Scalar Quantization

Scalar quantization, the process of mapping continuous data to a discrete set of values, is a fundamental technique in data compression, particularly crucial for image and video processing. Current research focuses on improving quantization methods within neural network architectures like variational autoencoders (VAEs), often employing techniques such as product quantization and exploring the interplay between quantization surrogates and gradient-based training. These advancements aim to optimize the trade-off between compression rate and reconstruction quality, leading to more efficient and effective data representation across various applications, including image compression and generation.