Blind image quality assessment (BIQA) aims to automatically evaluate the perceived quality
of a single image, whose performance has been improved by deep learning-based methods …

In this paper, a framework based on two feature extraction networks and a multilevel feature
fusion (MFF) network is proposed. Multilevel degradation features can be obtained through …

Blind image quality assessment (BIQA) has always been a challenging problem due to the
absence of reference images. In this paper, we propose a novel dual-branch vision …

We propose a no-reference image quality assessment (NR-IQA) approach to predict the
perceptual quality score of a given image without using any reference image. Our model …

In observing images, the perception of the human visual system (HVS) is affected by both
image contents and distortions. Obviously, the visual quality of the same image varies under …

With the development of image generation technology, AI-based image generation has
been applied in various fields. However, the development of AIGC image generative models …

Real-world Super-Resolution (real-SR) methods focus on dealing with diverse real-world
images and have attracted increasing attention in recent years. The key idea is to use a …

In recent years, Artificial Intelligence Generated Content (AIGC) has gained widespread
attention beyond the computer science community. Due to various issues arising from …

Blind image quality assessment (BIQA) is a task that predicts the perceptual quality of an
image without its reference. Research on BIQA attracts growing attention due to the …

Although many effective models and real-world datasets have been presented for blind
image quality assessment (BIQA), recent BIQA models usually tend to fit specific training set …