A virtual reality camera is a complex entity which has more dedicated components as well as image quality layers and image processing algorithms than a normal still or video camera. Components like fish eye optics, multiple camera synchronization and stitching algorithms create new practical challenges for image quality measurements. <p> </p>The work gives an overview of those measurement challenges which are faced daily in an image quality laboratory when virtual reality cameras are being validated. Some of the measurement issues are very concrete like size of the test charts or how to measure uniformity of a camera of which the field of view is more than 180 degrees. On the other hand, new algorithms and a great number of individual imaging sensors in a single camera device require new measurement methods and a powerful test environment to handle the huge number of images generated. <p> </p>The paper concentrates on measurement practices for three main features of virtual reality cameras. Firstly, image quality measurement issues of fish eye cameras. Secondly, challenges and requirements of a multi camera system. And thirdly, challenges in measuring stitching algorithms together with 3D rendering. Each of these areas differs from traditional image quality measurements and require some special test charts, measurement processes, equipment, or test environments.
Proc. SPIE. 9896, Optics, Photonics and Digital Technologies for Imaging Applications IV
KEYWORDS: Digital signal processing, Visualization, Imaging systems, Cameras, Cameras, Sensors, Video, Image quality, Image quality, Image sensors, Digital imaging, 3D metrology, Spatial resolution, Head-mounted displays, Virtual reality, Prototyping, 3D image processing, New and emerging technologies
Commercial presence capture cameras are coming to the markets and a new era of visual entertainment starts to get its shape. Since the true presence capturing is still a very new technology, the real technical solutions are just passed a prototyping phase and they vary a lot. Presence capture cameras have still the same quality issues to tackle as previous phases of digital imaging but also numerous new ones. This work concentrates to the quality challenges of presence capture cameras. A camera system which can record 3D audio-visual reality as it is has to have several camera modules, several microphones and especially technology which can synchronize output of several sources to a seamless and smooth virtual reality experience. Several traditional quality features are still valid in presence capture cameras. Features like color fidelity, noise removal, resolution and dynamic range create the base of virtual reality stream quality. However, co-operation of several cameras brings a new dimension for these quality factors. Also new quality features can be validated. For example, how the camera streams should be stitched together with 3D experience without noticeable errors and how to validate the stitching? The work describes quality factors which are still valid in the presence capture cameras and defines the importance of those. Moreover, new challenges of presence capture cameras are investigated in image and video quality point of view. The work contains considerations how well current measurement methods can be used in presence capture cameras.
High noise values and poor signal to noise ratio are traditionally associated to the low light imaging. Still, there are
several other camera quality features which may suffer from low light environment. For example, what happens to the
color accuracy and resolution or how the camera speed behaves in low light? Furthermore, how low light environments
affect to the camera benchmarking and which metrics are the critical ones?
The work contains standard based image quality measurements including noise, color, and resolution measurements in
three different light environments: 1000, 100, and 30 lux. Moreover, camera speed measurements are done. Detailed
measurement results of each quality and speed category are revealed and compared. Also a suitable benchmark algorithm
is evaluated and corresponding score is calculated to find an appropriate metric which characterize the camera
performance in different environments.
The result of this work introduces detailed image quality and camera speed measurements of mobile phone camera
systems in three different light environments. The paper concludes how different light environments influence to the
metrics and which metrics should be measured in low light environment. Finally, a benchmarking score is calculated
using measurement data of each environment and mobile phone cameras are compared correspondingly.
When a mobile phone camera is tested and benchmarked, the significance of image quality metrics is widely acknowledged. There are also existing methods to evaluate the camera speed. However, the speed or rapidity metrics of the mobile phone’s camera system has not been used with the quality metrics even if the camera speed has become a more and more important camera performance feature. There are several tasks in this work. First, the most important image quality and speed-related metrics of a mobile phone’s camera system are collected from the standards and papers and, also, novel speed metrics are identified. Second, combinations of the quality and speed metrics are validated using mobile phones on the market. The measurements are done toward application programming interface of different operating systems. Finally, the results are evaluated and conclusions are made. The paper defines a solution to combine different image quality and speed metrics to a single benchmarking score. A proposal of the combined benchmarking metric is evaluated using measurements of 25 mobile phone cameras on the market. The paper is a continuation of a previous benchmarking work expanded with visual noise measurement and updates of the latest mobile phone versions.
When a mobile phone camera is tested and benchmarked, the significance of quality metrics is widely acknowledged. There are also existing methods to evaluate the camera speed. For example, ISO 15781 defines several measurements to evaluate various camera system delays. However, the speed or rapidity metrics of the mobile phone’s camera system have not been used with the quality metrics even if the camera speed has become more and more important camera performance feature. There are several tasks in this work. Firstly, the most important image quality metrics are collected from the standards and papers. Secondly, the speed related metrics of a mobile phone’s camera system are collected from the standards and papers and also novel speed metrics are identified. Thirdly, combinations of the quality and speed metrics are validated using mobile phones in the market. The measurements are done towards application programming interface of different operating system. Finally, the results are evaluated and conclusions are made. The result of this work gives detailed benchmarking results of mobile phone camera systems in the market. The paper defines also a proposal of combined benchmarking metrics, which includes both quality and speed parameters.