  The vast majority of standard image and video content available online is
represented in display-encoded color spaces, in which pixel values are
conveniently scaled to a limited range (0-1) and the color distribution is
approximately perceptually uniform. In contrast, both camera RAW and high
dynamic range (HDR) images are often represented in linear color spaces, in
which color values are linearly related to colorimetric quantities of light.
While training on commonly available display-encoded images is a
well-established practice, there is no consensus on how neural networks should
be trained for tasks on RAW and HDR images in linear color spaces. In this
work, we test several approaches on three popular image restoration
applications: denoising, deblurring, and single-image super-resolution. We
examine whether HDR/RAW images need to be display-encoded using popular
transfer functions (PQ, PU21, mu-law), or whether it is better to train in
linear color spaces, but use loss functions that correct for perceptual
non-uniformity. Our results indicate that neural networks train significantly
better on HDR and RAW images represented in display-encoded color spaces, which
offer better perceptual uniformity than linear spaces. This small change to the
training strategy can bring a very substantial gain in performance, up to 10-15
dB.
