Electronic Imaging Conference 2013 - Uwe Artmann (Image Engineering)
Abstract
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 di erent 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, fine 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. Diff erent structures, optimized for di erent aspects of image quality evaluation, are moved with a defi ned 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 diff erent 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.