Image Encoding Quantization Parameter Lookup Table
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Solution Overview
Problem
Conventional image-encoding controllers require large computations for complexity and quantization parameter calculations, leading to overloads, errors, and inadequate image encoding, which results in improper encoding and failure to achieve target bit rates and uniform image quality.
Innovation Solution
An image-encoding controlling apparatus and method utilizing a table that statistically reflects the frequency of quantization parameter selection based on target bit quantities, allowing for efficient selection and application of optimized quantization parameters, reducing computational load and error potential.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mathematical quantization parameter calculation models are used to calculate appropriate quantization parameters, then the target bit rate can be controlled, but the computational load increases significantly causing overloads and errors
Solution Approach 1:
The patent pre-calculates and stores quantization parameter mappings in lookup tables during system initialization or training phases. During actual image encoding, the controller directly queries these pre-computed tables based on target bit rate requirements, avoiding real-time mathematical calculations and significantly reducing computational load while maintaining accurate quantization parameter selection
Solution Approach 2:
The patent creates simplified copy representations of complex quantization parameter relationships by storing pre-computed mapping data in lookup tables. Instead of performing complex mathematical model calculations during encoding, the system uses these tabular copies to directly obtain quantization parameters, reducing computational complexity while preserving the essential mapping relationships between target bit rates and appropriate quantization parameters
2Measurement precision
If complexity calculation is performed to control bit output quantity, then the target bit rate can be maintained, but the computational complexity increases causing overloads and improper encoding
Solution Approach 1:
The patent pre-computes and stores the relationships between image complexity metrics, target bit rates, and appropriate quantization parameters in lookup tables. During encoding operations, the system performs simple table lookups based on measured complexity and target bit rate, avoiding complex real-time calculations while maintaining accurate bit rate control
Solution Approach 2:
The patent introduces lookup tables as intermediary structures that mediate between image complexity measurements and quantization parameter selection. These tables pre-process and store the complex relationships, allowing the encoding system to use simple queries instead of performing complex calculations, thus reducing device complexity while maintaining control accuracy
3Measurement precision
If mathematical models are used for quantization parameter estimation, then theoretical optimality can be achieved, but errors increase due to model limitations and computational overloads
Solution Approach 1:
The patent replaces mathematical model calculations with pre-computed tabular data that captures the actual performance characteristics of the encoding system under various conditions. This empirical approach eliminates model estimation errors and provides more reliable quantization parameter selection based on real-world performance data stored in lookup tables
Solution Approach 2:
The patent implements feedback mechanisms where the encoding system monitors actual encoding results and uses this information to refine or update the lookup tables. This feedback loop ensures that the pre-computed tables remain accurate and reliable, adapting to different image characteristics and encoding conditions while maintaining high encoding reliability
Data Source
AI summary
An image-encoding method and apparatus is provided for using a table statistically reflecting a selection frequency of a quantization parameter. The method includes the steps of preparing a table representing a relationship among a target bit quantity, a quantization parameter and a selection frequency as a table for statistically reflecting a frequency of selecting a quantization parameter according to a target bit quantity. The method further includes the steps of searching for a maximum selection frequency among the selection frequencies corresponding to an input target bit quantity by referring to the table, and selecting a quantization parameter corresponding to the input target bit quantity and the searched maximum selection frequency as an optimized quantization parameter to thereby prevent inadequate image-encoding due to large amounts of computations and provide a target bit rate and uniform image quality.