  Recently, learned image compression methods have been actively studied. Among
them, entropy-minimization based approaches have achieved superior results
compared to conventional image codecs such as BPG and JPEG2000. However, the
quality enhancement and rate-minimization are conflictively coupled in the
process of image compression. That is, maintaining high image quality entails
less compression and vice versa. However, by jointly training separate quality
enhancement in conjunction with image compression, the coding efficiency can be
improved. In this paper, we propose a novel joint learning scheme of image
compression and quality enhancement, called JointIQ-Net, as well as entropy
model improvement, thus achieving significantly improved coding efficiency
against the previous methods. Our proposed JointIQ-Net combines an image
compression sub-network and a quality enhancement sub-network in a cascade,
both of which are end-to-end trained in a combined manner within the
JointIQ-Net. Also the JointIQ-Net benefits from improved entropy-minimization
that newly adopts a Gussian Mixture Model (GMM) and further exploits global
context to estimate the probabilities of latent representations. In order to
show the effectiveness of our proposed JointIQ-Net, extensive experiments have
been performed, and showed that the JointIQ-Net achieves a remarkable
performance improvement in coding efficiency in terms of both PSNR and MS-SSIM,
compared to the previous learned image compression methods and the conventional
codecs such as VVC Intra (VTM 7.1), BPG, and JPEG2000. To the best of our
knowledge, this is the first end-to-end optimized image compression method that
outperforms VTM 7.1 (Intra), the latest reference software of the VVC standard,
in terms of the PSNR and MS-SSIM.
