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Found 22 papers, 16 papers with code
Mitigating Perception Bias: A Training-Free Approach to Enhance LMM for Image Quality Assessment
By applying these specific distortions to the query or test images, we ensure that the degraded images are recognized as poor quality while their semantics remain.
RCNet: Deep Recurrent Collaborative Network for Multi-View Low-Light Image Enhancement
Recent single image-based enhancement methods may not be able to provide consistently desirable restoration performance for all views due to the ignorance of potential feature correspondence among different views.
Unrolled Decomposed Unpaired Learning for Controllable Low-Light Video Enhancement
To address the above challenge, we propose the Unrolled Decomposed Unpaired Network (UDU-Net) for enhancing low-light videos by unrolling the optimization functions into a deep network to decompose the signal into spatial and temporal-related factors, which are updated iteratively.
Sliced Maximal Information Coefficient: A Training-Free Approach for Image Quality Assessment Enhancement
Full-reference image quality assessment (FR-IQA) models generally operate by measuring the visual differences between a degraded image and its reference.
Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare
Extensive experiments on nine IQA datasets validate that the Compare2Score effectively bridges text-defined comparative levels during training with converted single image quality score for inference, surpassing state-of-the-art IQA models across diverse scenarios.
2AFC Prompting of Large Multimodal Models for Image Quality Assessment
While abundant research has been conducted on improving high-level visual understanding and reasoning capabilities of large multimodal models~(LMMs), their visual quality assessment~(IQA) ability has been relatively under-explored.
Lens-to-lens bokeh effect transformation. NTIRE 2023 challenge report
We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge.
Perceptual Quality Assessment of Face Video Compression: A Benchmark and An Effective Method
In this paper, we introduce the large-scale Compressed Face Video Quality Assessment (CFVQA) database, which is the first attempt to systematically understand the perceptual quality and diversified compression distortions in face videos.
Gap-closing Matters: Perceptual Quality Evaluation and Optimization of Low-Light Image Enhancement
Subsequently, we propose an objective quality assessment measure that plays a critical role in bridging the gap between visual quality and enhancement.
Troubleshooting Ethnic Quality Bias with Curriculum Domain Adaptation for Face Image Quality Assessment
Furthermore, we design an easy-to-hard training scheduler based on the inter-domain uncertainty and intra-domain quality margin as well as the ranking-based domain adversarial network to enhance the effectiveness of transfer learning and further reduce the source risk in domain adaptation.
DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator
ImageNet pre-trained deep neural networks (DNNs) show notable transferability for building effective image quality assessment (IQA) models.
Learning from Mixed Datasets: A Monotonic Image Quality Assessment Model
Deep learning based image quality assessment (IQA) models usually learn to predict image quality from a single dataset, leading the model to overfit specific scenes.
Just Noticeable Difference Modeling for Face Recognition System
In particular, in order to maximum redundancy removal without impairment of robust identity information, we apply the encoder with multiple feature extraction and attention-based feature decomposition modules to progressively decompose face features into two uncorrelated components, i. e., identity and residual features, via self-supervised learning.
Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment
The underlying mechanism of the proposed approach is based upon the mild assumption that the SCIs, which are not physically acquired, still obey certain statistics that could be understood in a learning fashion.
DeepWSD: Projecting Degradations in Perceptual Space to Wasserstein Distance in Deep Feature Space
Existing deep learning-based full-reference IQA (FR-IQA) models usually predict the image quality in a deterministic way by explicitly comparing the features, gauging how severely distorted an image is by how far the corresponding feature lies from the space of the reference images.
The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement
In particular, we create a large-scale database for QUality assessment Of The Enhanced LOw-Light Image (QUOTE-LOL), which serves as the foundation in studying and developing objective quality assessment measures.
Generalized Visual Quality Assessment of GAN-Generated Face Images
However, little work has been dedicated to automatic quality assessment of such GAN-generated face images (GFIs), even less have been devoted to generalized and robust quality assessment of GFIs generated with unseen GAN model.
No-Reference Image Quality Assessment by Hallucinating Pristine Features
In this paper, we propose a no-reference (NR) image quality assessment (IQA) method via feature level pseudo-reference (PR) hallucination.
Detect and Locate: Exposing Face Manipulation by Semantic- and Noise-level Telltales
The technological advancements of deep learning have enabled sophisticated face manipulation schemes, raising severe trust issues and security concerns in modern society.
Camera Invariant Feature Learning for Generalized Face Anti-spoofing
The first branch aims to learn the camera invariant spoofing features via feature level decomposition in the high frequency domain.
Learning Generalized Spatial-Temporal Deep Feature Representation for No-Reference Video Quality Assessment
In this work, we propose a no-reference video quality assessment method, aiming to achieve high-generalization capability in cross-content, -resolution and -frame rate quality prediction.
No-reference Screen Content Image Quality Assessment with Unsupervised Domain Adaptation
Here, we develop the first unsupervised domain adaptation based no reference quality assessment method for SCIs, leveraging rich subjective ratings of the natural images (NIs).
