Optimizing Audio Compression Through Entropy-Controlled Dithering

2025-01-04

AI Summary

To address the trade-off between perceptual audio quality and bitrate reduction in audio compression, this paper proposes an entropy-constrained dithering optimization method. Our approach designs a signal-adaptive dithering strategy based on the triangular probability density function (TPDF), integrating noise shaping with multidimensional audio feature modeling to establish an entropy–fidelity trade-off framework. We present the first systematic empirical validation of TPDF’s contextual superiority across diverse audio content and introduce the first digital audio workstation (DAW) plugin supporting customizable dither parameters. Experimental results under 16-bit quantization demonstrate that the optimal TPDF shape parameter α yields substantial improvements over uniform (RPDF) dithering: STOI ≥ 0.92, VISQOL ≥ 4.1, entropy reduction of 12–18%, and no perceptible auditory degradation—thereby enhancing both perceptual fidelity and compression efficiency.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Data Mining & Knowledge Management: Data Compression
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Responsible Web: Data and user privacy-enhancing technologies for the Web
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Audio Compression
• Perceptual Quality
• File Size Reduction

Innovation

• TPDF Dithering
• Music Compression Optimization
• Customizable Dithering Plugin