CodeContent

The latest in-depth technical AI news & concepts

Follow publication

Member-only story

Efficient Multimedia Retrieval: Exploring Binary and Product Quantization

Mostafa Ibrahim
CodeContent
Published in
7 min readJul 7, 2024

Introduction

In today’s digital landscape, the volume of multimedia content is skyrocketing, posing significant challenges for efficient data management and retrieval. From social media platforms to e-commerce websites, businesses rely on multimedia databases to store and serve images, videos, and other rich media. However, managing these massive data sets can be costly and time-consuming, particularly when it comes to storage requirements and retrieval speed.

Enter vector databases, a powerful solution for organizing and querying high-dimensional data, such as multimedia embeddings. While vector databases offer a robust framework, companies still grapple with the issue of maintaining these databases efficiently, both in terms of cost and retrieval speed.

In this blog post, we’ll explore the fascinating world of quantization, a technique that promises to revolutionize the way we handle multimedia databases. We’ll dive into the nitty-gritty of quantization, unveiling its magic and the potential it holds for storage reduction and speed enhancement. Additionally, we’ll compare two popular quantization methods, Binary Quantization, and Product Quantization, to help you choose the right approach for your specific needs.

What is Quantization, and Why Does it Matter?

Quantization is a process that converts high-dimensional, floating-point data (such as embeddings) into compact, discrete representations. By mapping these embeddings to lower-dimensional subspaces and encoding them using fewer bits, quantization can dramatically reduce the memory and storage requirements for multimedia data, such as images and videos.

Imagine compressing a 32-bit floating-point value into a mere 1-bit representation. This seemingly impossible feat is achievable through quantization, resulting in a whopping 32x reduction in storage requirements. In the world of multimedia databases, where petabytes of data are the norm, the potential…

Create an account to read the full story.

The author made this story available to Medium members only.
If you’re new to Medium, create a new account to read this story on us.

Or, continue in mobile web

Already have an account? Sign in

CodeContent
CodeContent

Published in CodeContent

The latest in-depth technical AI news & concepts

Mostafa Ibrahim
Mostafa Ibrahim

Written by Mostafa Ibrahim

Software Eng. University College London Computer Science Graduate. Passionate about Machine Learning in Healthcare. Top writer in AI

No responses yet

Write a response