  In this work, we present a comparison between color spaces namely YUV, LAB,
RGB and their effect on learned image compression. For this we use the
structure and color based learned image codec (SLIC) from our prior work, which
consists of two branches - one for the luminance component (Y or L) and another
for chrominance components (UV or AB). However, for the RGB variant we input
all 3 channels in a single branch, similar to most learned image codecs
operating in RGB. The models are trained for multiple bitrate configurations in
each color space. We report the findings from our experiments by evaluating
them on various datasets and compare the results to state-of-the-art image
codecs. The YUV model performs better than the LAB variant in terms of MS-SSIM
with a Bj{\o}ntegaard delta bitrate (BD-BR) gain of 7.5\% using VTM
intra-coding mode as the baseline. Whereas the LAB variant has a better
performance than YUV model in terms of CIEDE2000 having a BD-BR gain of 8\%.
Overall, the RGB variant of SLIC achieves the best performance with a BD-BR
gain of 13.14\% in terms of MS-SSIM and a gain of 17.96\% in CIEDE2000 at the
cost of a higher model complexity.
