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We present an efficient codec-agnostic method for bitrate allocation over alarge scale video corpus with the goal of minimizing the average bitratesubject to constraints on average and minimum quality. Our method clusters thevideos in the corpus such that videos within one cluster have similarrate-distortion (R-D) characteristics. We train a support vector machineclassifier to predict the R-D cluster of a video using simple video complexityfeatures that are computationally easy to obtain. The model allows us toclassify a large sample of the corpus in order to estimate the distribution ofthe number of videos in each of the clusters. We use this distribution to findthe optimal encoder operating point for each R-D cluster. Experiments with AV1encoder show that our method can achieve the same average quality over thecorpus with $22\%$ less average bitrate.

Rate distortion optimization over large scale video corpus with machine learning