Theoretical Bound-Guided Hierarchical VAE for Neural Image Codecs
Yichi Zhang Zhihao Duan Yuning Huang Fengqing Zhu
Recent studies reveal a significant theoretical link between variational
autoencoders (VAEs) and rate-distortion theory, notably in utilizing VAEs to
estimate the theoretical upper bound of the information rate-distortion
function of images. Such estimated theoretical bounds substantially exceed the
performance of existing neural image codecs (NICs). To narrow this gap, we
propose a theoretical bound-guided hierarchical VAE (BG-VAE) for NIC. The
proposed BG-VAE leverages the theoretical bound to guide the NIC model towards
enhanced performance. We implement the BG-VAE using Hierarchical VAEs and
demonstrate its effectiveness through extensive experiments. Along with
advanced neural network blocks, we provide a versatile, variable-rate NIC that
outperforms existing methods when considering both rate-distortion performance
and computational complexity. The code is available at BG-VAE.