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Found 16 papers, 8 papers with code
Localization of Just Noticeable Difference for Image Compression
By applying this framework, we created a novel PJND dataset, KonJND++, consisting of 300 source images, compressed versions thereof under JPEG or BPG compression, and an average of 43 ratings of PJND and 129 self-reported locations of JND-critical regions for each source image.
Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery
To effectively offset the negative impact between modalities in the process of multi-modal integration and extract heterogeneous information from graphs, we propose a novel method called MMKGL (Multi-modal Multi-Kernel Graph Learning).
Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model
An accurate computational model for image quality assessment (IQA) benefits many vision applications, such as image filtering, image processing, and image generation.
TranSalNet: Towards perceptually relevant visual saliency prediction
Due to the inherent inductive biases of CNN architectures, there is a lack of sufficient long-range contextual encoding capacity.
Ranked #1 on
Saliency Prediction
on SALICON
Subjective Image Quality Assessment with Boosted Triplet Comparisons
We also provide the details for Thurstonian scale reconstruction from TC and our annotated dataset, KonFiG-IQA, containing 10 source images, processed using 7 distortion types at 12 or even 30 levels, uniformly spaced over a span of 3 JND units.
EvolGAN: Evolutionary Generative Adversarial Networks
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.
Tarsier: Evolving Noise Injection in Super-Resolution GANs
More generally, our approach can be used to optimize any method based on noise injection.
DeepFL-IQA: Weak Supervision for Deep IQA Feature Learning
We propose a new IQA dataset and a weakly supervised feature learning approach to train features more suitable for IQA of artificially distorted images.
Subjective Annotation for a Frame Interpolation Benchmark using Artefact Amplification
This re-ranking not only shows the necessity of visual quality assessment as another evaluation metric for optical flow and frame interpolation benchmarks, the results also provide the ground truth for designing novel image quality assessment (IQA) methods dedicated to perceptual quality of interpolated images.
SUR-FeatNet: Predicting the Satisfied User Ratio Curvefor Image Compression with Deep Feature Learning
We then use deep feature learning to predict samples of the SUR curve and apply the method of least squares to fit the parametric model to the predicted samples.
KonVid-150k: A Dataset for No-Reference Video Quality Assessment of Videos in-the-Wild
Video quality assessment (VQA) methods focus on particular degradation types, usually artificially induced on a small set of reference videos.
Detecting motorcycle helmet use with deep learning
Our algorithm registers motorcycle helmet use rates with an accuracy of -4. 4% and +2. 1% in comparison to a human observer, with minimal training for individual observation sites.
KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment
Deep learning methods for image quality assessment (IQA) are limited due to the small size of existing datasets.
Ranked #16 on
Video Quality Assessment
on MSU NR VQA Database
Algorithm Selection for Image Quality Assessment
We compared 8 state-of-the-art algorithms for blind IQA and showed that an oracle, able to predict the best performing method for any given input image, yields a hybrid method that could outperform even the best single existing method by a large margin.
Technical Report on Visual Quality Assessment for Frame Interpolation
visual quality of interpolated frames mostly based on optical flow estimation.
KonIQ-10k: Towards an ecologically valid and large-scale IQA database
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.
Ranked #3 on
Image Quality Assessment
on KonIQ-10k
