Leveraging Compression to Construct Transferable Bitrate Ladders

TL;DR

This paper introduces a machine learning-based method for constructing content-adaptive bitrate ladders that accurately predict perceptual quality scores, aiming to improve video encoding efficiency and viewer experience.

Contribution

The paper presents a novel ML technique for predicting VMAF scores to build transferable bitrate ladders, reducing computational overhead compared to convex hull methods.

Findings

The proposed method achieves comparable quality predictions to existing approaches.

Per-shot bitrate ladders perform well across different encoding settings.

ML-based ladders outperform fixed bitrate ladders in quality and efficiency.

Abstract

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.