Image Sensor Quality Tuning With Machine Learning ISP Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing systems rely heavily on human evaluation for optimizing image sensor performance, leading to subjective and inaccurate tuning of image signal processors (ISPs), which can result in suboptimal image quality.
Innovation Solution
A system and platform that utilize machine learning models, specifically artificial neural networks, to automatically optimize image quality based on user preferences by generating an image tuning knowledge database and controlling ISP parameters, incorporating a distributed camera simulation system and image learning data generation units to enhance the accuracy and objectivity of image tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human evaluation is used for optimizing image sensor performance, then subjective tuning can be performed, but the accuracy and objectivity of image quality assessment deteriorates
Solution Approach 1:
The patent replaces the mechanical human evaluation system with an automated machine learning-based image signal processor modeling system. The system uses trained models to objectively assess image quality and automatically tune ISP parameters, substituting human subjective judgment with algorithmic objective measurement while maintaining ease of operation through automated workflows.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a bridge between raw image data and quality assessment. This intermediary model processes images and provides objective quality scores that mediate between the image sensor output and the optimization process, enabling accurate and automated image quality evaluation without direct human intervention.
2Measurement precision
If automated machine learning models are used for image quality optimization, then objectivity and precision improve, but system complexity increases
Solution Approach 1:
The patent segments the complex image quality optimization system into distinct functional modules: an image learning data generation unit that creates training datasets, an image signal processor modeling unit that trains machine learning models, and an optimization unit that applies the models. This segmentation manages complexity by dividing the system into specialized, independently developable components with clear interfaces.
Solution Approach 2:
The patent performs preliminary actions by pre-training machine learning models using extensively curated image learning databases before deployment. The complex work of model training and parameter optimization is done in advance during system setup, allowing the deployed system to operate with simpler real-time inference operations, thus managing operational complexity.
3Manufacturing precision
If extensive image learning data is generated through distributed camera simulation, then model training accuracy improves, but computational resources and time consumption increase
Solution Approach 1:
The patent employs periodic action by using a distributed camera simulation system that generates image learning data in structured batches or cycles. The simulation systematically varies parameters and captures images periodically across multiple camera models and scenarios, enabling comprehensive data collection while organizing the computational workload into manageable periodic tasks rather than continuous processing.
Data Source
AI summary
A system of automatic optimization of image quality of an image sensor includes an image learning data generation unit generating an image tuning knowledge database, which includes pairs of a plurality of sets of values of a plurality of parameters and a plurality of sets of image quality evaluation scores for a plurality of image quality evaluation items for evaluating a quality of each of a plurality of images generated by the image sensor, using an image tuning database sampling module, an image signal processor modeling unit generating a machine learning model, for each image, for automatically optimizing the quality of each image, and an image sensor image quality optimization unit automatically controlling values of some of the plurality of parameters based on a user's image quality selection and the machine learning model. The image quality evaluation scores are produced by a distributed camera simulation system including servers.