DeltaPQ: Lossless Product Quantization Code Compression for High Dimensional Similarity Search
Summary: Compresses high-dimensional vectors by first quantizing into codes and then applying a tree-based delta encoding (DeltaPQ) to those codes. A linear-time algorithm selects the optimal tree, yielding up to 5× compression and enabling approximate nearest neighbor search directly on the compressed data across inner product, cosine, Euclidean, and Lp norms. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Runhui Wang (Rutgers University)
- 2. Dong Deng (Rutgers University)
BibTeX Citation
@article{wang_vldb20,
title = {{DeltaPQ: Lossless Product Quantization Code Compression for High Dimensional Similarity Search}},
author = {Wang, Runhui and Deng, Dong},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {13},
pages = {3603--3616},
doi = {10.14778/3424573.3424580},
url = {https://doi.org/10.14778/3424573.3424580},
year = {2020}
}
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