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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)

Paper ID
14184
Venue
VLDB
Year
2025
Pagerank
5.093636e-05
Overall Rank
10,944 | 24.92%
DOI
10.14778/3749646.3749650

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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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