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tm - Technisches Messen

Plattform für Methoden, Systeme und Anwendungen der Messtechnik

[TM - Technical Measurement: A Platform for Methods, Systems, and Applications of Measurement Technology
]

Editor-in-Chief: Puente León, Fernando / Zagar, Bernhard

12 Issues per year


IMPACT FACTOR 2016: 0.348

CiteScore 2017: 0.46

SCImago Journal Rank (SJR) 2017: 0.239
Source Normalized Impact per Paper (SNIP) 2017: 0.566

Online
ISSN
2196-7113
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Volume 83, Issue 1

Issues

Noise equalisation and quasi loss-less image data compression – or how many bits needs an image sensor?

Rauschäquivalisierung und quasi verlustfreie Bilddatenkompression – oder wie viele Bit benötigt ein Bildsensor?

Bernd Jähne / Martin Schwarzbauer
Published Online: 2015-12-17 | DOI: https://doi.org/10.1515/teme-2015-0093

Abstract

Modern high-end image sensors require up to 16 bit quantisation. Uniform quantisation is, however, not well adapted to the signal, because the temporal noise strongly increases with the grey value. Here a non-linear transform h(g) is proposed, which yields an image sensor signal with an adjustable, grey-value independent temporal noise. The number of bits required to represent the noise-equalised signal is in good approximation equal to the maximum signal-to-noise ratio (SNRmax). Thus the noise-equalised signal of any imaging sensor with a full-well capacity of less than 216 can be quantised with 8 bit or less. The only disadvantage is an insignificant increase in the overall noise level caused by additional quantisation noise.

Zusammenfassung

Moderne Hochleistungsbildsensoren benötigen eine Quantisierung von bis zu 16 bit. Uniforme Quantisierung ist aber dem Bildsignal nicht gut angepasst, da das zeitliche Rauschen stark mit dem Grauwert ansteigt. In diesem Artikel wird eine nichtlineare Transformation h(g) vorgeschlagen, die zu einem Bildsignal mit einer einstellbaren und grauwertunabhängigen Standardabweichung des zeitlichen Rauschens führt. Die Anzahl der Bits in dem transformierten Signal ist in guter Näherung gleich dem maximalen Signal-Rausch-Verhältnis (SNRmax). Damit kann das rausch-equalisierte Signal jedes Bildsensors mit einer Sättigungskapazität von kleiner als 216 mit nur 8 oder weniger Bit repräsentiert werden. Der einzige Nachteil ist ein unwesentlicher Anstieg des zeitlichen Rauschens durch zusätzliches Quantisierungsrauschen.

Keywords: Image sensor; noise; quantisation; standard EMVA 1288

Schlagwörter: Bildsensor; Rauschen; Quantisierung; Standard EMVA 1288

PACS: 85.60.Gz

Acknowledgement

The authors gratefully acknowledge partial financial support for this research by the Heidelberg Collaboratory for Image Processing (HCI) within the Institutional Strategy ZUK49 “Heidelberg: Realizing the Potential of a Comprehensive University”, Measure 6.4 including matching funds from the industry partners of the HCI, especially PCO AG.

About the article

Bernd Jähne

Bernd Jähne received his Diploma, Doctoral degree and Habilitation degree in Physics from Heidelberg University in 1977, 1980, and 1985, respectively, and a Habilitation degree in Applied Computer Science from the University of Hamburg-Harburg in 1992. From 1988 to 2003 he hold a research professorship at the Scripps Institution of Oceanography, University of California in San Diego. Since 1994 he is professor at the Interdisciplinary Center for Scientific Computing (IWR) and Institute for Environmental Physics of Heidelberg University and since 2008 he also heads the Heidelberg Collaboratory for Image Processing (HCI). His research interests include small-scale air-sea interaction and image processing. He chairs the EMVA 1288 committee of the European Machine Vision Association for camera characterization.

Heidelberg University, Heidelberg Collaboratory for Image Processing (HCI), Speyerer Straße 6, 69115 Heidelberg, Germany

Martin Schwarzbauer

Martin Schwarzbauer received his Diploma degree in Electrical and Information Engineering and M.Eng. degree in Electrical and Microsystems Engineering from the University of Applied Sciences, Regensburg, Germany, in 2007 and 2010, respectively. He joined PCO AG, Kelheim, Germany, in 2007 as an R&D hardware engineer. His main focus at PCO started with data interfaces. Later he joined as a founding member the CameraLinkHS standardization committee hosted by the Automated Imaging Association (AIA, Ann Arbor, Michigan, USA) in 2010. Meanwhile he is developing complete camera designs for high-speed and scientific cameras. In 2014 he has started working towards the Ph.D. degree. His research interests are real-time image compression algorithm especially for very high dynamic image data and sensor RAW data.

PCO AG, Donaupark 11 93309 Kelheim, Germany


Accepted: 2015-10-30

Received: 2015-10-05

Published Online: 2015-12-17

Published in Print: 2016-01-28


Citation Information: tm - Technisches Messen, Volume 83, Issue 1, Pages 16–24, ISSN (Online) 2196-7113, ISSN (Print) 0171-8096, DOI: https://doi.org/10.1515/teme-2015-0093.

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©2015 B. Jähne and M. Schwarzbauer, published by De Gruyter.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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