  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.
