  Recently, image compression codecs based on Neural Networks(NN) outperformed
the state-of-art classic ones such as BPG, an image format based on HEVC intra.
However, the typical NN codec has high complexity, and it has limited options
for parallel data processing. In this work, we propose a conditional separation
principle that aims to improve parallelization and lower the computational
requirements of an NN codec. We present a Conditional Color Separation (CCS)
codec which follows this principle. The color components of an image are split
into primary and non-primary ones. The processing of each component is done
separately, by jointly trained networks. Our approach allows parallel
processing of each component, flexibility to select different channel numbers,
and an overall complexity reduction. The CCS codec uses over 40% less memory,
has 2x faster encoding and 22% faster decoding speed, with only 4% BD-rate loss
in RGB PSNR compared to our baseline model over BPG.
