Boosting Supervised Learning in Small Data Regimes With Conditional Gan Augmentation

Video transcoding is an increasingly important application in the streaming media industry. It has become important to investigate the optimisation of transcoder parameters for a single clip simply because of the immense number of playbacks for popular clips. in this paper, we explore the use of a canned optimiser to estimate the optimal RD tradeoff achievable for a particular clip. We show that by adjusting the Lagrange multiplier in RD optimisation on keyframes alone we can achieve more than 10x the previous BD-Rate gains possible without affecting quality for any operating point.