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.

翻译：本文提出一种基于率失真理论的图像下采样评估方法（IDA-RD），用于定量评价图像下采样算法性能。与基于下采样图像质量评估的图像驱动方法不同，本文方法借鉴率失真理论思想，从过程维度衡量下采样过程中产生的失真。核心思想在于：可将下采样与超分辨率（SR）分别视为率失真模型中的编码与解码过程，若下采样算法能在低分辨率（LR）图像中保留更多细节，则超分辨率后的高分辨率（HR）图像失真更小。换言之，失真度应随下采样算法性能劣化而增加。然而，该失真的测量颇具挑战性，因为需要超分辨率算法具备盲性和随机性。本文的关键洞见在于：基于深度生成模型的最新超分辨率算法恰好满足上述要求——这类算法能在学习到的图像流形上，为给定低分辨率图像找到所有匹配的高分辨率图像。大量实验结果表明，本文提出的IDA-RD评估方法具有显著有效性。