Smartphone cameras have progressed a lot during recent years and even caught up with entry-level DSLR cameras in many standard situations. One domain where the difference remained obvious was portrait photography. Now smartphone manufacturers equip their flagship models with special modes where they computationally simulate shallow depth of field. We propose a method to quantitatively evaluate the quality of such computational bokeh in a reproducible way, focusing on both the quality of the bokeh (depth of field, shape), as well as on artifacts brought by the challenge to accurately differentiate the face of a subject from the background, especially on complex transitions such as curly hairs. Depth of field simulation is a complex topic and standard metrics for out-of-focus blur do not currently exist. The proposed method is based on perceptual, systematic analysis of pictures shot in our lab. We show that the depth of field of the best mobile devices is as shallow as that of DSLRs, but also reveal processing artifacts that are inexistent on DSLRs. Our primary goal is to help customers comparing smartphone cameras among each other and to DSLRs. We also hope that our method will guide smartphone makers in their developments and will thereby contribute to advancing mobile portrait photography.
Wolf Hauser, Balthazar Neveu, Jean-Benoit Jourdain, Clément Viard, Frédéric Guichard, "Image quality benchmark of computational bokeh" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Quality and System Performance XV, 2018, pp 340-1 - 340-10, https://doi.org/10.2352/ISSN.2470-1173.2018.12.IQSP-340