Towards practical vector quantization for image coding

Vector quantization (VQ) has long been recognized for its potential in image compression due to its ability to represent high-dimensional signal vectors compactly. However, the adoption of VQ in modern codecs has been hindered by practical limitations such as fixed-rate codebooks, lack of scalability, and inefficiency in entropy coding. In this work, we revisit VQ from a practical perspective and explore effective techniques to integrate it into modern image coding frameworks. We propose a rate-distortion-oriented (RD-oriented) design of tree-structured vector quantization (TSVQ) that enables flexible codebook construction at multiple bit rates through a growing method. Moreover, we introduce a cascade VQ strategy to promote the VQ ability by successively quantizing residual vectors, and propose a switching point decision method based on RD slope analysis. Our proposed methods enable scalable and RD-optimized vector quantization, demonstrating the potential of being applied to practical image coding frameworks.