Vector Quantization

Vector Quantization (VQ) is a data compression technique that maps high-dimensional vectors to a finite set of representative vectors, called codebook vectors or centroids, to reduce data size while preserving essential information. It is widely used in signal processing, image compression, and machine learning for tasks like feature extraction and clustering. The process involves partitioning the vector space into regions (Voronoi cells) and representing each region by its centroid, enabling efficient storage and transmission.

Also known as: VQ, Vector Quantisation, Vector Quantization Algorithm, Codebook-Based Compression, Voronoi Quantization

Why learn Vector Quantization?

Developers should learn Vector Quantization when working on applications requiring data compression, such as audio/video encoding (e.g., in MPEG standards), image processing (e.g., in JPEG), or reducing dimensionality in machine learning models. It is particularly useful in scenarios with limited bandwidth or storage, as it allows for lossy compression with controlled quality trade-offs, and in clustering algorithms like k-means for pattern recognition and data analysis.

Learning Resources

• docs: Vector Quantization - Wikipedia
• tutorial: Vector Quantization Tutorial by Stanford
• course: Vector Quantization in Machine Learning - Coursera Course
• video: Image Compression Using Vector Quantization - YouTube Video
• book: Fundamentals of Vector Quantization - Book by Allen Gersho