Leveraging Compression to Construct Transferable Bitrate Ladders
This work addresses efficiency in video streaming for viewers by reducing encoding time, though it appears incremental as it builds on existing ML-based methods.
The paper tackles the computational overhead of constructing per-shot bitrate ladders for video encoding by proposing a new machine learning-based technique that predicts VMAF scores from compression analysis and perceptual measurements on source videos, achieving evaluation against prior methods on a large video corpus with metrics like Bjontegaard-delta.
Over the past few years, per-title and per-shot video encoding techniques have demonstrated significant gains as compared to conventional techniques such as constant CRF encoding and the fixed bitrate ladder. These techniques have demonstrated that constructing content-gnostic per-shot bitrate ladders can provide significant bitrate gains and improved Quality of Experience (QoE) for viewers under various network conditions. However, constructing a convex hull for every video incurs a significant computational overhead. Recently, machine learning-based bitrate ladder construction techniques have emerged as a substitute for convex hull construction. These methods operate by extracting features from source videos to train machine learning (ML) models to construct content-adaptive bitrate ladders. Here, we present a new ML-based bitrate ladder construction technique that accurately predicts the VMAF scores of compressed videos, by analyzing the compression procedure and by making perceptually relevant measurements on the source videos prior to compression. We evaluate the performance of our proposed framework against leading prior methods on a large corpus of videos. Since training ML models on every encoder setting is time-consuming, we also investigate how per-shot bitrate ladders perform under different encoding settings. We evaluate the performance of all models against the fixed bitrate ladder and the best possible convex hull constructed using exhaustive encoding with Bjontegaard-delta metrics.