Not Small Enough? SegPQ: A Learned Approach to Compress Product Quantization Codebooks
Summary: SegPQ losslessly compresses PQ codebooks via learned ε-bounded piecewise-linear models plus low-bit residuals, with a provable near-optimal bit bound. SIMD-aware decompression cuts memory up to 4.7× for billion-scale ANN search at only 3.3% overhead. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Qiyu Liu (Southwest University)
- 2. Yanlin Qi (Harbin Engineering University)
- 3. Siyuan Han (Hong Kong University of Science and Technology)
- 4. Jingshu Peng (ByteDance)
- 5. Jin Li (Harvard University)
- 6. Lei Chen (Hong Kong University of Science and Technology)
BibTeX Citation
@article{liu_vldb25,
title = {{Not Small Enough? SegPQ: A Learned Approach to Compress Product Quantization Codebooks}},
author = {Liu, Qiyu and Qi, Yanlin and Han, Siyuan and Peng, Jingshu and Li, Jin and Chen, Lei},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {11},
pages = {3730--3743},
doi = {10.14778/3749646.3749650},
url = {https://doi.org/10.14778/3749646.3749650},
year = {2025}
}
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