Deep Generative Model based Rate-Distortion for Image Downscaling Assessment

Yuanbang Liang, Bhavesh Garg, Paul L Rosin, Yipeng Qin

Abstract: In this paper, we propose a novel measure, namely Image Downscaling Assessment by Rate-Distortion (IDA-RD), 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 the 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. Empirically, we first validate our IDA-RD measure with synthetic downscaling algorithms which simulate distortions by adding various types and levels of degradations to the downscaled images. We then test our measure on traditional downscaling algorithms such as bicubic, bilinear, nearest neighbor interpolation as well as state-of-the-art downscaling algorithms such as DPID, L0-regularized downscaling, and Perceptual downscaling. Experimental results show the effectiveness of our IDA-RD in evaluating image downscaling algorithms.

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