AI Super Resolution Circuit Online Feature Adaptation
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Solution Overview
Problem
Conventional edge devices with limited computing power struggle to maintain high image quality during super-resolution operations due to inflexible pre-configured algorithms, which are inadequate for diverse input images and qualities.
Innovation Solution
A video processing circuit with an AI super-resolution circuit and online adaptation circuit that forms training pairs from low-resolution and high-resolution frames to update representative features, allowing for dynamic adjustment of AI models to enhance image quality in real-time.
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 adaptability to diverse input images deteriorates
Solution Approach 1:
The patent implements dynamic adaptation by allowing the super-resolution algorithm to adjust its parameters and features in real-time based on the input image characteristics. The system transitions from static pre-configured algorithms to dynamic adaptive processing, where the algorithm evolves during runtime to match the specific content being processed.
Solution Approach 2:
The system changes parameters by updating representative features that characterize the input low-resolution frames. These feature updates allow the algorithm to adapt its behavior based on the specific properties of each input image, such as content type, quality level, and degradation characteristics.
2Adaptability or versatility
If online adaptation is implemented to improve adaptability, then computational requirements increase, but edge device capabilities are limited
Solution Approach 1:
The system performs partial adaptation by selectively updating only the most critical representative features rather than retraining the entire model. This approach provides sufficient adaptability for diverse inputs while keeping computational requirements within the limits of edge devices.
Solution Approach 2:
The super-resolution circuit performs self-adaptation by automatically adjusting its own parameters and features based on the input data characteristics. This eliminates the need for external training or manual configuration, allowing the system to serve itself and reduce computational overhead.
3Productivity
If pre-configured parameters are used, then processing speed is maintained, but image quality deteriorates for diverse contents
Solution Approach 1:
The system performs preliminary analysis of the input low-resolution frame to extract representative features before applying the super-resolution transformation. This preliminary action allows the algorithm to prepare appropriate processing parameters in advance, ensuring both speed and quality optimization for each specific input.
Solution Approach 2:
The system uses feedback from the input image characteristics to dynamically adjust processing parameters. By analyzing the representative features of the input frame and using this information to guide the super-resolution process, the system achieves improved image quality while maintaining efficient processing through informed decision-making.
Data Source
AI summary
A video processing circuit includes an input buffer, an online adaptation circuit, and an artificial intelligence (AI) super-resolution (SR) circuit. The input buffer receives input low-resolution (LR) frames and high-resolution (HR) frames from a video source over a network. The online adaptation circuit forms training pairs, and calculates an update to representative features that characterize the input LR frames using the training pairs. Each training pair formed by one of the input LR frames and one of the HR frames. The AI SR circuit receives the input LR frames from the input buffer and the representative features from the online adaptation circuit. Concurrently with calculating the update to the representative features, the AI SR circuit generates SR frames for display from the input LR frames based on the representative features. Each SR frame has a higher resolution than a corresponding one of the input LR frames.