Optimizing Audio Compression Through Entropy-Controlled Dithering

TL;DR

This paper investigates entropy-controlled dithering techniques in audio compression, demonstrating improved perceptual quality with TPDF-based methods and introducing a practical plugin for enhanced audio processing.

Contribution

It introduces entropy-controlled dithering methods using TPDFs and noise shaping, providing a practical plugin implementation and insights into optimizing audio compression.

Findings

TPDF-based dithering outperforms RPDF in perceptual quality

Optimal alpha conditions enhance dithering performance

Situational choice of TPDF distributions affects results

Abstract

This paper explores entropy-controlled dithering techniques in audio compression, examining the application of standard and modified TPDFs, combined with noise shaping and entropy-controlled parameters, across various audio contexts, including pitch, loudness, rhythm, and instrumentation variations. Perceptual quality metrics such as VISQOL and STOI were used to evaluate performance. The results demonstrate that TPDF-based dithering consistently outperforms RPDF, particularly under optimal alpha conditions, while highlighting performance variability based on signal characteristics. These findings suggest the situational appropriateness of using various TPDF distributions. This work emphasizes the trade-off between entropy and perceptual fidelity, offering insights into the potential of entropy-controlled dithering as a foundation for enhanced audio compression algorithms. A practical implementation as a Digital Audio Workstation plugin introduces customizable dithering controls, laying the groundwork for future advancements in audio compression algorithms.