Paper
8 February 2015 Mobile phone camera benchmarking in low light environment
Author Affiliations +
Proceedings Volume 9396, Image Quality and System Performance XII; 93960A (2015) https://doi.org/10.1117/12.2075630
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States
Abstract
High noise values and poor signal to noise ratio are traditionally associated to the low light imaging. Still, there are several other camera quality features which may suffer from low light environment. For example, what happens to the color accuracy and resolution or how the camera speed behaves in low light? Furthermore, how low light environments affect to the camera benchmarking and which metrics are the critical ones? The work contains standard based image quality measurements including noise, color, and resolution measurements in three different light environments: 1000, 100, and 30 lux. Moreover, camera speed measurements are done. Detailed measurement results of each quality and speed category are revealed and compared. Also a suitable benchmark algorithm is evaluated and corresponding score is calculated to find an appropriate metric which characterize the camera performance in different environments. The result of this work introduces detailed image quality and camera speed measurements of mobile phone camera systems in three different light environments. The paper concludes how different light environments influence to the metrics and which metrics should be measured in low light environment. Finally, a benchmarking score is calculated using measurement data of each environment and mobile phone cameras are compared correspondingly.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Veli-Tapani Peltoketo "Mobile phone camera benchmarking in low light environment", Proc. SPIE 9396, Image Quality and System Performance XII, 93960A (8 February 2015); https://doi.org/10.1117/12.2075630
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KEYWORDS
Cameras

Signal to noise ratio

Image quality

Environmental sensing

Cell phones

Image processing

Image resolution

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