18067298. PREDICTING OPTIMAL VALUES FOR PARAMETERS USED IN AN OPERATION OF AN IMAGE SIGNAL PROCESSOR USING MACHINE LEARNING simplified abstract (Samsung Electronics Co., Ltd.)
Contents
- 1 PREDICTING OPTIMAL VALUES FOR PARAMETERS USED IN AN OPERATION OF AN IMAGE SIGNAL PROCESSOR USING MACHINE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 PREDICTING OPTIMAL VALUES FOR PARAMETERS USED IN AN OPERATION OF AN IMAGE SIGNAL PROCESSOR USING MACHINE LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Original Abstract Submitted
PREDICTING OPTIMAL VALUES FOR PARAMETERS USED IN AN OPERATION OF AN IMAGE SIGNAL PROCESSOR USING MACHINE LEARNING
Organization Name
Inventor(s)
YOUNGHOON Kim of Suwon-si (KR)
PREDICTING OPTIMAL VALUES FOR PARAMETERS USED IN AN OPERATION OF AN IMAGE SIGNAL PROCESSOR USING MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18067298 titled 'PREDICTING OPTIMAL VALUES FOR PARAMETERS USED IN AN OPERATION OF AN IMAGE SIGNAL PROCESSOR USING MACHINE LEARNING
Simplified Explanation
The abstract describes a method for predicting optimal values for multiple parameters used in an image signal processor (ISP) operation. Here are the key points:
- The method involves inputting initial values for the parameters into a machine learning model.
- The machine learning model has an input layer representing the parameters and an output layer representing evaluation items extracted from a result image generated by the ISP.
- Evaluation scores for the evaluation items are obtained using the output of the machine learning model.
- The weights applied to the parameters are adjusted based on the evaluation scores.
- The optimal values for the parameters are determined using the adjusted weights.
Potential Applications
This technology has potential applications in various fields where image signal processing is involved, such as:
- Photography: Optimizing parameters in image processing algorithms to enhance image quality.
- Video processing: Fine-tuning parameters for video enhancement and noise reduction.
- Medical imaging: Improving image quality and accuracy in diagnostic imaging.
- Surveillance systems: Enhancing image clarity and reducing noise for better object recognition.
Problems Solved
This technology addresses the following problems:
- Manual parameter tuning: Automating the process of finding optimal parameter values, reducing the need for manual intervention and trial-and-error.
- Time-consuming optimization: Speeding up the optimization process by leveraging machine learning techniques to predict optimal values.
- Subjective evaluation: Providing objective evaluation scores for different evaluation items, reducing reliance on subjective human judgment.
Benefits
The use of this technology offers several benefits:
- Improved image quality: By predicting optimal parameter values, the image signal processor can produce higher-quality images.
- Time and cost savings: Automating the parameter optimization process saves time and reduces the need for extensive testing and experimentation.
- Objective evaluation: The use of evaluation scores based on machine learning models provides a more objective and consistent assessment of image quality.
- Adaptability: The method can adapt to different image processing scenarios and optimize parameters accordingly, leading to better results in various applications.
Original Abstract Submitted
A method of predicting optimal values for a plurality of parameters used in an operation of an image signal processor includes: inputting initial values for the plurality of parameters to a machine learning model having an input layer, corresponding to the plurality of parameters, and an output layer corresponding to a plurality of evaluation items extracted from a result image generated by the image signal processor; obtaining evaluation scores for the plurality of evaluation items using an output of the machine learning model; adjusting weights, applied to the plurality of parameters, based on the evaluation scores; and determining the optimal values using the adjusted weights.
