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Found 10 papers, 9 papers with code
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 relative 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.
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.
Blind Image Quality Assessment
No-Reference Image Quality Assessment
+1
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.
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
No-Reference 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.
