Signal Encoding Parameter Optimization
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Solution Overview
Problem
Multimedia systems face challenges in signal encoding and transcoding due to interoperability issues between heterogeneous terminals, requiring adaptive techniques that optimize performance while considering constraints on encoding, storage, and transfer of signals, particularly in multimedia messaging services and video streaming.
Innovation Solution
The method determines optimal encoding parameters for multimedia data streams by using reference data records and predictive regression analysis to generate granular tables, allowing for efficient selection of parameters that balance fidelity index, size, and flow rate, utilizing analytical functions to model the encoding process and facilitate fast search mechanisms for real-time implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If encoding parameters are optimized for highest fidelity index, then signal quality is improved, but encoded signal size and flow rate increase
Solution Approach 1:
The patent applies parameter changes by systematically varying encoding parameters (quantization granularity, display resolution, frame rate) to find optimal combinations that balance fidelity index against signal size and flow rate constraints. The system uses predictive regression analysis to model how parameter changes affect multiple output properties simultaneously, enabling selection of parameter sets that achieve desired fidelity while maintaining acceptable size and bandwidth utilization.
2Manufacturing precision
If encoding parameters are optimized for highest fidelity index, then signal quality is improved, but flow rate increases
Solution Approach 1:
The system uses parameter changes to adjust encoding settings dynamically based on available bandwidth and quality requirements. By modeling the relationship between encoding parameters and flow rate through predictive regression, the system can select parameter combinations that maintain high fidelity index while adapting flow rate to match network capacity and user requirements.
3Quantity of substance
If encoded signal size is minimized, then storage efficiency is improved, but fidelity index decreases
Solution Approach 1:
The patent applies parameter changes in reverse optimization mode, where the system starts with size constraints and adjusts encoding parameters to minimize signal size while maintaining fidelity index above a required threshold. The predictive regression models enable the system to estimate the fidelity impact of size-reducing parameter adjustments, allowing selection of parameter sets that achieve storage efficiency goals without excessive quality degradation.
4Productivity
If flow rate is minimized, then bandwidth utilization is improved, but fidelity index and signal quality decrease
Solution Approach 1:
The system uses parameter changes to optimize for low-bandwidth scenarios by adjusting encoding parameters to minimize flow rate while maintaining fidelity index above acceptable thresholds. The predictive regression analysis allows the system to forecast the quality impact of bandwidth-constraining parameter selections, enabling selection of parameter combinations that achieve efficient bandwidth utilization while preserving adequate signal quality for the given network conditions.
Data Source
AI summary
Methods of optimal encoding of signals to be compatible with characteristics of target receivers while meeting constraints pertinent to sizes of encoded signals or capacities of paths communicating signals to the target receivers are disclosed. The methods are based on analytical modeling of the encoding process guided by experimental data relating measured performance indicators of encoded signals of diverse classifications to respective encoding parameters. A computationally-efficient technique is devised to determine optimal encoding parameters based on pre-processed data derived from the analytical models. The methods may be implemented at an encoder of original signals or a transcoder of pre-encoded signals.