提出难易度感知评估法，让超分模型表现更真实可比。
Image-Difficulty-Aware Evaluation of Super-Resolution Models
- 用高频指数和边缘不变指数衡量图像难易度
- 发现某些模型在难图上效果差异显著但平均分接近
- 适合想对比模型真实视觉差异的研究者
图像超分辨率模型通常通过基准测试集上的平均分数进行评估，无法反映模型在不同难度图像上的表现差异。某些模型在特定困难图像上会产生伪影，但平均分却难以体现这一问题。本文提出一种难易度感知的评估方法，旨在区分那些在部分图像上视觉结果差异明显但整体平均分相近的超分辨率模型。具体地，提出两个图像难易度度量：高频指数与旋转不变边缘指数，用于预测哪些测试图像中一个模型会显著优于另一个模型。同时设计了一种评估方法，使这些视觉差异能体现在客观指标中。实验验证了所提度量与评估方法的有效性。
原文摘要 · Abstract (English)
Image super-resolution models are commonly evaluated by average scores (over some benchmark test sets), which fail to reflect the performance of these models on images of varying difficulty and that some models generate artifacts on certain difficult images, which is not reflected by the average scores. We propose difficulty-aware performance evaluation procedures to better differentiate between SISR models that produce visually different results on some images but yield close average performance scores over the entire test set. In particular, we propose two image-difficulty measures, the high-frequency index and rotation-invariant edge index, to predict those test images, where a model would yield significantly better visual results over another model, and an evaluation method where these visual differences are reflected on objective measures. Experimental results demonstrate the effectiveness of the proposed image-difficulty measures and evaluation methodology.
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