Codec Workspace Seeding for Compression Efficiency
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Solution Overview
Problem
Current data compression and decompression techniques, particularly in network environments, face inefficiencies due to resource consumption and varying complexity levels between compression and decompression processes, which can impact performance and resource utilization.
Innovation Solution
The use of seeding data to generate workspace data for codec operations, where the seeding data is determined based on the characteristics of the data and the codec, optimizing the initial conditions for both compression and decompression processes, and enabling synchronization across network devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is performed using traditional codec operations, then data size is reduced, but resource consumption (memory, CPU, network bandwidth) increases during compression and decompression processes
Solution Approach 1:
The patent applies preliminary action by pre-computing workspace data using seeding data before actual compression operations. This workspace data is prepared in advance and stored, eliminating the need to perform computationally intensive operations during real-time compression and decompression, thereby reducing resource consumption while maintaining compression effectiveness
Solution Approach 2:
The patent changes the parameter state by transforming seeding data into pre-optimized workspace data with specific characteristics tailored to the codec algorithm. This parameter transformation creates a specialized data structure that enables faster processing and reduced resource usage during actual compression operations
2Productivity
If codec operations are performed without pre-optimized workspace data, then implementation is simpler, but processing speed and adaptation to specific data types is slower
Solution Approach 1:
The patent performs preliminary action by pre-computing workspace data using seeding data before actual compression operations. This workspace data is prepared in advance and stored, eliminating the need to perform computationally intensive operations during real-time compression and decompression, thereby reducing resource consumption while maintaining compression effectiveness
Solution Approach 2:
The system performs self-service by automatically generating workspace data from seeding data based on the specific codec algorithm and data characteristics. This self-optimization process creates tailored workspace configurations without requiring manual intervention, balancing the trade-off between processing speed and implementation complexity
Data Source
AI summary
Various embodiments are directed toward compressing and/or decompressing data communicated between one or more network devices (e.g., codec operations). In particular, embodiments are directed towards improving codec performance by seeding the computation workspace that may be used by various codec processors. The seeding data may be determined based on at least one characteristic of a particular codec and the characteristics of data that may be processed by the codec processor. Also, the codec processor may be employed to generate data for the codec workspace based on the determined seeding data. Workspace data may be generated by processing the seeding data with the same codec processor that is used for normal codec operations. The workspace generated from the seeding data may be stored for future use, such as, when a matched data stream arrives.