Residual Quantization

Residual quantization (RQ) is a data compression technique that iteratively quantizes the residual error from previous quantization steps, aiming to achieve high accuracy with low bitrates. Current research focuses on improving RQ's performance in various applications, including speech and image coding, through advancements in model architectures like transformers and convolutional neural networks, and the development of novel quantization algorithms such as multi-level codebooks and implicit neural codebooks. These improvements address challenges like the "hourglass" phenomenon (uneven codebook utilization) and aim to extract more information from residuals, leading to better compression rates and reconstruction quality in diverse fields such as speech processing, video compression, and generative retrieval.