Rate distortion optimization over large scale video corpus with machine learning
This work addresses video compression efficiency for streaming or storage applications, offering a practical improvement but is incremental as it builds on existing rate-distortion optimization methods.
The paper tackles the problem of optimizing bitrate allocation across a large video corpus to minimize average bitrate while maintaining quality constraints, achieving a 22% reduction in average bitrate with the same quality using the AV1 encoder.
We present an efficient codec-agnostic method for bitrate allocation over a large scale video corpus with the goal of minimizing the average bitrate subject to constraints on average and minimum quality. Our method clusters the videos in the corpus such that videos within one cluster have similar rate-distortion (R-D) characteristics. We train a support vector machine classifier to predict the R-D cluster of a video using simple video complexity features that are computationally easy to obtain. The model allows us to classify a large sample of the corpus in order to estimate the distribution of the number of videos in each of the clusters. We use this distribution to find the optimal encoder operating point for each R-D cluster. Experiments with AV1 encoder show that our method can achieve the same average quality over the corpus with $22\%$ less average bitrate.