An End-to-End Deep Generative Network for Low Bitrate Image Coding
Yifei Pei, Ying Liu, Nam Ling, Yongxiong Ren, Lingzhi Liu · 2023
Generative adversarial network (GAN)-based image compression approaches reconstruct images with highly realistic quality at low bit rates. However, currently there is no published GAN-based image compression approach that utilizes advanced GAN losses, such as the Wasserstein GAN with gradient penalty loss (WGAN-GP), to improve the quality of reconstructed images. Meanwhile, existing deep learning-based image compression approaches require extra convolution layers to estimate and constrain the entropy during training, which makes the network larger and may require extra bits to send information to the decoder. In this paper, we propose a new GAN for image compression with novel discriminator and generator loss functions and a simple entropy estimation approach. Our new loss functions outperform the current GAN loss for low bitrate image compression. Our entropy estimation approach does not require extra convolution layers but still works well to constrain the number of bits during training.