Image compression is an intensively studied subject in computer vision. The deep generative model, especially generative adversarial networks (GANs), is a popular new direction for this subject. In this study, the authors propose a new compression method based on a generative model and focus on its application by GANs. The decoder in the proposed method is modified from the GAN generator model, which can produce visually real-like synthetic images. It is one of the two models in GANs, which is trained through a two-players' contest game. The encoder is an optimisation algorithm called backpropagation-to-the-input, which derives from an image inpainting algorithm based on generative models. In the proposed method, the authors turn the encoding process into an optimisation task to search for optimal encoded representations. Compared with traditional methods, the proposed method can compress images from certain domains into extremely small and shape-fixed encoded space but still retain better visual representations. It is easy and convenient to apply without any retraining or additional modification to the generative models.

中文翻译：

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使用优化编码器的基于生成模型的通用图像压缩方法

图像压缩是计算机视觉中一个深入研究的课题。深入的生成模型，尤其是生成对抗网络（GAN），是该主题流行的新方向。在这项研究中，作者提出了一种基于生成模型的新压缩方法，并着重于GANs的应用。所提出的方法中的解码器是从GAN生成器模型修改而来的，可以生成视觉上逼真的合成图像。它是GAN中两个模型之一，通过两个玩家的竞赛游戏进行训练。编码器是一种称为输入反向传播的优化算法，它是基于生成模型的图像修复算法得出的。在提出的方法中，作者将编码过程转变为优化任务，以搜索最佳的编码表示形式。与传统方法相比，所提出的方法可以将来自特定域的图像压缩到极小的且形状固定的编码空间中，但仍保留更好的视觉表示。无需对生成模型进行任何再培训或其他修改即可轻松轻松地进行应用。