Perceptual quality assessment plays a vital role in the visual communication systems owing
to the existence of quality degradations introduced in various stages of visual signal …

Abstract The goal of No-Reference Image Quality Assessment (NR-IQA) is to estimate the
perceptual image quality in accordance with subjective evaluations, it is a complex and …

Due to the wavelength-dependent light absorption and scattering, the raw underwater
images are usually inevitably degraded. Underwater image enhancement (UIE) is of great …

Guided by the free-energy principle, generative adversarial networks (GAN)-based no-
reference image quality assessment (NR-IQA) methods have improved the image quality …

No-reference image quality assessment (NR-IQA) is a fundamental yet challenging task in
low-level computer vision community. The difficulty is particularly pronounced for the limited …

For many applications the collection of labeled data is expensive laborious. Exploitation of
unlabeled data during training is thus a long pursued objective of machine learning. Self …

A good distortion representation is crucial for the success of deep blind image quality
assessment (BIQA). However, most previous methods do not effectively model the …

Image quality assessment (IQA) is very important for both end-users and service providers
since a high-quality image can significantly improve the user's quality of experience (QoE) …

The number of images associated with weakly supervised user-provided tags has increased
dramatically in recent years. User-provided tags are incomplete, subjective and noisy. In this …

Ranking problems, also known as preference learning problems, define a widely spread
class of statistical learning problems with many applications, including fraud detection …