UPDATED
Color and Calibration in Compact Camera Modules for AR, Machine Vision, Automotive, and Mobile Applications
Sunday 26 January • 15:45 – 17:45
Course Length: 2 hours
Course Level: Introductory
Instructors: Uwe Artmann, Image Engineering GmbH & Co. KG, and Kevin J. Matherson, Microsoft Corporation

Learning Outcomes
This course enables the attendee to:

• Understand how hardware choices in compact cameras impact calibrations and the type of calibrations performed and how such choices can impact overall image quality.
• Describe basic image processing steps for color cameras based on application.
• Understand calibration methods used for camera modules.
• Describe the differences between class calibration and individual module calibration.
• Understand how spectral sensitivities and color matrices are calculated.
• Understand how the calibration light source impacts calibration.
• Describe required calibration methods based on the hardware chosen and the image processing used.
• Understand artifacts associated with color shading and incorrect calibrations.
• Understand how chromatic aberrations impact color and how to remove its unwanted effects.
• Learn about the impacts of pixel saturation and the importance of controlling it for color.
• Learn about the impact of tone reproduction on perceived color (skin tone, memory colors, etc.).
• Learn how flare compensation is done in electronic cameras.

Color cameras produce several different images in a single acquisition, often one red, one green, and one blue. The most common configuration is the Bayer filter, a designation to the arrangement of the color filters. Following capture, the image needs to be rendered. For most AR/VR, consumer, mobile, and automotive applications, image processing is done within the camera and covers various steps like dark current subtraction, flare compensation, shading, color compensation, demosaicing, white balancing, tonal and color correction, sharpening, and compression. Each of these steps has a significant influence on the color and overall image quality. There are many implementation challenges, the largest being part-to-part variation. In order to design and tune cameras, it is important to understand how color camera hardware varies as well as the methods that can be used to calibrate such variations. This course provides the basic methods describing capture, calibration, and processing of a color camera image for applications in AR/VR, machine vision, automotive, and consumer cameras. Participants get to examine the basic color image capture and how calibration can improve images using a typical color imaging pipeline. In the course, participants are shown how raw image data influences color transforms and white balance. The knowledge acquired in understanding the image capture and calibration process can be used to understand tradeoffs in improving overall image quality for a particular application.

Intended Audience
People involved in the design and image quality of electronic cameras (regardless of application) and scanners. Technical staff of manufacturers, managers of digital imaging projects, as well as journalists, and students studying image technology.

Uwe Artmann studied photo technology at the University of Applied Sciences in Cologne following an apprenticeship as a photographer and finished with the German 'Diploma Engineer'. He is now the CTO at Image Engineering, an independent test lab for imaging devices and manufacturer of all kinds of test equipment for these devices. His special interest is the influence of noise reduction on image quality and MTF measurement in general.

Kevin Matherson is a director of optical engineering at Microsoft Corporation working on advanced optical technologies for AR/VR, machine vision, and consumer products. Prior to Microsoft, he participated in the design and development of compact cameras at HP and has more than 15 years of experience developing miniature cameras for consumer products. His primary research interests focus on sensor characterization, optical system design and analysis, and the optimization of camera image quality. Matherson holds a Masters and PhD in optical sciences from the University of Arizona.