TianYi Liu

The rapid evolution of visual data demands compression technologies that balance theoretical expressiveness with practical deployment constraints. Current learning-based approaches face dual challenges: non-differentiable quantization operations that hinder end-to-end optimization, and rigid architectural components limiting adaptability to diverse content characteristics. This paper introduces a novel neural compression framework that integrates principles from Kolmogorov-Arnold Networks (KANs) with dynamic quantization mechanisms.