Recommended to watch in 720p:
Today, I've also stumbled upon a interesting paper entitled "Toward Evaluating Progressive Rendering Methods in Appearance Design Tasks" by Ou, Karlik, Krivanek and Pellacini. The authors tested 4 progressive rendering methods (random path tracing, quasirandom path tracing, progressive photon mapping and virtual point lights rendering) and investigated how people subjectively perceive and enjoy the progressive updating of the image.
Quoting the abstract (the full paper can be found here):
"Progressive rendering is becoming a popular alternative to precomputation approaches for appearance design tasks. Images created by different progressive algorithms exhibit various kinds of visual artifacts at the early stages of computation. We present a user study that investigates the effects of these artifacts on user performance in appearance design tasks. Speciﬁcally, we ask both novice and expert subjects to perform lighting and material editing tasks with the following algorithms: random path tracing, quasi-random path tracing, progressive photon mapping, and virtual point light (VPL) rendering. Data collected from the experiments suggest that path tracing is strongly preferred to progressive photon mapping and VPL rendering by both experts and novices. There is no indication that quasi-random path tracing is systematically preferred to random path tracing or vice-versa; the same holds between progressive photon mapping and VPL rendering. Interestingly, we did not observe any signiﬁcant difference in user workﬂow for the different algorithms. As can be expected, experts are faster and more accurate than novices, but surprisingly both groups have similar subjective preferences and workﬂow."
During the first frame updates, progressive photon mapping exhibits low frequency noise in the form of ugly splotches, while VPL rendering suffers from banding artefacts. The noise produced by path tracing on the other hand is much more easy on the eyes showing that the human visual system is more forgiving for Monte Carlo noise.