论文标题
具有量化的分层VAE的有损图像压缩
Lossy Image Compression with Quantized Hierarchical VAEs
论文作者
论文摘要
最近的研究表明，变异自动编码器（VAE）与速率延伸理论之间有着密切的理论联系。由此激发，我们从生成建模的角度考虑了有损图像压缩的问题。从最初是为数据（图像）分布建模设计的Resnet VAE开始，我们使用量化感知的后验和先验重新设计其潜在变量模型，从而在测试时易于量化和熵编码。除了改进的神经网络体系结构外，我们还提出了一个强大而有效的模型，该模型优于自然图像有损压缩的先前方法。我们的模型以粗略的方式压缩图像，并支持并行编码和解码，从而在GPU上快速执行。代码可在https://github.com/duanzhiihao/lossy-vae中找到。
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.
