46 citations · 177 across the 24 of their papers we have counts for
24 papers
From Global to Granular: Revealing IQA Model Performance via Correlation Surface
Baoliang Chen, Danni Huang, Hanwei Zhu +5
Evaluation of Image Quality Assessment (IQA) models has long been dominated by global correlation metrics, such as Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank-O…
Plug In, Grade Right: Psychology-Inspired AGIQA
Zhicheng Liao, Baoliang Chen, Hanwei Zhu +3
Existing AGIQA models typically estimate image quality by measuring and aggregating the similarities between image embeddings and text embeddings derived from multi-grade quality d…
AgenticIQA: An Agentic Framework for Adaptive and Interpretable Image Quality Assessment
Hanwei Zhu, Yu Tian, Keyan Ding +4
Image quality assessment (IQA) is inherently complex, as it reflects both the quantification and interpretation of perceptual quality rooted in the human visual system. Conventiona…
AI-generated Image Quality Assessment in Visual Communication
Yu Tian, Yixuan Li, Baoliang Chen +3
Assessing the quality of artificial intelligence-generated images (AIGIs) plays a crucial role in their application in real-world scenarios. However, traditional image quality asse…
Mitigating Perception Bias: A Training-Free Approach to Enhance LMM for Image Quality Assessment
Baoliang Chen, Siyi Pan, Dongxu Wu +4
Despite the impressive performance of large multimodal models (LMMs) in high-level visual tasks, their capacity for image quality assessment (IQA) remains limited. One main reason…
RCNet: Deep Recurrent Collaborative Network for Multi-View Low-Light Image Enhancement
Hao Luo, Baoliang Chen, Lingyu Zhu +2
Scene observation from multiple perspectives would bring a more comprehensive visual experience. However, in the context of acquiring multiple views in the dark, the highly correla…
Unrolled Decomposed Unpaired Learning for Controllable Low-Light Video Enhancement
Lingyu Zhu, Wenhan Yang, Baoliang Chen +4
Obtaining pairs of low/normal-light videos, with motions, is more challenging than still images, which raises technical issues and poses the technical route of unpaired learning as…
Sliced Maximal Information Coefficient: A Training-Free Approach for Image Quality Assessment Enhancement
Kang Xiao, Xu Wang, Yulin He +2
Full-reference image quality assessment (FR-IQA) models generally operate by measuring the visual differences between a degraded image and its reference. However, existing FR-IQA m…
Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare
Hanwei Zhu, Haoning Wu, Yixuan Li +7
While recent advancements in large multimodal models (LMMs) have significantly improved their abilities in image quality assessment (IQA) relying on absolute quality rating, how to…
2AFC Prompting of Large Multimodal Models for Image Quality Assessment
Hanwei Zhu, Xiangjie Sui, Baoliang Chen +4
While abundant research has been conducted on improving high-level visual understanding and reasoning capabilities of large multimodal models~(LMMs), their visual quality assessmen…
Perceptual Quality Assessment of Face Video Compression: A Benchmark and An Effective Method
Yixuan Li, Bolin Chen, Baoliang Chen +3
Recent years have witnessed an exponential increase in the demand for face video compression, and the success of artificial intelligence has expanded the boundaries beyond traditio…
Gap-closing Matters: Perceptual Quality Evaluation and Optimization of Low-Light Image Enhancement
Baoliang Chen, Lingyu Zhu, Hanwei Zhu +3
There is a growing consensus in the research community that the optimization of low-light image enhancement approaches should be guided by the visual quality perceived by end users…
DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator
Hanwei Zhu, Baoliang Chen, Lingyu Zhu +2
ImageNet pre-trained deep neural networks (DNNs) show notable transferability for building effective image quality assessment (IQA) models. Such a remarkable byproduct has often be…
Learning from Mixed Datasets: A Monotonic Image Quality Assessment Model
Zhaopeng Feng, Keyang Zhang, Shuyue Jia +2
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. To account for…
Just Noticeable Difference Modeling for Face Recognition System
Yu Tian, Zhangkai Ni, Baoliang Chen +4
High-quality face images are required to guarantee the stability and reliability of automatic face recognition (FR) systems in surveillance and security scenarios. However, a massi…
Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment
Baoliang Chen, Hanwei Zhu, Lingyu Zhu +2
The statistical regularities of natural images, referred to as natural scene statistics, play an important role in no-reference image quality assessment. However, it has been widel…
DeepWSD: Projecting Degradations in Perceptual Space to Wasserstein Distance in Deep Feature Space
Xingran Liao, Baoliang Chen, Hanwei Zhu +3
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…
The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement
Danni Huang, Lingyu Zhu, Zihao Lin +3
There is an increasing consensus that the design and optimization of low light image enhancement methods need to be fully driven by perceptual quality. With numerous approaches pro…
Generalized Visual Quality Assessment of GAN-Generated Face Images
Yu Tian, Zhangkai Ni, Baoliang Chen +3
Recent years have witnessed the dramatically increased interest in face generation with generative adversarial networks (GANs). A number of successful GAN algorithms have been deve…
No-Reference Image Quality Assessment by Hallucinating Pristine Features
Baoliang Chen, Lingyu Zhu, Chenqi Kong +3
In this paper, we propose a no-reference (NR) image quality assessment (IQA) method via feature level pseudo-reference (PR) hallucination. The proposed quality assessment framework…
Detect and Locate: Exposing Face Manipulation by Semantic- and Noise-level Telltales
Chenqi Kong, Baoliang Chen, Haoliang Li +3
The technological advancements of deep learning have enabled sophisticated face manipulation schemes, raising severe trust issues and security concerns in modern society. Generally…
Camera Invariant Feature Learning for Generalized Face Anti-spoofing
Baoliang Chen, Wenhan Yang, Haoliang Li +2
There has been an increasing consensus in learning based face anti-spoofing that the divergence in terms of camera models is causing a large domain gap in real application scenario…
Learning Generalized Spatial-Temporal Deep Feature Representation for No-Reference Video Quality Assessment
Baoliang Chen, Lingyu Zhu, Guo Li +2
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 pred…
No-reference Screen Content Image Quality Assessment with Unsupervised Domain Adaptation
Baoliang Chen, Haoliang Li, Hongfei Fan +1
In this paper, we quest the capability of transferring the quality of natural scene images to the images that are not acquired by optical cameras (e.g., screen content images, SCIs…