With the advent of image super-resolution (SR) algorithms, how to evaluate
the quality of generated SR images has become an urgent task. Although
full-reference methods perform well in SR image quality assessment (SR-IQA),
their reliance on high-resolution (HR) images limits their practical
applicability. Leveraging available reconstruction information as much as
possible for SR-IQA, such as low-resolution (LR) images and the scale factors,
is a promising way to enhance assessment performance for SR-IQA without HR for
reference. In this letter, we attempt to evaluate the perceptual quality and
reconstruction fidelity of SR images considering LR images and scale factors.
Specifically, we propose a novel dual-branch reduced-reference SR-IQA network,
\ie, Perception- and Fidelity-aware SR-IQA (PFIQA). The perception-aware branch
evaluates the perceptual quality of SR images by leveraging the merits of
global modeling of Vision Transformer (ViT) and local relation of ResNet, and
incorporating the scale factor to enable comprehensive visual perception.
Meanwhile, the fidelity-aware branch assesses the reconstruction fidelity
between LR and SR images through their visual perception. The combination of
the two branches substantially aligns with the human visual system, enabling a
comprehensive SR image evaluation. Experimental results indicate that our PFIQA
outperforms current state-of-the-art models across three widely-used SR-IQA
benchmarks. Notably, PFIQA excels in assessing the quality of real-world SR
images.

通过利用低分辨率图像和比例因子来评估图像超分辨率（SR）图像的感知质量和重建保真度，本研究提出了一种新颖的双分支减参考 SR-IQA 网络（PFIQA），其中感知分支利用 Vision Transformer（ViT）和 ResNet 的全局建模和局部关系，结合比例因子实现综合视觉感知，而保真性分支通过视觉感知评估低分辨率和超分辨率图像之间的重建保真度，两个分支的结合与人类视觉系统高度契合，实现全面的 SR 图像评估。实验结果表明，PFIQA 在三个广泛使用的 SR-IQA 基准测试中优于当前最先进的模型，特别在评估真实世界 SR 图像质量方面表现出色。