Context Model Grouping for Lower-Overhead Data Encoding
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Solution Overview
Problem
Transmitting large amounts of data consumes significant communication and networking resources, and existing methods are inefficient due to the format of data representation.
Innovation Solution
A computing system determines the frequencies of succeeding symbols in a data block, generates clusters based on these frequencies, and encodes symbols using variable length codewords associated with each cluster, reducing the resources required for data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If data is transmitted using traditional fixed-length encoding formats, then data representation is simple and consistent, but transmission consumes significant communication and networking resources
Solution Approach 1:
The patent segments the data encoding process by dividing symbols into different clusters based on their frequency distributions. Each cluster is assigned a separate codebook with optimized variable-length codewords. This segmentation allows the system to apply different encoding strategies to different symbol groups, reducing the average codeword length and thus minimizing communication resource consumption while managing encoding complexity through structured organization.
Solution Approach 2:
The patent applies local quality by generating context-specific codebooks for different symbol clusters rather than using a single uniform encoding scheme. Each cluster's codebook is tailored to the specific frequency distribution characteristics of that cluster, optimizing the codeword length for local symbol patterns. This localized optimization reduces overall transmission overhead while the systematic clustering approach keeps the encoding process manageable.
2Quantity of substance
If variable length codewords are used to reduce transmission size, then communication resources are minimized, but the encoding and decoding process becomes more complex
Solution Approach 1:
The patent performs preliminary action by pre-calculating and organizing codebooks for multiple symbol clusters before actual data encoding. The system analyzes frequency distributions, identifies clusters, and generates optimized codebooks in advance. This preliminary preparation stores the encoding mappings in structured formats, allowing the actual encoding process to simply look up and apply pre-determined codewords, thereby reducing real-time computational complexity while achieving compact variable-length encoding.
Solution Approach 2:
The patent introduces dynamics by adapting the encoding scheme to the specific characteristics of the input data. The system dynamically determines which cluster each symbol belongs to based on frequency distribution analysis, and selects the appropriate codebook for that cluster. This dynamic adaptation allows the encoder to optimize codeword lengths according to actual symbol patterns in the data, minimizing transmission size while the systematic cluster-based approach maintains manageable complexity through structured decision-making.
Data Source
AI summary
A non-transitory computer-readable storage medium comprising instructions stored thereon. When executed by at least one processor, the instructions may be configured to cause a computing system to at least determine, for each of a plurality of current symbols in a data block, frequencies of succeeding symbols within the data block, generate a plurality of clusters, each of the plurality of clusters including a subset of the plurality of current symbols, generate, for each of the clusters, a code, the code including variable length codewords for each of the succeeding symbols of the current symbols included in the respective cluster, and encode each of the succeeding symbols in the data block based on the code for the cluster that includes the succeeding symbol's respective current symbol.