论文标题
通过使用相对熵编码编码其潜在表示来压缩图像
Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding
论文作者
论文摘要
各种自动编码器（VAE）已在学习的图像压缩中广泛使用。它们用于学习表达潜在表示，下游压缩方法可以以高效率运行。最近提出的“ BITS-BACK”方法可以间接地编码图像的潜在表示，该图像的代码长接近潜在的后部和先验之间的相对熵。但是，由于基本算法，这些方法只能用于无损压缩，并且仅在同时压缩多个图像时才能达到标称效率。它们无效地压缩单个图像。作为替代方案，我们提出了一种新的方法，即相对熵编码（REC），该方法可以直接用接近单个图像的相对熵编码潜在表示，并得到我们在CIFAR10，Imagenet32和Kodak数据集中获得的经验结果的支持。此外，与以前的BITS背包方法不同，REC立即适用于有损耗的压缩，它与Kodak数据集上的最新作品具有竞争力。
Variational Autoencoders (VAEs) have seen widespread use in learned image compression. They are used to learn expressive latent representations on which downstream compression methods can operate with high efficiency. Recently proposed 'bits-back' methods can indirectly encode the latent representation of images with codelength close to the relative entropy between the latent posterior and the prior. However, due to the underlying algorithm, these methods can only be used for lossless compression, and they only achieve their nominal efficiency when compressing multiple images simultaneously; they are inefficient for compressing single images. As an alternative, we propose a novel method, Relative Entropy Coding (REC), that can directly encode the latent representation with codelength close to the relative entropy for single images, supported by our empirical results obtained on the Cifar10, ImageNet32 and Kodak datasets. Moreover, unlike previous bits-back methods, REC is immediately applicable to lossy compression, where it is competitive with the state-of-the-art on the Kodak dataset.
