Multimedia Encoder Parameter Optimization via Regression Tables
Find Innovative SolutionsGenerate Solutions
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 encoding constraints, storage, and transfer limitations.
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 encoding parameters that balance fidelity, size, and flow rate, employing analytical functions to model the dependence on quantization, resolution, and frame rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If encoding parameters are optimized for highest fidelity index, then signal quality is improved, but encoded signal size increases
Solution Approach 1:
The patent changes encoding parameters (quantization granularity, display resolution, frame rate) to find the optimal balance between fidelity index and encoded signal size. By systematically varying these parameters and using regression analysis to model their effects, the system identifies parameter settings that achieve high fidelity while controlling file size within constraints.
2Quantity of substance
If encoding parameters are optimized for minimal encoded signal size, then storage efficiency is improved, but fidelity index decreases
Solution Approach 1:
The patent adjusts encoding parameters to minimize encoded signal size while maintaining fidelity above a required threshold. The regression models allow the system to predict how changes in quantization granularity, resolution, and frame rate will affect both size and fidelity, enabling optimization for compact storage while preserving acceptable quality.
3Speed
If encoding parameters are optimized for highest frame rate, then temporal resolution is improved, but encoded signal flow rate increases
Solution Approach 1:
The patent varies the frame rate parameter along with other encoding parameters to optimize the balance between temporal resolution and flow rate. The regression analysis models the combined effect of frame rate, quantization granularity, and display resolution on both visual quality and bit rate, allowing the system to select parameter combinations that achieve desired frame rates while controlling data transmission requirements.
4Productivity
If complex regression analysis and granular table generation are implemented, then encoding parameter optimization is improved, but computational complexity increases
Solution Approach 1:
The patent performs regression analysis and generates granular tables of encoding parameter effects in advance, before actual encoding operations. By pre-computing the relationships between encoding parameters and output quality/size metrics, and storing these in lookup tables, the system reduces the computational burden during real-time encoding to simple table queries and parameter selections.
Solution Approach 2:
The patent creates simplified representations (granular tables) that copy and store the results of complex regression analyses. These tables serve as pre-computed models that capture the relationships between encoding parameters and output properties, allowing the system to use lightweight table lookups instead of performing heavy computational analysis during actual encoding operations.
Data Source
AI summary
Methods and apparatus for determining encoding parameters of an encoder or a transcoder which yield an encoded signal of optimal measurable properties are disclosed. For a video signal, the encoding parameters may include quantization granularity, a measure of display resolution, and a frame rate. The measurable properties of an encoded signal may include a fidelity index, a relative size, and a relative flow rate. Reference data records quantifying properties of sample signals encoded according to experimental sets of encoding parameters are used to define parameters of conjectured analytical functions characterizing the encoding or transcoding functions. The analytical functions are then used to generate granular tables of estimated measures of encoded-signal properties. A fast search mechanism relies on the granular tables, together with sorted arrangements of the granular tables, to determine, in real-time, preferred encoding parameters for multimedia data streams received at an encoder or a transcoder.