Perception- and Fidelity-aware Reduced-Reference Super-Resolution Image Quality Assessment
CoRR(2024)
Abstract
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,
, 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.
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