  Normalizing flow models using invertible neural networks (INN) have been
widely investigated for successful generative image super-resolution (SR) by
learning the transformation between the normal distribution of latent variable
$z$ and the conditional distribution of high-resolution (HR) images gave a
low-resolution (LR) input. Recently, image rescaling models like IRN utilize
the bidirectional nature of INN to push the performance limit of image
upscaling by optimizing the downscaling and upscaling steps jointly. While the
random sampling of latent variable $z$ is useful in generating diverse
photo-realistic images, it is not desirable for image rescaling when accurate
restoration of the HR image is more important. Hence, in places of random
sampling of $z$, we propose auxiliary encoding modules to further push the
limit of image rescaling performance. Two options to store the encoded latent
variables in downscaled LR images, both readily supported in existing image
file format, are proposed. One is saved as the alpha-channel, the other is
saved as meta-data in the image header, and the corresponding modules are
denoted as suffixes -A and -M respectively. Optimal network architectural
changes are investigated for both options to demonstrate their effectiveness in
raising the rescaling performance limit on different baseline models including
IRN and DLV-IRN.
