Image Signal Processor Parameter Tuning with Machine Learning
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Solution Overview
Problem
Existing methods for tuning image signal processors are subjective and lack objectivity, making it difficult to precisely improve image quality.
Innovation Solution
A machine learning model is trained to predict optimal parameter values for an image signal processor by capturing sample images, generating sample scores, and adjusting weights based on evaluation items, thereby objectively improving image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual tuning methods are used for image signal processor parameters, then the process is simple and easy to operate, but the subjectivity and lack of objectivity make it difficult to precisely improve image quality
Solution Approach 1:
The patent replaces manual mechanical tuning processes with an automated machine learning-based system. The ML model automatically predicts optimal parameter values based on training data, eliminating the need for manual adjustment and subjective evaluation, thereby achieving precise image quality optimization without manual intervention
Solution Approach 2:
The system enables self-service tuning by automatically generating training data through image capture and processing, then using this data to train the ML model. The model subsequently autonomously predicts optimal parameters without requiring expert knowledge or manual input, allowing the system to tune itself based on objective criteria
2Reliability
If multiple parameters are adjusted to improve image quality, then image processing performance improves, but the complexity of parameter optimization increases
Solution Approach 1:
The patent implements feedback mechanisms where the ML model continuously evaluates image quality based on predicted parameters and adjusts predictions accordingly. Training data is generated by capturing images, processing them with different parameter sets, evaluating quality metrics, and using this feedback to refine the model's predictions, creating a closed-loop optimization system
Solution Approach 2:
The system systematically varies multiple parameters across different training scenarios to understand their individual and interactive effects on image quality. The ML model learns optimal parameter combinations through this systematic parameter changes during training, enabling accurate predictions without manual optimization of each parameter
Data Source
AI summary
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.