It is possible to achieve improved color accuracy with a color camera by placing a color filter in front of the camera. Unfortunately, the color filter will block some of the light entering the camera, which will result in additional noise in the recorded data. This paper provides an initial investigation into finding an optimal solution to the filter design, in the presence of noise.
When evaluating camera systems for their noise performance, uniform patches in the object space are used. This is required as the measurement is based on the assumption that any variation of the digital values can be considered as noise. In presence of adaptive noise removal, this method can lead to misleading results as it is relatively easy for algorithms to smooth uniform areas of an image. In this paper, we evaluate the possibilities to measure noise on the so called dead leaves pattern, a random pattern of circles with varying diameter and color. As we measure the noise on a non-uniform pattern, we have a better description of the true noise performance and a potentially better correlation to the user experience.
Right now there are at least three publicly known ranking systems for cell phones (CPIQ [IEEE P1858, in preparation, DxOmark, VCX) that try to tell us which camera phone provides the best image quality. Now that IEEE is about to publish the P1858 standard with currently only 6 Image quality parameters the question arises how many parameters are needed to characterize a camera in a current cell phone and how important is each factor for the perceived quality. For testing the importance of a factor the IEEE cellphone image quality group (CPIQ) has created psychophysical studies for all 6 image quality factors that are described in the first version of IEEE P1858. That way a connection between the physical measurement of the image quality aspect and the perceived quality can be made.