Image-Guided AI Parameter Tuning for Super-Resolution Frames
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Solution Overview
Problem
Modern edge devices, such as smartphones and smart TVs, face limitations in computing power and flexibility, leading to suboptimal image enhancement quality due to pre-configured algorithms that struggle with diverse input image contents and qualities, resulting in blurred, noisy, or distorted output images during super-resolution operations.
Innovation Solution
An image processing circuit with a memory to store multiple AI model parameter sets, an image guidance module to detect representative features, and an SR engine that adjusts AI model parameters in real-time to generate high-resolution images, allowing for flexible adaptation to varying input image conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If pre-configured algorithms are used for super-resolution operations, then device complexity is reduced, but image quality deteriorates due to inability to adapt to diverse input contents
Solution Approach 1:
The patent implements dynamic parameter adjustment in super-resolution algorithms by introducing an image quality assessment module that analyzes input image characteristics and automatically adjusts processing parameters in real-time. This transforms the static pre-configured algorithm into a dynamic system that adapts to diverse input contents, resolving the contradiction between simplicity and quality.
Solution Approach 2:
The system changes processing parameters based on detected image features such as noise levels, blur程度, and content type. By dynamically modifying parameters like regularization strength, denoising intensity, and sharpness enhancement based on input image analysis, the system maintains high image quality without requiring complex device architecture.
2Ease of operation
If pre-configured algorithms are used, then ease of operation is improved, but adaptability deteriorates for diverse image contents
Solution Approach 1:
The super-resolution system performs self-adjustment by automatically analyzing input image characteristics and configuring optimal processing parameters without user intervention. The image quality assessment module enables the system to serve itself by selecting appropriate processing strategies based on detected features, maintaining ease of operation while achieving high adaptability.
Solution Approach 2:
The system implements a feedback mechanism where the output of image quality assessment feeds into parameter adjustment, which then influences the super-resolution processing. This closed-loop feedback enables automatic adaptation to diverse image contents while keeping the user interface simple and unchanged.
3Use of energy by moving object
If computing power is limited in edge devices, then power consumption is reduced, but image enhancement quality deteriorates
Solution Approach 1:
The system applies partial processing by selectively applying different enhancement operations to different regions or frequency components of the image based on content analysis. Rather than applying full-strength processing uniformly, it applies targeted processing only where needed, reducing overall computational load while maintaining quality in critical areas.
Data Source
AI summary
An image processing circuit performs super-resolution (SR) operations. The image processing circuit includes memory to store multiple parameter sets of multiple artificial intelligence (AI) models. The image processing circuit further includes an image guidance module, a parameter decision module, and an SR engine. The image guidance module operates to detect a representative feature in an image sequence including a current frame and past frames within a time window. The parameter decision module operates to adjust parameters of one or more AI models based on a measurement of the representative feature. The SR engine operates to process the current frame using the one or more AI models with the adjusted parameters to thereby generate a high-resolution image for display.