Lossy Image Compression with Quantized Hierarchical VAEs
arXiv:2208.13056 · doi:10.1109/WACV56688.2023.00028
Abstract
Recent research has shown a strong theoretical connection between variational autoencoders (VAEs) and the rate-distortion theory. Motivated by this, we consider the problem of lossy image compression from the perspective of generative modeling. Starting with ResNet VAEs, which are originally designed for data (image) distribution modeling, we redesign their latent variable model using a quantization-aware posterior and prior, enabling easy quantization and entropy coding at test time. Along with improved neural network architecture, we present a powerful and efficient model that outperforms previous methods on natural image lossy compression. Our model compresses images in a coarse-to-fine fashion and supports parallel encoding and decoding, leading to fast execution on GPUs. Code is available at https://github.com/duanzhiihao/lossy-vae.
WACV 2023 Best Algorithms Paper Award, revised version
References in corpus (5)
- NVAE: A Deep Hierarchical Variational Autoencoder
- CompressAI: a PyTorch library and evaluation platform for end-to-end compression research
- Universally Quantized Neural Compression
- Hierarchical Quantized Autoencoders
- Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding