690 citations · 1.7k across the 14 of their papers we have counts for
17 papers
When Composition Doesn't Add Up: Humans Identifying Defects in AI-Generated Images
Ruoqi Hu, Chulin Zhao, Jiashuo Chang +2
*Chulin Zhao and Ruoqi Hu contributed equally to this work. State-of-the-art text-to-image (T2I) models exhibit pronounced and systematic defects when prompts involve intricate com…
MMP-2K: A Benchmark Multi-Labeled Macro Photography Image Quality Assessment Database
Jiashuo Chang, Zhengyi Li, Jianxun Lou +2
Macro photography (MP) is a specialized field of photography that captures objects at an extremely close range, revealing tiny details. Although an accurate macro photography image…
Localization of Just Noticeable Difference for Image Compression
Guangan Chen, Hanhe Lin, Oliver Wiedemann +1
The just noticeable difference (JND) is the minimal difference between stimuli that can be detected by a person. The picture-wise just noticeable difference (PJND) for a given refe…
Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model
Shaolin Su, Hanhe Lin, Vlad Hosu +6
An accurate computational model for image quality assessment (IQA) benefits many vision applications, such as image filtering, image processing, and image generation. Although the…
TranSalNet: Towards perceptually relevant visual saliency prediction
Jianxun Lou, Hanhe Lin, David Marshall +2
Visual saliency prediction using transformers - Convolutional neural networks (CNNs) have significantly advanced computational modelling for saliency prediction. However, accuratel…
Subjective Image Quality Assessment with Boosted Triplet Comparisons
Hui Men, Hanhe Lin, Mohsen Jenadeleh +1
In subjective full-reference image quality assessment, differences between perceptual image qualities of the reference image and its distorted versions are evaluated, often using d…
EvolGAN: Evolutionary Generative Adversarial Networks
Baptiste Roziere, Fabien Teytaud, Vlad Hosu +4
We propose to use a quality estimator and evolutionary methods to search the latent space of generative adversarial networks trained on small, difficult datasets, or both. The new…
Tarsier: Evolving Noise Injection in Super-Resolution GANs
Baptiste Roziere, Nathanal Carraz Rakotonirina, Vlad Hosu +4
Super-resolution aims at increasing the resolution and level of detail within an image. The current state of the art in general single-image super-resolution is held by NESRGAN+, w…
DeepFL-IQA: Weak Supervision for Deep IQA Feature Learning
Hanhe Lin, Vlad Hosu, Dietmar Saupe
Multi-level deep-features have been driving state-of-the-art methods for aesthetics and image quality assessment (IQA). However, most IQA benchmarks are comprised of artificially d…
Subjective Annotation for a Frame Interpolation Benchmark using Artefact Amplification
Hui Men, Vlad Hosu, Hanhe Lin +2
Current benchmarks for optical flow algorithms evaluate the estimation either directly by comparing the predicted flow fields with the ground truth or indirectly by using the predi…
SUR-FeatNet: Predicting the Satisfied User Ratio Curvefor Image Compression with Deep Feature Learning
Hanhe Lin, Vlad Hosu, Chunling Fan +4
The satisfied user ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, characterizes the complementary cumulative distribution function of the just noticeable diffe…
KonVid-150k: A Dataset for No-Reference Video Quality Assessment of Videos in-the-Wild
Franz Götz-Hahn, Vlad Hosu, Hanhe Lin +1
Video quality assessment (VQA) methods focus on particular degradation types, usually artificially induced on a small set of reference videos. Hence, most traditional VQA methods u…
Detecting motorcycle helmet use with deep learning
Felix Wilhelm Siebert, Hanhe Lin
The continuous motorization of traffic has led to a sustained increase in the global number of road related fatalities and injuries. To counter this, governments are focusing on en…
KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment
Vlad Hosu, Hanhe Lin, Tamas Sziranyi +1
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 generatin…
Algorithm Selection for Image Quality Assessment
Markus Wagner, Hanhe Lin, Shujun Li +1
Subjective perceptual image quality can be assessed in lab studies by human observers. Objective image quality assessment (IQA) refers to algorithms for estimation of the mean subj…
Technical Report on Visual Quality Assessment for Frame Interpolation
Hui Men, Hanhe Lin, Vlad Hosu +3
Current benchmarks for optical flow algorithms evaluate the estimation quality by comparing their predicted flow field with the ground truth, and additionally may compare interpola…
KonIQ-10k: Towards an ecologically valid and large-scale IQA database
Hanhe Lin, Vlad Hosu, Dietmar Saupe
The main challenge in applying state-of-the-art deep learning methods to predict image quality in-the-wild is the relatively small size of existing quality scored datasets. The rea…