# Leveraging Compression to Construct Transferable Bitrate Ladders

arXiv: https://arxiv.org/abs/2512.12952 · HTML: https://mlsanity.com/paper/2512.12952/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 53 references that are in the map.
- First posted on arXiv 15 Dec 2025.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [BVI-DVC: A Training Database for Deep Video Compression](https://mlsanity.com/paper/2003.13552/) (2020)
- [Towards Perceptually Optimized End-to-end Adaptive Video Streaming](https://mlsanity.com/paper/1808.03898/) (2018)
- [Bjontegaard Delta (BD): A Tutorial Overview of the Metric, Evolution, Challenges, and Recommendations](https://mlsanity.com/paper/2401.04039/) (2024)
- [VMAF-based Bitrate Ladder Estimation for Adaptive Streaming](https://mlsanity.com/paper/2103.07564/) (2021)
- [Convex Hull Prediction for Adaptive Video Streaming by Recurrent Learning](https://mlsanity.com/paper/2206.04877/) (2022)
- [Ensemble Learning for Efficient VVC Bitrate Ladder Prediction](https://mlsanity.com/paper/2207.10317/) (2022)
- [Study of Compression Statistics and Prediction of Rate-Distortion Curves for Video Texture](https://mlsanity.com/paper/2102.04167/) (2021)
- [Bitrate Ladder Construction using Visual Information Fidelity](https://mlsanity.com/paper/2312.07780/) (2023)
- [Just Noticeable Difference-aware Per-Scene Bitrate-laddering for Adaptive Video Streaming](https://mlsanity.com/paper/2305.00225/) (2023)
- [Constructing Per-Shot Bitrate Ladders using Visual Information Fidelity](https://mlsanity.com/paper/2408.01932/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Self-Supervised Learning Framework for Video Encoding Complexity Clustering](https://mlsanity.com/paper/2606.29166/) (2026)

## Abstract (arXiv)

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.