Relative Impact of Key Rendering Parameters on Perceived Quality of VR Imagery Captured by the Facebook Surround 360 Camera

High quality, 360 capture for Cinematic VR is a relatively new and rapidly evolving technology. The field demands very high quality, distortion- free 360 capture which is not possible with cameras that depend on fish- eye lenses for capturing a 360 field of view. The Facebook Surround 360 Camera, one of the few “players” in this space, is an open-source license design that Facebook has released for anyone that chooses to build it from off-the-shelf components and generate 8K stereo output using open-source licensed rendering software. However, the components are expensive and the system itself is extremely demanding in terms of computer hardware and software. Because of this, there have been very few implementations of this design and virtually no real deployment in the field. We have implemented the system, based on Facebook’s design, and have been testing and deploying it in various situations; even generating short video clips. We have discovered in our recent experience that high quality, 360 capture comes with its own set of new challenges. As an example, even the most fundamental tools of photography like “exposure” become difficult because one is always faced with ultra-high dynamic range scenes (one camera is pointing directly at the sun and the others may be pointing to a dark shadow). The conventional imaging pipeline is further complicated by the fact that the stitching software has different effects on various as- pects of the calibration or pipeline optimization. Most of our focus to date has been on optimizing the imaging pipeline and improving the qual- ity of the output for viewing in an Oculus Rift headset. We designed a controlled experiment to study 5 key parameters in the rendering pipeline– black level, neutral balance, color correction matrix (CCM), geometric calibration and vignetting. By varying all of these parameters in a combinatorial manner, we were able to assess the relative impact of these parameters on the perceived image quality of the output. Our results thus far indicate that the output image quality is greatly influenced by the black level of the individual cameras (the Facebook cam- era comprised of 17 cameras whose output need to be stitched to obtain a 360 view). Neutral balance is least sensitive. We are most confused about the results we obtain from accurately calculating and applying the CCM for each individual camera. We obtained improved results by using the average of the matrices for all cameras. Future work includes evaluating the effects of geometric calibration and vignetting on quality.