Research

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.