Blind image quality assessment (BIQA) for authentically distorted images has always been a
challenging problem, since images captured in the wild include varies contents and diverse …

Deep learning methods for image quality assessment (IQA) are limited due to the small size
of existing datasets. Extensive datasets require substantial resources both for generating …

We propose a deep bilinear model for blind image quality assessment that works for both
synthetically and authentically distorted images. Our model constitutes two streams of deep …

Current artificially distorted image quality assessment (IQA) databases are small in size and
limited in content. Larger IQA databases that are diverse in content could benefit the …

In this paper we introduce new methods for the improved training of generative adversarial
networks (GANs) for image synthesis. We construct a variant of GANs employing label …

Abstract While Multimodal Large Language Models (MLLMs) have experienced significant
advancement in visual understanding and reasoning, their potential to serve as powerful …

We propose a multi-task end-to-end optimized deep neural network (MEON) for blind image
quality assessment (BIQA). MEON consists of two sub-networks-a distortion identification …

The ability to estimate the perceptual error between images is an important problem in
computer vision with many applications. Although it has been studied extensively, however …

Video quality assessment (VQA) is an important problem in computer vision. The videos in
computer vision applications are usually captured in the wild. We focus on automatically …

Objective assessment of image quality is fundamentally important in many image processing
tasks. In this paper, we focus on learning blind image quality assessment (BIQA) models …