Samsung electronics co., ltd. (20240203113). INTERPOLATION MODEL LEARNING METHOD AND DEVICE FOR LEARNING INTERPOLATION FRAME GENERATING MODULE simplified abstract
Contents
- 1 INTERPOLATION MODEL LEARNING METHOD AND DEVICE FOR LEARNING INTERPOLATION FRAME GENERATING MODULE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 INTERPOLATION MODEL LEARNING METHOD AND DEVICE FOR LEARNING INTERPOLATION FRAME GENERATING MODULE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Interpolation Model Learning
- 1.13 Original Abstract Submitted
INTERPOLATION MODEL LEARNING METHOD AND DEVICE FOR LEARNING INTERPOLATION FRAME GENERATING MODULE
Organization Name
Inventor(s)
Junsik Jung of Daejeon-si (KR)
Sungeui Yoon of Daejeon-si (KR)
INTERPOLATION MODEL LEARNING METHOD AND DEVICE FOR LEARNING INTERPOLATION FRAME GENERATING MODULE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240203113 titled 'INTERPOLATION MODEL LEARNING METHOD AND DEVICE FOR LEARNING INTERPOLATION FRAME GENERATING MODULE
Simplified Explanation
The patent application describes a method for learning an interpolation model using a neural network, stored on a computer-readable storage medium, and a device for generating interpolation frames.
- The method involves extracting temporal-spatial features using a neural network model from frame groups containing interpolation frames and ground truth frames, adjusting the neural network to minimize differences in features.
Key Features and Innovation
- Learning method for interpolation model using neural network.
- Extraction of temporal-spatial features from frame groups.
- Adjustment of neural network to reduce differences in features.
Potential Applications
The technology can be used in video processing, image interpolation, and computer vision applications.
Problems Solved
The technology addresses the challenge of generating accurate interpolation frames in video processing.
Benefits
- Improved accuracy in generating interpolation frames.
- Enhanced performance in video processing tasks.
- Efficient utilization of neural networks for interpolation.
Commercial Applications
- Video editing software.
- Image processing applications.
- Surveillance systems for real-time video analysis.
Prior Art
Researchers can explore prior work on neural network-based interpolation models in the field of computer vision and video processing.
Frequently Updated Research
Stay updated on advancements in neural network models for video interpolation and temporal-spatial feature extraction.
Questions about Interpolation Model Learning
What are the potential real-world applications of this technology?
The technology can be applied in various fields such as video editing, image processing, and surveillance systems for enhanced performance.
How does the neural network model improve the accuracy of interpolation frames?
By extracting temporal-spatial features and adjusting the neural network parameters, the model aims to minimize differences between features for more accurate interpolation frames.
Original Abstract Submitted
an interpolation model learning method, a non-transitory computer-readable storage medium storing instructions allowing the method to be performed, and a device for learning an interpolation frame generating module are provided. the interpolation model learning method may include extracting, by using a neural network model, a temporal-spatial feature of each of a frame group including an interpolation frame generated by an interpolation model based on a neural network and a frame group including a ground truth (gt) frame corresponding to an interpolation frame and changing a weight and/or a bias of a neural network of an interpolation model to decrease a difference between temporal-spatial features.
