Adaptive Bit-Width Compression for Variable Data Trends
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Solution Overview
Problem
Existing data compression methods do not effectively adapt to varying data trends, leading to inefficient data storage and communication, and may result in information loss.
Innovation Solution
A compression apparatus that determines a bit width based on data variation trends, generating frames with deviations of data groups, including a bit width ID, to minimize data loss and optimize storage and communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed bit width is used for data compression, then device complexity is reduced, but information loss increases due to inability to adapt to varying data trends
Solution Approach 1:
The patent applies dynamics by making the bit width adaptive rather than fixed. The compression algorithm dynamically determines the appropriate bit width based on the actual variation range of the data being compressed. This allows the system to adjust its compression parameters in real-time according to data characteristics, preventing information loss while maintaining reasonable device complexity through automated adaptation.
Solution Approach 2:
The patent changes the parameter of bit width from a static value to a dynamic variable that is determined based on data variation. By calculating the range of data values and selecting an appropriate bit width that accommodates the maximum deviation, the system optimizes compression efficiency while preserving data integrity. This parameter adaptation resolves the contradiction between simplicity and precision.
2Loss of information
If larger bit width is used to prevent information loss, then data precision is maintained, but data volume increases reducing compression efficiency
Solution Approach 1:
The patent optimizes the bit width parameter by calculating it based on the actual data variation range. Instead of using a uniformly large bit width that would preserve precision but increase data volume, the system determines the minimum sufficient bit width needed to represent the maximum deviation in the data. This parameter optimization achieves both precision preservation and compression efficiency.
Solution Approach 2:
The patent applies partial action by using only the necessary bit width required to represent the data variation, rather than allocating excessive bits for all possible values. By calculating the maximum deviation and selecting a bit width that accommodates just enough range, the system avoids the overhead of representing unlikely extreme values, thus reducing compressed data volume while maintaining sufficient precision.
3Loss of information
If adaptive bit width determination is implemented, then information loss is prevented, but device complexity increases due to additional calculations
Solution Approach 1:
The compression algorithm performs self-service by automatically determining the appropriate bit width based on the data it is processing. The system calculates the variation range of the input data and selects the optimal bit width without requiring external intervention or complex configuration. This self-adaptation mechanism prevents information loss while keeping the overall system complexity manageable through automated decision-making.
Solution Approach 2:
The patent applies preliminary action by performing a quick assessment of data variation range before committing to a compression scheme. By预先 (in advance) determining the maximum deviation and selecting an appropriate bit width, the system prepares the optimal compression parameters before the main compression process, preventing information loss without adding significant complexity during the actual compression operation.
4Quantity of substance
If compression is applied to reduce data volume, then storage efficiency is improved, but communication time may increase due to more complex processing
Solution Approach 1:
The patent optimizes compression parameters by dynamically adjusting bit width based on data characteristics. This parameter adaptation enables more efficient compression ratios compared to fixed-bit methods, reducing the compressed data volume that needs to be transmitted. The initial processing overhead is offset by the reduced transmission time, achieving net time savings in communication-intensive scenarios.
Solution Approach 2:
The system dynamically adapts compression parameters to match data patterns, achieving higher compression efficiency for varying data types. This dynamic approach allows the system to optimize the balance between compression ratio and processing speed, reducing overall communication time by minimizing the amount of data that needs to be transmitted despite the additional initial analysis required.
Data Source
AI summary
Provided is a compression apparatus including: a decision unit which decides, based on a variation trend of data in a data string, a bit width indicating a deviation of data; a calculation unit which calculates, with the bit width, deviations of respective pieces of data in a data group obtained by dividing the data string; a generation unit which generates a frame including: the deviations of the respective pieces of data in the data group; and information indicating the bit width; and a specification unit which specifies, from the data string, at least one continuous piece of data, a deviation of which falls within the bit width, as the data group.