts, then compression efficiency improves, but the memory requirements and system resources increase
Solution Approach 1:
The patent creates multiple indexed dictionaries that can serve different contextual purposes, allowing a single encoding system to handle diverse data patterns efficiently. These dictionaries are organized and selected based on context states, enabling the system to achieve high compression efficiency across different data types without requiring completely separate encoding systems for each context.
Data Source
AI summary
An context-based encoding mechanism uses a predetermined number of bytes in a previous segment of a file to determine a context for the current segment. The current segment is encoded using a dictionary that corresponds to the determined context. An example method includes determining, for a first segment in a data file, a first context state based on a first context segment within the data file that precedes the first segment, identifying a first indexed dictionary from a plurality of indexed dictionaries based on the first context state, and encoding the first segment using the identified first indexed dictionary.