Deep Generative Model based Rate-Distortion for Image Downscaling
  Assessment
Yuanbang Liang Bhavesh Garg Paul L Rosin Yipeng Qin
In this paper, we propose Image Downscaling Assessment by Rate-Distortion
(IDA-RD), a novel measure to quantitatively evaluate image downscaling
algorithms. In contrast to image-based methods that measure the quality of
downscaled images, ours is process-based that draws ideas from rate-distortion
theory to measure the distortion incurred during downscaling. Our main idea is
that downscaling and super-resolution (SR) can be viewed as the encoding and
decoding processes in the rate-distortion model, respectively, and that a
downscaling algorithm that preserves more details in the resulting
low-resolution (LR) images should lead to less distorted high-resolution (HR)
images in SR. In other words, the distortion should increase as the downscaling
algorithm deteriorates. However, it is non-trivial to measure this distortion
as it requires the SR algorithm to be blind and stochastic. Our key insight is
that such requirements can be met by recent SR algorithms based on deep
generative models that can find all matching HR images for a given LR image on
their learned image manifolds. Extensive experimental results show the
effectiveness of our IDA-RD measure.