Product Quantization Compression

Compression technique that converts full-precision vectors to binary representations, achieving 32x storage reduction while maintaining 90-95% recall for efficient large-scale vector search.

Compression method developed by Timescale researchers that improves on standard Binary Quantization, reducing vector memory footprint by 32x while maintaining high accuracy for filtered searches.

Elasticsearch and Lucene's implementation of RaBitQ algorithm for 1-bit vector quantization, renamed as BBQ. Provides 32x compression with asymptotically optimal error bounds, enabling efficient vector search at massive scale with minimal accuracy loss.

Advanced quantization technique that applies per-vector normalization and scalar quantization, adapting the quantization bounds individually for each vector. Achieves four-fold reduction in vector size while maintaining search accuracy with 26-37% overall memory footprint reduction.

An advanced quantization technique introduced by Google's ScaNN that prioritizes preserving parallel components between vectors rather than minimizing overall distance. Optimized for Maximum Inner Product Search (MIPS) and significantly improves retrieval accuracy.

Extreme vector compression technique converting each dimension to a single bit (0 or 1), achieving 32x memory reduction and enabling ultra-fast Hamming distance calculations with acceptable accuracy trade-offs.