Data compression plays a crucial role in today’s digital world, allowing for efficient storage and transmission of information. Traditional compression algorithms focus on reducing redundancies in data sequences, resulting in smaller file sizes but some loss of information. However, neural compression techniques, which use neural networks to represent and compress data, are emerging as a new approach. These techniques have the potential to achieve high compression rates with minimal loss of perceptual information. Neural compression can be applied to various data types, such as images and audio, and can recreate data with high accuracy using upscaling and super-resolution techniques. In the realm of audio compression, neural audio codecs based on Residual Vector Quantization surpass traditional methods. These codecs compress audio signals while maintaining high fidelity. They employ an encoder, a quantizer, and a decoder, all trained simultaneously, and utilize Residual Vector Quantization to compress the encoded vector. This approach breaks down the quantization process into multiple layers, allowing for efficient compression. Neural compression methods, particularly those employing Residual Vector Quantization, are revolutionizing audio codecs and are paving the way for more efficient and sustainable digital ecosystems.
https://www.assemblyai.com/blog/what-is-residual-vector-quantization/