Hybrid Video Resolution Blending for Low-Bandwidth Super-Resolution
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Solution Overview
Problem
Converting video signals to match display size and resolution requirements consumes significant silicon area, power, and memory bandwidth, and existing methods face challenges in efficiently enhancing image resolution without causing artifacts.
Innovation Solution
A system utilizing machine learning and non-machine learning techniques to process video signals, separating invariant and non-invariant portions, and blending them based on probability, reducing the need for large silicon areas, power, and memory bandwidth, while improving image resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional video signal conversion methods are used to match display size and resolution, then the video signals can be converted to the required format, but large silicon area, high power consumption, and large memory bandwidth are required
Solution Approach 1:
The patent segments the video signal processing into two distinct paths: a machine learning path for complex resolution enhancement and a non-machine learning path for standard conversion tasks. This segmentation allows the system to apply computational resources selectively, reducing the overall silicon area required while maintaining conversion accuracy for signals that need it.
Solution Approach 2:
The system dynamically changes processing parameters by switching between machine learning and non-machine learning modes based on the specific video signal characteristics and display requirements. This parameter change enables efficient resource utilization, avoiding the need for always-on high-power conversion circuits.
2Manufacturing precision
If conventional video signal conversion methods are used to match display size and resolution, then the video signals can be converted to the required format, but high power consumption occurs
Solution Approach 1:
The patent segments the video signal processing into two distinct paths: a machine learning path for complex resolution enhancement and a non-machine learning path for standard conversion tasks. This segmentation allows the system to apply computational resources selectively, reducing the overall silicon area required while maintaining conversion accuracy for signals that need it.
Solution Approach 2:
The system dynamically changes processing parameters by switching between machine learning and non-machine learning modes based on the specific video signal characteristics and display requirements. This parameter change enables efficient resource utilization, avoiding the need for always-on high-power conversion circuits.
3Manufacturing precision
If conventional video signal conversion methods are used to match display size and resolution, then the video signals can be converted to the required format, but large memory bandwidth is required
Solution Approach 1:
The patent segments the video signal processing into two distinct paths: a machine learning path for complex resolution enhancement and a non-machine learning path for standard conversion tasks. This segmentation allows the system to apply computational resources selectively, reducing the overall silicon area required while maintaining conversion accuracy for signals that need it.
4Manufacturing precision
If machine learning techniques are used to enhance image resolution, then image quality and detail are improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the video signal processing into two distinct paths: a machine learning path for complex resolution enhancement and a non-machine learning path for standard conversion tasks. This segmentation allows the system to apply computational resources selectively, reducing the overall silicon area required while maintaining conversion accuracy for signals that need it.
Solution Approach 2:
The system applies machine learning processing locally only to specific portions of video signals that benefit from it, rather than processing entire frames uniformly. This local quality approach reduces computational complexity while maintaining high resolution quality where needed.
Data Source
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AI summary
In some respects, the disclosure is directed to methods and systems for processing signals. A device (200) can include a first circuit configured to process video information for a resolution or scaling operation and provide first data, a second circuit configured to process the video information for the resolution or scaling operation and provide second data, and a third circuit configured to receive the first data and the second data and provide third data according to the resolution or scaling operation. the first circuit comprising a first machine learning engine (402).