IVQ: Structured and Lightweight Vector Quantization via Binary Hierarchical Composition Inspired by $\textit{IChing}$

Vector Quantization (VQ) has been widely used in visual and audio representation due to its effectiveness in compressing high-dimensional signals. However, existing VQ methods often rely on large and unstructured codebooks, which leads to inefficient code utilization and frequent codebook collapse. In this paper, we propose IChing Vector Quantization (IVQ), a lightweight and structured VQ framework inspired by IChing . IVQ introduces binary hierarchical composition and geometric symmetry relations into the codebook design, enabling a compact set of structured codes to represent the latent space while maintaining high utilization without codebook collapse. Experimental results show that IVQ achieves superior quality with significantly smaller codebooks and consistently higher utilization rates compared to several VQ variants in audio representation. Auxiliary experiments on visual reconstruction and cross-modal generation further validate the universality and robustness of IVQ. Codes are released at https://github.com/chouliuzuo/IVQ.