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Found 17 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.
Relaxed forced choice improves performance of visual quality assessment methods
The experimental results show that the inclusion of the ``not sure'' response option in the forced choice method reduced mental load and led to models with better data fit and correspondence to ground truth.
KonX: Cross-Resolution Image Quality Assessment
Through KonX, we provide empirical evidence of label shifts caused by changes in the presentation resolution.
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.
Critical analysis on the reproducibility of visual quality assessment using deep features
Data used to train supervised machine learning models are commonly split into independent training, validation, and test sets.
Comment on "No-Reference Video Quality Assessment Based on the Temporal Pooling of Deep Features"
In Neural Processing Letters 50, 3 (2019) a machine learning approach to blind video quality assessment was proposed.
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.
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.
Effective Aesthetics Prediction with Multi-level Spatially Pooled Features
We propose an effective deep learning approach to aesthetics quality assessment that relies on a new type of pre-trained features, and apply it to the AVA data set, the currently largest aesthetics database.
Ranked #3 on
Aesthetics Quality Assessment
on AVA
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
