QARV: Quantization-Aware ResNet VAE for Lossy Image Compression

We consider the problem of lossy image compression, a fundamental problem in both information theory and many real-world applications. We begin by reviewing the relationship between variational autoencoders (VAEs), a powerful class of deep generative models, and rate-distortion theory, the theoretical foundation for lossy compression. By combining the ResNet VAE architecture and techniques including test-time quantization and quantization-aware training, we present a quantization-aware ResNet VAE (QARV) framework for lossy image compression. For sake of practical usage, we propose a new neural network architecture for fast decoding, and we introduce an adaptive normalization operation for variable-rate coding. QARV employs a hierarchical progressive coding structure, supports continuously variable-rate compression with fast entropy coding, and gives a better rate-distortion efficiency than existing baseline methods. Code is made publicly available at https://github.com/duanzhiihao/lossy-vae