Noise-to-Compression Variational Autoencoder for Efficient End-to-End Optimized Image Coding

Generative model has emerged as a disruptive alternative for lossy compression of natural images, but suffers from the low-fidelity reconstruction. In this paper, we propose a noise-to-compression variational antoencoder (NC-VAE) to achieve efficient rate-distortion optimization (RDO) for end-to-end optimized image compression with a guarantee of fidelity. The proposed NC-VAE improves rate-distortion performance by adaptively adjusting the distribution of latent variables with trainable noise perturbation. Consequently, high-efficiency RDO is developed based on the distribution of latent variables for simplified decoder. Furthermore, robust end-to-end learning is developed over the corrupted inputs to suppress the deformation and color drift in standard VAE based generative models. Experimental results show that NC-VAE outperforms the state-of-the-art lossy image coders and recent end-to-end optimized compression methods in low bit-rate region, i.e., below 0.2 bits per pixel (bpp).