General generative model-based image compression method using an optimisation encoder

• Published in: IET image processing Vol. 14; no. 9; pp. 1750 - 1758
• Main Authors: Wu, Mengtian, He, Zaixing, Zhao, Xinyue, Zhang, Shuyou
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 20.07.2020
• Subjects: backpropagation; computer vision; data compression; deep generative model; encoding; encoding process; extremely small shape‐fixed encoded space; GAN generator model; GANs; general generative model‐based image compression method; generative adversarial networks; generative models; image coding; image inpainting algorithm; image restoration; intensively studied subject; optimal encoded representations; optimisation; optimisation algorithm; optimisation encoder; optimisation task; Research Article; synthetic images; generative adversarial networks; data compression; intensively studied subject; extremely small shape-fixed encoded space; optimisation task; image inpainting algorithm; encoding process; optimal encoded representations; generative models; GAN generator model; general generative model-based image compression method; encoding; deep generative model; synthetic images; optimisation encoder; optimisation; image restoration; backpropagation; computer vision; optimisation algorithm; image coding; GANs
• ISSN: 1751-9659; 1751-9667
• DOI: 10.1049/iet-ipr.2019.0715

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.