  In contrast to traditional compression techniques performing linear
transforms, the latent space of popular compressive autoencoders is obtained
from a learned nonlinear mapping and hard to interpret. In this paper, we
explore a promising alternative approach for neural compression, with an
autoencoder whose latent space represents a nonlinear wavelet decomposition.
Previous work has shown that neural wavelet image coding can outperform HEVC.
However, the approach codes color components independently, thereby ignoring
inter-component dependencies. Hence, we propose a novel cross-component context
model (CCM). With CCM, the entropy model for the chroma latent space can be
conditioned on previously coded components exploiting correlations in the
learned wavelet space. The proposed CCM outperforms the baseline model with
average Bj{\o}ntegaard delta rate savings of 2.6 % and 1.6 % for the Kodak and
Tecnick image sets. Also, our method is competitive with VVC and learning-based
methods.
