Type-Specific Data Compression Models for Faster Arithmetic Encoding
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Solution Overview
Problem
Current data compression methods using arithmetic encoding face challenges with high memory overheads and long encoding times due to the need to compress data based on a large number of data compression models.
Innovation Solution
The proposed solution involves identifying the data type of the to-be-compressed data and selecting a subset of data compression models specific to that type, rather than using all available models, thereby reducing the computational burden and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all data compression models are used for arithmetic encoding, then compression rate is improved, but memory overhead and encoding time increase significantly
Solution Approach 1:
The patent segments the large set of data compression models into multiple smaller model sets, where each model set contains a subset of compression models. Instead of loading all models into memory simultaneously, the system loads only the relevant model set corresponding to the data type being compressed, thereby reducing memory overhead while maintaining compression effectiveness.
Solution Approach 2:
The patent implements dynamic model set selection based on data type identification. The system dynamically determines which model set to load into memory based on the type of data being compressed (e.g., text, image, audio), ensuring that only necessary models are loaded at any given time, thus optimizing memory usage adaptively.
2Loss of information
If all data compression models are used for arithmetic encoding, then compression rate is improved, but encoding time increases significantly
Solution Approach 1:
By segmenting models into type-specific sets, the system reduces the number of models that need to be evaluated during encoding. The encoder only searches within the relevant model set for the current data type, significantly reducing the search space and encoding time compared to evaluating all models.
Solution Approach 2:
The system performs preliminary data type identification before the actual compression process. This preliminary action determines which model set should be loaded and used, avoiding the time-consuming process of evaluating all models during compression and enabling faster encoding.
3Loss of time
If a subset of data compression models is used, then memory overhead and encoding time are reduced, but compression rate may be compromised
Solution Approach 1:
The patent applies local quality by creating specialized model sets optimized for specific data types. Each model set contains compression models that are particularly effective for its corresponding data type, ensuring high compression rates within each category while maintaining overall system efficiency.
Solution Approach 2:
The system changes the parameter of model selection based on data type characteristics. By identifying the data type and selecting the corresponding model set, the system adapts the compression approach to match the specific properties of the input data, maintaining optimal compression rates for different data types.
Data Source
AI summary
This application discloses a data compression method and a computing device. The disclosed method is applied to the computing device. The method includes receiving, by the computing device, to-be-compressed data, and identifying a data type of the to-be-compressed data. The method further includes selecting one or more data compression models based on the identified data type and compressing the to-be-compressed data based on the selected one or more data compression models.