Product Quantization Compression

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.

Vector compression technique that splits high-dimensional vectors into subvectors and quantizes each independently, achieving significant memory reduction while enabling approximate similarity search.

Vector compression technique reducing precision of each vector component from 32-bit floats to 8-bit integers, achieving 4x memory reduction with minimal accuracy loss for vector search.