  This paper presents a cross channel context model for latents in deep image
compression. Generally, deep image compression is based on an autoencoder
framework, which transforms the original image to latents at the encoder and
recovers the reconstructed image from the quantized latents at the decoder. The
transform is usually combined with an entropy model, which estimates the
probability distribution of the quantized latents for arithmetic coding.
Currently, joint autoregressive and hierarchical prior entropy models are
widely adopted to capture both the global contexts from the hyper latents and
the local contexts from the quantized latent elements. For the local contexts,
the widely adopted 2D mask convolution can only capture the spatial context.
However, we observe that there are strong correlations between different
channels in the latents. To utilize the cross channel correlations, we propose
to divide the latents into several groups according to channel index and code
the groups one by one, where previously coded groups are utilized to provide
cross channel context for the current group. The proposed cross channel context
model is combined with the joint autoregressive and hierarchical prior entropy
model. Experimental results show that, using PSNR as the distortion metric, the
combined model achieves BD-rate reductions of 6.30% and 6.31% over the baseline
entropy model, and 2.50% and 2.20% over the latest video coding standard
Versatile Video Coding (VVC) for the Kodak and CVPR CLIC2020 professional
dataset, respectively. In addition, when optimized for the MS-SSIM metric, our
approach generates visually more pleasant reconstructed images.
