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
Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm Regularization
To be specific, we present theoretical evidence showing that the magnitude of score changes is related to the $\ell_1$ norm of the model's gradient with respect to the input image.
Quality-Aware Image-Text Alignment for Real-World Image Quality Assessment
In particular, we introduce a quality-aware image-text alignment strategy to make CLIP generate representations that correlate with the inherent quality of the images.
Transformer-based No-Reference Image Quality Assessment via Supervised Contrastive Learning
We first train a model on a large-scale synthetic dataset by SCL (no image subjective score is required) to extract degradation features of images with various distortion types and levels.
Learning Generalizable Perceptual Representations for Data-Efficient No-Reference Image Quality Assessment
No-reference (NR) image quality assessment (IQA) is an important tool in enhancing the user experience in diverse visual applications.
PKU-I2IQA: An Image-to-Image Quality Assessment Database for AI Generated Images
jiquan123/i2iqa
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Although previous work has established several human perception-based AIGC image quality assessment (AIGCIQA) databases for text-generated images, the AI image generation technology includes scenarios like text-to-image and image-to-image, and assessing only the images generated by text-to-image models is insufficient.
VCISR: Blind Single Image Super-Resolution with Video Compression Synthetic Data
kiteretsu77/vcisr-official
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In this work, we for the first time, present a video compression-based degradation model to synthesize low-resolution image data in the blind SISR task.
ARNIQA: Learning Distortion Manifold for Image Quality Assessment
miccunifi/arniqa
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In this work, we propose a self-supervised approach named ARNIQA (leArning distoRtion maNifold for Image Quality Assessment) for modeling the image distortion manifold to obtain quality representations in an intrinsic manner.
You Only Train Once: A Unified Framework for Both Full-Reference and No-Reference Image Quality Assessment
When our proposed model is independently trained on NR or FR IQA tasks, it outperforms existing models and achieves state-of-the-art performance.
On the Effectiveness of Spectral Discriminators for Perceptual Quality Improvement
luciennnnnnn/dualformer
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ICCV 2023
We tackle this issue by examining the spectral discriminators in the context of perceptual image super-resolution (i. e., GAN-based SR), as SR image quality is susceptible to spectral changes.
Self2Self+: Single-Image Denoising with Self-Supervised Learning and Image Quality Assessment Loss
To improve the feasibility of denoising procedures, in this study, we proposed a single-image self-supervised learning method in which only the noisy input image is used for network training.
