Measuring and validating the quality, visual fidelity, and performance of synthetic image data is an advanced and evolving subject. This study will present an overview of various methods and approaches for measuring the visual quality and fidelity of synthetic image data. Cignal will present an overview of approaches, including established industry standards, such as ASTM E1695 and ANSI N42.45, statistical methods, and neural-network based approaches. Lastly, Cignal will discuss how these approaches may be integrated into a Continuous Integration/ Continuous Delivery (CI/CD) data generation pipeline to monitor and improve data quality and predicted performance for Artificial Intelligence/Machine Learning (AI/ML) use cases.
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