IEEE

Adaptive Latitude-Aware and Importance-Activated Transform Coding for Learned Omnidirectional Image Compression

Abstract: Based on the measured latitude and longitude, users can freely view different perspectives of the omnidirectional image. Typically, omnidirectional images are represented in the ...
IEEE

Design of an Embedded Image Compression System Using Python and Machine Learning

Abstract: The exponential growth of digital imagery necessitates advanced compression techniques that balance storage efficiency, transmission speed, and image quality. This paper presents an embedded ...
GitHub

Near-optimal vector quantization for LLM KV cache compression.

Random rotation: Multiply the input vector by a fixed random orthogonal matrix. This makes each coordinate follow a known Beta(d/2, d/2) distribution. Lloyd-Max scalar quantization: Quantize each ...
TechSpot

Google's TurboQuant compression tech cuts LLM memory use by 6x with no accuracy loss

The big picture: Google has developed three AI compression algorithms – TurboQuant, PolarQuant, and Quantized Johnson-Lindenstrauss – designed to significantly reduce the memory footprint of large ...
Ars Technica

Google’s TurboQuant AI-compression algorithm can reduce LLM memory usage by 6x

Even if you don’t know much about the inner workings of generative AI models, you probably know they need a lot of memory. Hence, it is currently almost impossible to buy a measly stick of RAM without ...