Popularity Prediction for Social Media over Arbitrary Time Horizons
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
SPIE Optics + Photonics
With the recent development of video codecs, compression efficiency is expected to improve by at least 30% over their predecessors. Such impressive improvements come with a significant, typically orders of magnitude, increase in computational complexity. At the same time, objective video quality metrics that correlate increasingly better with human perception, such as SSIM and VMAF, have been gaining popularity. In this work, we build on the per-shot encoding optimization framework that can produce the optimal encoding parameters for each shot in a video, albeit carrying itself another significant computational overhead. We demonstrate that, with this framework, a faster encoder can be used to predict encoding parameters that can be directly applied to a slower encoder. Experimental results show that we can approximate within 1% the optimal convex hull, with significant reduction in complexity. This can significantly reduce the energy spent on optimal video transcoding.
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
Liqi Yan, Qifan Wang, Yiming Cu, Fuli Feng, Xiaojun Quan, Xiangyu Zhang, Dongfang Liu
Patrick Lewis, Barlas Oğuz, Wenhan Xiong, Fabio Petroni, Wen-tau Yih, Sebastian Riedel