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Rate distortion optimization over large scale video corpus with machine learning
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abstract
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.
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Cited by 1 Pith paper
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Rate-Distortion Optimization with Non-Reference Metrics for UGC Compression
Linearizing a no-reference quality metric around the uncompressed input yields a block-wise rate-distortion cost that reduces bitrate by more than 30% versus SSE-based RDO on the metric being optimized.
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