We report measurement methods and metrics for the evaluation of dynamic vision sensor (DVS) pixels. In particular, we developed automated test environments and test metrics which can quantify the sensitivity, latency and background noise of DVS pixels. For sensitivity measurements, response probabilities of pixels were analyzed at various conditions, such as base light intensity and region of interests of a sensor. Pixel latency was measured by varying the duty of light pulse, and noise level were also characterized at different light intensities. We expect the developed methods and metrics can help to clarify the performance of DVS pixels at the user point of view.
Recently, smartphones are equipped with high resolution mobile camera modules of 100 million pixels or more. After that, it is expected that much higher resolution mobile camera modules will be mounted. However, in order to mount more pixels in a limited space, the pixel size must be reduced. If 1.0 um pixel sensor was the mainstream in the past, now 0.64um pixel sensor has been developed now, and a sensor with smaller pixel will be developed in the future. However, there are technical limitations. In terms of image quality of sensor, if the size of pixel becomes smaller, the amount of light received decreases, and the image quality in terms of noise becomes poor. In order to solve this limitation, an attempt is made to develop a high-sensitivity sensor in various ways. One of them is the image sensor using CMY color filter technology. CMY color filter has higher sensitivity than RGB, so it is advantageous for developing high sensitivity sensors. In this paper, we introduce a method to Image quality evaluate the CMOS image sensor equipped with CMY color filter in mobile devices.
Under Display Camera(UDC) technology is being developed to eliminate camera holes and place cameras behind display panels according to full display trend in mobile phone. However, these camera systems cause attenuation and diffraction as light passes through the panel, which is inevitable to deteriorate the camera image. In particular, the deterioration of image quality due to diffraction and flares is serious, in this regard, this paper discusses techniques for restoring it. The diffraction compensation algorithm in this paper is aimed at real-time processing through HW implementation in the sensor for preview and video mode, and we've been able to use effective techniques to reduce computation by about 40 percent.
The state-of-the art smartphones have a motion correction function such as an electric image stabilizer and record the video without shaking. As the motion is corrected in various ways according to the set maker, there is a difference in performance and it is difficult to distinguish clearly its performance. This paper defines the Effective angle of View and Motion, for video motion correction performance evaluation. In the case of motion, we classified the motion volume, motion standard deviation, and motion frequency parameters. The performance of motion correction on the electronic device can be scored for each of parameters. In this way, the motion correction performance can be objectively modelled and evaluated.