  We propose the structure and color based learned image codec (SLIC) in which
the task of compression is split into that of luminance and chrominance. The
deep learning model is built with a novel multi-scale architecture for Y and UV
channels in the encoder, where the features from various stages are combined to
obtain the latent representation. An autoregressive context model is employed
for backward adaptation and a hyperprior block for forward adaptation. Various
experiments are carried out to study and analyze the performance of the
proposed model, and to compare it with other image codecs. We also illustrate
the advantages of our method through the visualization of channel impulse
responses, latent channels and various ablation studies. The model achieves
Bj{\o}ntegaard delta bitrate gains of 7.5% and 4.66% in terms of MS-SSIM and
CIEDE2000 metrics with respect to other state-of-the-art reference codecs.
