论文标题
图像超分辨率的质量评估：平衡确定性和统计保真度
Quality Assessment of Image Super-Resolution: Balancing Deterministic and Statistical Fidelity
论文作者
论文摘要
人们对开发图像超分辨率（SR）算法的兴趣越来越大，该算法将低分辨率（LR）转换为更高分辨率的图像，但是自动评估超级分辨图像的视觉质量仍然是一个具有挑战性的问题。在这里，我们在确定性忠诚度（DF）与统计保真度（SF）的二维（2D）空间中查看SR图像质量评估（SR IQA）的问题。这使我们能够更好地理解现有SR算法的优势和缺点，这些算法在（DF，SF）的2D空间中在不同簇中产生图像。具体而言，我们观察到更传统的SR算法的有趣趋势，这些趋势通常倾向于在失去SF的同时优化DF，再到最新的基于生成的对抗网络（GAN）的方法，相反，这些方法在实现高SF方面具有很强的优势，但有时在维持DF时会显得较弱。此外，我们提出了一种基于内容依赖性的清晰度和纹理评估的不确定性加权方案，将两种保真度度量合并为名为“超级分辨率图像保真度（SRIF）索引”的总体质量预测，该索引在对主题评估的数据集中进行测试时证明了针对最先进的ART IQA模型的出色性能。
There has been a growing interest in developing image super-resolution (SR) algorithms that convert low-resolution (LR) to higher resolution images, but automatically evaluating the visual quality of super-resolved images remains a challenging problem. Here we look at the problem of SR image quality assessment (SR IQA) in a two-dimensional (2D) space of deterministic fidelity (DF) versus statistical fidelity (SF). This allows us to better understand the advantages and disadvantages of existing SR algorithms, which produce images at different clusters in the 2D space of (DF, SF). Specifically, we observe an interesting trend from more traditional SR algorithms that are typically inclined to optimize for DF while losing SF, to more recent generative adversarial network (GAN) based approaches that by contrast exhibit strong advantages in achieving high SF but sometimes appear weak at maintaining DF. Furthermore, we propose an uncertainty weighting scheme based on content-dependent sharpness and texture assessment that merges the two fidelity measures into an overall quality prediction named the Super Resolution Image Fidelity (SRIF) index, which demonstrates superior performance against state-of-the-art IQA models when tested on subject-rated datasets.
