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Found 23 papers, 8 papers with code
AIGCOIQA2024: Perceptual Quality Assessment of AI Generated Omnidirectional Images
Finally, we conduct a benchmark experiment to evaluate the performance of state-of-the-art IQA models on our database.
LLM-based Interaction for Content Generation: A Case Study on the Perception of Employees in an IT department
Furthermore, our analyses suggest that the frequency of use of generative tools is likely to be a key factor in understanding how employees perceive these tools in the context of their work.
From Just Noticeable Differences to Image Quality
This first JND point provides an intrinsic quantification of the visible distortions within the multimedia content.
On the benefit of parameter-driven approaches for the modeling and the prediction of Satisfied User Ratio for compressed video
However, most of the previous works predict each point in SUR curve by using features both from source video and from compressed videos with assumption that the group-based JND annotations follow Gaussian distribution, which is neither practical nor accurate.
A Framework to Map VMAF with the Probability of Just Noticeable Difference between Video Encoding Recipes
To this end, in order to decouple encoding recipes and JND estimation, we propose a novel framework to map the difference of objective Video Quality Assessment (VQA) scores, i. e., VMAF, between two given videos encoded with different encoding recipes from the same content to the probability of having just noticeable difference between them.
Utility-Oriented Underwater Image Quality Assessment Based on Transfer Learning
Based on this task, we build an Underwater Image Utility Database (UIUD) and a learning-based Underwater Image Utility Measure (UIUM).
Confusing Image Quality Assessment: Towards Better Augmented Reality Experience
An objective metric termed CFIQA is also proposed to better evaluate the confusing image quality.
Improving Maximum Likelihood Difference Scaling method to measure inter content scale
The goal of most subjective studies is to place a set of stimuli on a perceptual scale.
Considering user agreement in learning to predict the aesthetic quality
In this paper, we thus propose (1) a re-adapted multi-task attention network to predict both the mean opinion score and the standard deviation in an end-to-end manner; (2) a brand-new confidence interval ranking loss that encourages the model to focus on image-pairs that are less certain about the difference of their aesthetic scores.
Seeing by haptic glance: reinforcement learning-based 3D object Recognition
Human is able to conduct 3D recognition by a limited number of haptic contacts between the target object and his/her fingers without seeing the object.
Subjective and Objective Quality Assessment of Mobile Gaming Video
Nowadays, with the vigorous expansion and development of gaming video streaming techniques and services, the expectation of users, especially the mobile phone users, for higher quality of experience is also growing swiftly.
Multi-Modal Aesthetic Assessment for MObile Gaming Image
Our inclination is to seek and learn the correlations between different aesthetic relevant dimensions to further boost the generalization performance in predicting all the aesthetic dimensions.
Few-Shot Object Detection in Real Life: Case Study on Auto-Harvest
Confinement during COVID-19 has caused serious effects on agriculture all over the world.
Strategy for Boosting Pair Comparison and Improving Quality Assessment Accuracy
The development of rigorous quality assessment model relies on the collection of reliable subjective data, where the perceived quality of visual multimedia is rated by the human observers.
Capturing and Explaining Trajectory Singularities using Composite Signal Neural Networks
In this paper we present a novel ANN architecture designed to capture the spatio-temporal patterns characteristic of a set of trajectories, while taking into account the demographics of the navigators.
GPM: A Generic Probabilistic Model to Recover Annotator's Behavior and Ground Truth Labeling
In the big data era, data labeling can be obtained through crowdsourcing.
A New Ensemble Adversarial Attack Powered by Long-term Gradient Memories
Deep neural networks are vulnerable to adversarial attacks.
Binocular Rivalry Oriented Predictive Auto-Encoding Network for Blind Stereoscopic Image Quality Measurement
Stereoscopic image quality measurement (SIQM) has become increasingly important for guiding stereo image processing and commutation systems due to the widespread usage of 3D contents.
Multimedia Image and Video Processing
How is Gaze Influenced by Image Transformations? Dataset and Model
Data size is the bottleneck for developing deep saliency models, because collecting eye-movement data is very time consuming and expensive.
Adversarial Attacks against Deep Saliency Models
The proposed attack only requires a part of the model information, and is able to generate a sparser and more insidious adversarial perturbation, compared to traditional image-space attacks.
GANs-NQM: A Generative Adversarial Networks based No Reference Quality Assessment Metric for RGB-D Synthesized Views
In this paper, we proposed a no-reference (NR) quality metric for RGB plus image-depth (RGB-D) synthesis images based on Generative Adversarial Networks (GANs), namely GANs-NQM.
Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation
In this paper we present a hybrid active sampling strategy for pairwise preference aggregation, which aims at recovering the underlying rating of the test candidates from sparse and noisy pairwise labelling.
Prediction of the Influence of Navigation Scan-path on Perceived Quality of Free-Viewpoint Videos
This paper aims at exploring the impact of exploration trajectories (defined as Hypothetical Rendering Trajectories: HRT) on perceived quality of FVV subjectively and objectively, providing two main contributions.
