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Found 18 papers, 14 papers with code
Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives
Meanwhile, it is important to note that the correlation, like ranking correlation, plays a significant role in NR-IQA tasks.
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.
When No-Reference Image Quality Models Meet MAP Estimation in Diffusion Latents
In this work, we show -- for the first time -- that NR-IQA models can be plugged into the maximum a posteriori (MAP) estimation framework for image enhancement.
Hierarchical Prior-based Super Resolution for Point Cloud Geometry Compression
The content-dependent hierarchical prior is constructed at the encoder side, which enables coarse-to-fine super resolution of the point cloud geometry at the decoder side.
Exploring Vulnerabilities of No-Reference Image Quality Assessment Models: A Query-Based Black-Box Method
Ensuring the robustness of NR-IQA methods is vital for reliable comparisons of different image processing techniques and consistent user experiences in recommendations.
Lightweight super resolution network for point cloud geometry compression
This paper presents an approach for compressing point cloud geometry by leveraging a lightweight super-resolution network.
Personalized Image Generation for Color Vision Deficiency Population
Approximately, 350 million people, a proportion of 8%, suffer from color vision deficiency (CVD).
Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop
No-reference image quality assessment (NR-IQA) aims to quantify how humans perceive visual distortions of digital images without access to their undistorted references.
Image Quality Assessment: Integrating Model-Centric and Data-Centric Approaches
Learning-based image quality assessment (IQA) has made remarkable progress in the past decade, but nearly all consider the two key components -- model and data -- in isolation.
Deep Geometry Post-Processing for Decompressed Point Clouds
Point cloud compression plays a crucial role in reducing the huge cost of data storage and transmission.
Semi-Supervised Deep Ensembles for Blind Image Quality Assessment
Ensemble methods are generally regarded to be better than a single model if the base learners are deemed to be "accurate" and "diverse."
Continual Learning for Blind Image Quality Assessment
In this paper, we formulate continual learning for BIQA, where a model learns continually from a stream of IQA datasets, building on what was learned from previously seen data.
Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training
We focus on automatically assessing the quality of in-the-wild videos, which is a challenging problem due to the absence of reference videos, the complexity of distortions, and the diversity of video contents.
Ranked #1 on
Video Quality Assessment
on MSU NR VQA Database
Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment
Experiments on two relevant datasets (KonIQ-10k and CLIVE) show that, compared to MAE or MSE loss, the new loss enables the IQA model to converge about 10 times faster and the final model achieves better performance.
Ranked #2 on
Image Quality Assessment
on MSU NR VQA Database
Quality Assessment of In-the-Wild Videos
We propose an objective no-reference video quality assessment method by integrating both effects into a deep neural network.
Ranked #6 on
Video Quality Assessment
on MSU NR VQA Database
Quality Assessment for Tone-Mapped HDR Images Using Multi-Scale and Multi-Layer Information
So we propose a new no-reference method of tone-mapped image quality assessment based on multi-scale and multi-layer features that are extracted from a pre-trained deep convolutional neural network model.
Blind Image Quality Assessment
Multimedia
Exploiting High-Level Semantics for No-Reference Image Quality Assessment of Realistic Blur Images
To guarantee a satisfying Quality of Experience (QoE) for consumers, it is required to measure image quality efficiently and reliably.
Which Has Better Visual Quality: The Clear Blue Sky or a Blurry Animal?
The proposed method, SFA, is compared with nine representative blur-specific NR-IQA methods, two general-purpose NR-IQA methods, and two extra full-reference IQA methods on Gaussian blur images (with and without Gaussian noise/JPEG compression) and realistic blur images from multiple databases, including LIVE, TID2008, TID2013, MLIVE1, MLIVE2, BID, and CLIVE.
