View source for 18067298. PREDICTING OPTIMAL VALUES FOR PARAMETERS USED IN AN OPERATION OF AN IMAGE SIGNAL PROCESSOR USING MACHINE LEARNING simplified abstract (Samsung Electronics Co., Ltd.)

=PREDICTING OPTIMAL VALUES FOR PARAMETERS USED IN AN OPERATION OF AN IMAGE SIGNAL PROCESSOR USING MACHINE LEARNING=


==Organization Name==

[[:Category:Samsung Electronics Co., Ltd.|Samsung Electronics Co., Ltd.]]


[[Category:Samsung Electronics Co., Ltd.]]

==Inventor(s)==

[[:Category:YOUNGHOON Kim of Suwon-si (KR)|YOUNGHOON Kim of Suwon-si (KR)]][[Category:YOUNGHOON Kim of Suwon-si (KR)]]

[[:Category:SUNGSU Kim of Suwon-si (KR)|SUNGSU Kim of Suwon-si (KR)]][[Category:SUNGSU Kim of Suwon-si (KR)]]

[[:Category:JUNGMIN Lee of Suwon-si (KR)|JUNGMIN Lee of Suwon-si (KR)]][[Category:JUNGMIN Lee 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.



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