Automated ISP Optimization Using Simulated Sensor Data
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Solution Overview
Problem
Existing image signal processors (ISPs) are manually optimized using ground truth images, which is time-consuming, subjective, and limited to specific sensors, making it difficult to achieve consistent image processing across different sensors, affecting computer vision (CV) algorithm accuracy.
Innovation Solution
A method that uses a simulator to generate simulated sensor image data, which is processed by an ISP to produce output image data, with quality metric data comparison to optimize ISP settings automatically, allowing for optimization across various sensors and improving CV algorithm accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual optimization using ground truth images is used, then ISP settings can be optimized for a specific sensor, but the process is time-consuming, subjective, and limited to one sensor
Solution Approach 1:
The patent creates a simulator that copies the essential characteristics of a specific sensor to generate synthetic training data. This allows the ISP to be trained on the simulated sensor data instead of requiring manual optimization with ground truth images from the actual sensor, thereby reducing time consumption while maintaining optimization precision.
Solution Approach 2:
The simulator pre-generates synthetic sensor image data and corresponding ground truth images in advance, creating a training dataset before the actual ISP optimization is needed. This preliminary action eliminates the need for time-consuming manual optimization during deployment, as the ISP can be directly trained on the pre-prepared synthetic data.
2Reliability
If manual optimization is used, then expert knowledge can guide the process, but the process remains subjective and sensor-specific
Solution Approach 1:
The simulator is designed to model multiple different sensor types and characteristics, allowing a single ISP to be trained on diverse synthetic data from various sensor configurations. This universal approach enables the ISP to adapt to multiple sensors without requiring separate manual optimization for each, thereby improving both reliability and sensor compatibility.
Solution Approach 2:
The system uses automated algorithms to train the ISP on synthetic data generated by the simulator, eliminating the need for subjective expert judgment. The optimization process becomes objective and repeatable, with the ISP automatically learning from the simulated training data, improving reliability while enabling broader sensor adaptability.
3Measurement precision
If ground truth images are used for optimization, then accurate ISP settings can be achieved, but ground truth data is difficult to obtain and sensor-specific
Solution Approach 1:
Instead of acquiring difficult-to-obtain ground truth images from real sensors, the patent uses a simulator to copy and generate synthetic ground truth images that match the expected output quality. This synthetic ground truth data is easily generated and can be paired with corresponding synthetic sensor images, making the optimization process much easier while maintaining measurement precision.
Solution Approach 2:
The simulator pre-generates pairs of synthetic sensor images and corresponding ground truth images before optimization begins. This preliminary preparation of training data eliminates the need for difficult ground truth acquisition during the optimization process, making the system easier to implement while maintaining accurate quality measurement capabilities.
Data Source
AI summary
A method of optimising an image signal processor (ISP), which is to be used to process sensor image data generating output image data. The method may include obtaining sensor image data; processing the sensor image data according to one or more ISP settings to produce output image data; producing quality metric data associated with the output image data and optimising the one or more ISP settings based on the quality metric data.