Mr. Uwe Artmann Profile

Uwe Artmann

Technical at Image Engineering GmbH & Co KG

SPIE Involvement:

Author | Instructor

Proceedings Article | 27 February 2015 Paper

Image quality assessment using the dead leaves target: experience with the latest approach and further investigations

Uwe Artmann

Proceedings Volume 9404, 94040J (2015) https://doi.org/10.1117/12.2079609

KEYWORDS: Cameras, Spatial frequencies, Image quality, Image processing, Signal processing, Imaging systems, Denoising, Signal attenuation, Stars, Image resolution

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The so-called texture loss is a critical parameter in the objective image quality assessment of todays cameras. Especially cameras build in mobile phones show significant loss of low contrast, fine details which are hard to describe using standard resolution measurement procedures. The combination of very small form factor and high pixel count leads to a high demand of noise reduction in the signal-processing pipeline of these cameras. Different work groups within ISO and IEEE are investigating methods to describe the texture loss with an objective method. The so-called dead leaves pattern has been used for quite a while in this context. Image Engineering presented a new intrinsic approach at the Electronic Imaging Conference 2014, which promises to solve the open issue of the original approach, which could be influenced by noise and artifacts. In this paper, we present our experience with the new approach for a large set of different imaging devices. We show, that some sharpening algorithm found in todays cameras can significantly influence the Spatial Frequency Response based on the Dead Leaves structure (SFRDeadLeaves) results and therefore make an objective evaluation of the perceived image quality even harder. For an objective comparison of cameras, the resulting SFR needs to be reduced to a small set of numbers, ideally a single number. The observed sharpening algorithms lead to much better numerical results, while the image quality already degrades due to strong sharpening. So the measured, high SFR_DeadLeavesresult is not wrong, as it reflects the artificially enhanced SFR, but the numerical result cannot be used as the only number to describe the image quality. We propose to combine the SFRDeadLeaves measurement with other SFR measurement procedures as described in ISO12233:2014. Based on the three different SFR functions using the dead leaves pattern, sinusoidal Siemens Stars and slanted edges, it is possible to obtain a much better description if the perceived image quality. We propose a combination of SFR_DeadLeaves, SFREdge and SFR_Siemensmeasurements for an in-depth test of cameras and present our experience based on todays cameras.

Proceedings Article | 7 March 2014 Paper

Description of texture loss using the dead leaves target: current issues and a new intrinsic approach

Leonie Kirk, Philip Herzer, Uwe Artmann, Dietmar Kunz

Proceedings Volume 9023, 90230C (2014) https://doi.org/10.1117/12.2039689

KEYWORDS: Cameras, Image quality, Denoising, Imaging systems, Spatial frequencies, Galactic astronomy, Zoom lenses, Visualization, Imaging devices, Image enhancement

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Proceedings Article | 7 March 2013 Paper

Image quality evaluation using moving targets

Uwe Artmann

Proceedings Volume 8667, 86671D (2013) https://doi.org/10.1117/12.2003027

KEYWORDS: Image quality, Image processing, Cameras, Digital signal processing, Signal processing, Video, Spatial frequencies, Imaging devices, Digital image processing, Image enhancement

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The basic concept of testing a digital imaging device is to reproduce a known target and to analyze the resulting image. This semi-reference approach can be used for various different aspects of image quality. Each part of the imaging chain can have an influence on the results: lens, sensor, image processing and the target itself. The results are valid only for the complete system. If we want to test a single component, we have to make sure that we change only one and keep all others constant. When testing mobile imaging devices, we run into the problem that hardly anything can be manually controlled by the tester. Manual exposure control is not available for most devices, the focus cannot be influenced and hardly any settings for the image processing are available. Due to the limitations in the hardware, the image pipeline in the digital signal processor (DSP) of mobile imaging devices is a critical part of the image quality evaluation. The processing power of the DSPs allows sharpening, tonal correction and noise reduction to be non-linear and adaptive. This makes it very hard to describe the behavior for an objective image quality evaluation. The image quality is highly influenced by the signal processing for noise and resolution and the processing is the main reason for the loss of low contrast, _ne details, the so called texture blur. We present our experience to describe the image processing in more detail. All standardized test methods use a defined chart and require, that the chart and the camera are not moved in any way during test. In this paper, we present our results investigating the influence of chart movement during the test. Different structures, optimized for different aspects of image quality evaluation, are moved with a defined speed during the capturing process. The chart movement will change the input for the signal processing depending on the speed of the target during the test. The basic theoretical changes in the image will be the introduction of motion blur. With the known speed and the measured exposure time, we can calculate the theoretical motion blur. We compare the theoretical influence of the motion blur with the measured results. We use different methods to evaluate image quality parameter vs. motion speed of the chart. Slanted edges are used to obtain a SFR and to check for image sharpening. The aspect of texture blur is measured using dead leaves structures. The theoretical and measured results are plotted against the speed of the chart and allow an insight into the behavior of the DSP.

Proceedings Article | 25 January 2012 Paper

Improving texture loss measurement: spatial frequency response based on a colored target

Uwe Artmann, Dietmar Wueller

Proceedings Volume 8293, 829305 (2012) https://doi.org/10.1117/12.907303

KEYWORDS: Cameras, Image quality, Spatial frequencies, Denoising, Detection and tracking algorithms, Image analysis, Signal attenuation, Picosecond phenomena, Image resolution, Digital cameras

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Proceedings Article | 18 January 2010 Paper

Differences of digital camera resolution metrology to describe noise reduction artifacts

Uwe Artmann, Dietmar Wueller

Proceedings Volume 7529, 75290L (2010) https://doi.org/10.1117/12.838743

KEYWORDS: Denoising, Cameras, Modulation transfer functions, Spatial frequencies, Stars, Modulation, Image processing, Image quality, Signal processing, Digital cameras

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Proceedings Article | 19 January 2009 Paper

Interaction of image noise, spatial resolution, and low contrast fine detail preservation in digital image processing

Uwe Artmann, Dietmar Wueller

Proceedings Volume 7250, 72500I (2009) https://doi.org/10.1117/12.805927

KEYWORDS: Cameras, Signal to noise ratio, Image processing, Spatial resolution, Image resolution, Image analysis, Denoising, Image quality, Digital image processing, Digital cameras

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Proceedings Article | 3 March 2008 Paper

Noise reduction versus spatial resolution

Uwe Artmann, Dietmar Wueller

Proceedings Volume 6817, 68170A (2008) https://doi.org/10.1117/12.765887

KEYWORDS: Cameras, Modulation, Denoising, Stars, Spatial resolution, Spatial frequencies, Digital imaging, Image processing, Modulation transfer functions, Image analysis

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Showing 5 of 7 publications

Course Instructor

SC1058: Image Quality and Evaluation of Cameras In Mobile Devices

Digital and mobile imaging camera system performance is determined by a combination of sensor characteristics, lens characteristics, and image-processing algorithms. As pixel size decreases, sensitivity decreases and noise increases, requiring a more sophisticated noise-reduction algorithm to obtain good image quality. Furthermore, small pixels require high-resolution optics with low chromatic aberration and very small blur circles. Ultimately, there is a tradeoff between noise, resolution, sharpness, and the quality of an image. This short course provides an overview of "light in to byte out" issues associated with digital and mobile imaging cameras. The course covers, optics, sensors, image processing, and sources of noise in these cameras, algorithms to reduce it, and different methods of characterization. Although noise is typically measured as a standard deviation in a patch with uniform color, it does not always accurately represent human perception. Based on the "visual noise" algorithm described in ISO 15739, an improved approach for measuring noise as an image quality aspect will be demonstrated. The course shows a way to optimize image quality by balancing the tradeoff between noise and resolution. All methods discussed will use images as examples.

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