Deep Generative Model based Rate-Distortion for Image Downscaling Assessment

Abstract

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 dis-torted 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 re-cent SR algorithms based on deep generative models that can find all matching HR images for a given LR image on their learned manifolds. Extensive experimental results show the effectiveness of our IDA-RD measure. Our code is available at: https://github.com/Byronliang8/Ida-Rd