# Uniform Convergence Bounds for Codec Selection

## TL;DR

This paper introduces a statistical framework using uniform convergence theory to optimally select audio codecs tailored to specific data distributions, balancing quality and compression effectively.

## Contribution

It provides rigorous, distribution-agnostic guarantees for codec selection, enabling adaptive optimization for audio quality and bandwidth based on data characteristics.

## Key findings

- Guarantees near-optimal codec selection with statistical bounds
- Balances quality and compression ratio effectively
- Outperforms fixed codec approaches in diverse scenarios

## Abstract

We frame the problem of selecting an optimal audio encoding scheme as a supervised learning task. Through uniform convergence theory, we guarantee approximately optimal codec selection while controlling for selection bias. We present rigorous statistical guarantees for the codec selection problem that hold for arbitrary distributions over audio sequences and for arbitrary quality metrics. Our techniques can thus balance sound quality and compression ratio, and use audio samples from the distribution to select a codec that performs well on that particular type of data. The applications of our technique are immense, as it can be used to optimize for quality and bandwidth usage of streaming and other digital media, while significantly outperforming approaches that apply a fixed codec to all data sources.