PrismQuant: Rate-Distortion-Optimal Vector Quantization for Gaussian-Mixture Sources
The paper presents PrismQuant, a method for rate-distortion-optimal vector quantization tailored for Gaussian-mixture sources. It addresses the challenges posed by multimodal sources and introduces a new approach to bit allocation across heterogeneous branches. Experimental results indicate that PrismQuant performs competitively against existing methods while maintaining a smaller model size.
- ▪PrismQuant is designed for Gaussian-mixture sources and aims to optimize rate-distortion performance.
- ▪The method utilizes a global reverse-waterfilling level for efficient bit allocation across different components.
- ▪Experiments show that PrismQuant closely approaches theoretical rate-distortion bounds and outperforms transformer-based codecs.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15507 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
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Computer Science > Information Theory arXiv:2605.15507 (cs) [Submitted on 15 May 2026] Title:PrismQuant: Rate-Distortion-Optimal Vector Quantization for Gaussian-Mixture Sources Authors:Bumsu Park, Chanho Park, Youngmok Park, Namyoon Lee View a PDF of the paper titled PrismQuant: Rate-Distortion-Optimal Vector Quantization for Gaussian-Mixture Sources, by Bumsu Park and 3 other authors View PDF HTML (experimental) Abstract:For a Gaussian source under mean-squared error (MSE), classical transform coding is rate--distortion (RD) optimal: the Karhunen--Loeve transform (KLT) diagonalizes the covariance, reverse waterfilling allocates the bits, and scalar quantization closes the loop.
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