Statistical Binary Quantization

Overview

Statistical Binary Quantization (SBQ) is an advanced compression method developed by Timescale researchers that improves upon standard Binary Quantization for vector search applications.

Key Innovation

SBQ provides superior compression compared to traditional binary quantization while maintaining accuracy, particularly important for filtered vector search operations.

Compression Benefits

• 32x Compression: Reduces vector storage by factor of 32
• Maintained Accuracy: Preserves search quality despite aggressive compression
• Cost Efficiency: Dramatically reduces storage and memory costs

Technical Approach

Builds on Binary Quantization (BQ) which reduces each float32 value to a single bit, but adds statistical methods to improve accuracy. Works particularly well with DiskANN-based indexes for filtered searches.

Use Cases

• Large-scale vector databases with memory constraints
• Cost-sensitive applications
• Filtered vector search scenarios
• PostgreSQL-based vector applications

Integration

Available as part of the pgvectorscale PostgreSQL extension, working alongside StreamingDiskANN indexing.

Performance Characteristics

• Enables billion-scale vector search on commodity hardware
• Maintains high recall rates (>99%)
• Significantly faster than uncompressed approaches for filtered search

Comparison to Other Methods

• vs Standard BQ: Better accuracy through statistical optimization
• vs Product Quantization: Simpler implementation, good for binary scenarios
• vs Scalar Quantization: Higher compression ratio

Pricing

Free and open-source as part of pgvectorscale.