No-Reference Image Quality Assessment
53 papers with code • 5 benchmarks • 5 datasets
An Image Quality Assessment approach where no reference image information is available to the model. Sometimes referred to as Blind Image Quality Assessment (BIQA).
CSIQ
KADID-10k
MSU NR VQA Database
TID2013
Latest papers with no code
Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives
Meanwhile, it is important to note that the correlation, like ranking correlation, plays a significant role in NR-IQA tasks.
Comparison of No-Reference Image Quality Models via MAP Estimation in Diffusion Latents
Contemporary no-reference image quality assessment (NR-IQA) models can effectively quantify the perceived image quality, with high correlations between model predictions and human perceptual scores on fixed test sets.
PromptIQA: Boosting the Performance and Generalization for No-Reference Image Quality Assessment via Prompts
On the other hand, PromptIQA is trained on a mixed dataset with two proposed data augmentation strategies to learn diverse requirements, thus enabling it to effectively adapt to new requirements.
Black-box Adversarial Attacks Against Image Quality Assessment Models
Specifically, we first formulate the attack problem as maximizing the deviation between the estimated quality scores of original and perturbed images, while restricting the perturbed image distortions for visual quality preservation.
Diffusion Model Based Visual Compensation Guidance and Visual Difference Analysis for No-Reference Image Quality Assessment
Firstly, we devise a new diffusion restoration network that leverages the produced enhanced image and noise-containing images, incorporating nonlinear features obtained during the denoising process of the diffusion model, as high-level visual information.
GMC-IQA: Exploiting Global-correlation and Mean-opinion Consistency for No-reference Image Quality Assessment
Due to the subjective nature of image quality assessment (IQA), assessing which image has better quality among a sequence of images is more reliable than assigning an absolute mean opinion score for an image.
Exploring Vulnerabilities of No-Reference Image Quality Assessment Models: A Query-Based Black-Box Method
Ensuring the robustness of NR-IQA methods is vital for reliable comparisons of different image processing techniques and consistent user experiences in recommendations.
UIEDP:Underwater Image Enhancement with Diffusion Prior
To address this issue, we propose UIE with Diffusion Prior (UIEDP), a novel framework treating UIE as a posterior distribution sampling process of clear images conditioned on degraded underwater inputs.
Adaptive Feature Selection for No-Reference Image Quality Assessment using Contrastive Mitigating Semantic Noise Sensitivity
The current state-of-the-art No-Reference Image Quality Assessment (NR-IQA) methods typically use feature extraction in upstream backbone networks, which assumes that all extracted features are relevant.
Less is More: Learning Reference Knowledge Using No-Reference Image Quality Assessment
Concretely, by innovatively introducing a novel feature distillation method in IQA, we propose a new framework to learn comparative knowledge from non-aligned reference images.
