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.

翻译：最近的研究揭示了变分自编码器（VAE）与率失真理论之间的重要理论联系，特别是在利用VAE估计图像信息率失真函数的理论上界方面。这些估计的理论上界显著超越了现有神经图像编解码器（NIC）的性能。为缩小这一差距，我们提出了一种理论边界引导的分层变分自编码器（BG-VAE）用于NIC。所提出的BG-VAE利用理论边界引导NIC模型向更优性能方向优化。我们采用分层VAE实现BG-VAE，并通过大量实验证明其有效性。结合先进的神经网络模块，我们提供了一个在率失真性能和计算复杂度方面均优于现有方法的通用可变速率NIC。代码已开源在BG-VAE。