Theoretical Bound-Guided Hierarchical VAE for Neural Image Codecs
<|reference_start|>Theoretical Bound-Guided Hierarchical VAE for Neural Image Codecs: 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.<|reference_end|>
@article{zhang2024theoretical,
    title={Theoretical Bound-Guided Hierarchical VAE for Neural Image Codecs},
    author={Yichi Zhang, Zhihao Duan, Yuning Huang, Fengqing Zhu},
    journal={arXiv preprint arXiv:2403.18535},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.18535},
    primaryClass={eess.IV cs.LG}
}