We propose an end-to-end trainable image compression framework with a
multi-scale and context-adaptive entropy model, especially for low bitrate
compression. Due to the success of autoregressive priors in probabilistic
generative model, the complementary combination of autoregressive and
hierarchical priors can estimate the distribution of each latent representation
accurately. Based on this combination, we firstly propose a multi-scale masked
convolutional network as our autoregressive model. Secondly, for the
significant computational penalty of generative model, we focus on decoded
representations covered by receptive field, and skip full zero latents in
arithmetic codec. At last, according to the low-rate compression's constraint
in CLIC-2019, we use a method to maximize MS-SSIM by allocating bitrate for
each image.

本文提出了一种端到端可训练的图像压缩框架，采用自回归和分层先验的组合估计每个潜在表示的分布，并使用一种方法将比特率分配到每个图像以最大化 MS-SSIM。

多尺度和上下文自适应熵模型用于图像压缩

Multi-scale and Context-adaptive Entropy Model for Image Compression

We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE)
models for large scale image generation. To this end, we scale and enhance the
autoregressive priors used in VQ-VAE to generate synthetic samples of much
higher coherence and fidelity than possible before. We use simple feed-forward
encoder and decoder networks, making our model an attractive candidate for
applications where the encoding and/or decoding speed is critical.
Additionally, VQ-VAE requires sampling an autoregressive model only in the
compressed latent space, which is an order of magnitude faster than sampling in
the pixel space, especially for large images. We demonstrate that a multi-scale
hierarchical organization of VQ-VAE, augmented with powerful priors over the
latent codes, is able to generate samples with quality that rivals that of
state of the art Generative Adversarial Networks on multifaceted datasets such
as ImageNet, while not suffering from GAN's known shortcomings such as mode
collapse and lack of diversity.

本文探讨矢量量化变分自编码器模型 (VQ-VAE) 进行大规模图像生成的应用，采用自回归先验信息，结合简单前馈编码器和解码器网络，以压缩的潜变量空间进行自动回归模型采样，同时使用多尺度层级 VQ-VAE 进行生成，取得比 Generative Adversarial Networks 更加优秀的样本生成品质。