The method of importance map has been widely adopted in DNN-based lossy image compression to achieve bit allocation according to the importance of image contents. However, insufficient allocation of bits in non-important regions often leads to severe distortion at low bpp (bits per pixel), which hampers the development of efficient content-weighted image compression systems. This paper rethinks content-based compression by using Generative Adversarial Network (GAN) to reconstruct the non-important regions. Moreover, multiscale pyramid decomposition is applied to both the encoder and the discriminator to achieve global compression of high-resolution images. A tunable compression scheme is also proposed in this paper to compress an image to any specific compression ratio without retraining the model. The experimental results show that our proposed method improves MS-SSIM by more than 10.3% compared to the recently reported GAN-based method to achieve the same low bpp (0.05) on the Kodak dataset.

中文翻译：

---

基于 GAN 的可调图像压缩系统

重要性图的方法在基于 DNN 的有损图像压缩中被广泛采用，以根据图像内容的重要性来实现比特分配。然而，非重要区域中的比特分配不足通常会导致低 bpp（每像素比特数）的严重失真，这阻碍了高效的内容加权图像压缩系统的开发。本文通过使用生成对抗网络 (GAN) 来重建非重要区域，重新思考基于内容的压缩。此外，多尺度金字塔分解应用于编码器和鉴别器，以实现高分辨率图像的全局压缩。本文还提出了一种可调压缩方案，可以在不重新训练模型的情况下将图像压缩到任何特定的压缩率。