Samsung electronics co., ltd. (20240320793). METHOD AND APPARATUS WITH SUPER-SAMPLING simplified abstract
Contents
METHOD AND APPARATUS WITH SUPER-SAMPLING
Organization Name
Inventor(s)
Hyeonseung Yu of Suwon-si (KR)
Jaeyoung Moon of Suwon-si (KR)
METHOD AND APPARATUS WITH SUPER-SAMPLING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240320793 titled 'METHOD AND APPARATUS WITH SUPER-SAMPLING
Simplified Explanation: The patent application describes a method for merging super-sampled image frames generated at different time points and increasing the bit precision of the result using a neural network model.
- **Super-sampling Image Frames:** Combining high-resolution image frames to improve overall image quality.
- **Neural Network Model:** Utilizing artificial intelligence to enhance image processing.
- **Bit Precision Increase:** Enhancing the quality of the final image by increasing the level of detail.
- **Time Point:** Refers to specific instances when the image frames are generated and processed.
- **Processor Implementation:** The method is executed by a computer processor for efficient image manipulation.
Potential Applications: 1. Image processing in photography and videography. 2. Enhancing graphics in gaming and virtual reality applications. 3. Improving medical imaging for diagnostic purposes. 4. Enhancing satellite imagery for remote sensing applications.
Problems Solved: 1. Enhancing image quality by merging super-sampled frames. 2. Increasing the level of detail in images through bit precision adjustment. 3. Streamlining the super-sampling process for efficient image processing.
Benefits: 1. Improved image quality and resolution. 2. Enhanced visual experience in various applications. 3. Efficient processing of high-resolution images. 4. Enhanced accuracy in image analysis and interpretation.
Commercial Applications: The technology can be applied in industries such as entertainment, healthcare, and remote sensing for improved image quality and analysis. It can also be utilized in consumer electronics for enhancing visual content.
Questions about Super-sampled Image Processing: 1. How does super-sampling improve image quality compared to traditional methods? 2. What are the potential limitations of using neural network models for image processing?
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Original Abstract Submitted
a processor-implemented method including merging a first super-sampled image frame, having been generated at a first time point, with a second input image frame corresponding to a super-sampling target for a second time point to generate a merged image and generating a second super-sampled image frame by performing a super-sampling operation at the second time point that includes increasing a bit-precision of a result of an executing, by the processor, of a super-sampling neural network model provided a decreased bit precision of the merged image.
