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Works109 from public data
- Image Quality Assessment: Unifying Structure and Texture Similarity1,590
This work develops the first full-reference image quality model with explicit tolerance to texture resampling, using a convolutional neural network to construct an injective and differentiable function that transforms images to multi-scale overcomplete representations.
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- Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network1,013
A deep bilinear model for blind image quality assessment that works for both synthetically and authentically distorted images and achieves state-of-the-art performance on both synthetic and authentic IQA databases is proposed.
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- Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective465
This work develops a general and automated multitask learning scheme for BIQA to exploit auxiliary knowledge from other tasks, in a way that the model parameter sharing and the loss weighting are determined automatically.
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- Reversibility improved data hiding in encrypted images411
A novel reversible data hiding technique in encrypted images where some pixels are estimated before encryption so that additional data can be embedded in the estimating errors and the data extraction and image recovery are free of errors.
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- Robust Multi-Exposure Image Fusion: A Structural Patch Decomposition Approach355
The proposed algorithm not only outperforms previous MEF algorithms on static scenes but also consistently produces high quality fused images with little ghosting artifacts for dynamic scenes and maintains a lower computational cost compared with the state-of-the-art deghosting schemes.
- Unified Blind Quality Assessment of Compressed Natural, Graphic, and Screen Content Images337
A unified content-type adaptive (UCA) blind image quality assessment model that is applicable across content types and leads to superior performance on the constructed CCT database, and is training-free, implying strong generalizability.
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- Continual Learning for Blind Image Quality Assessment126
This paper formulates continual learning for BIQA, where a model learns continually from a stream of IQA datasets, building on what was learned from previously seen data, and proposes a simple yet effective continual learning method.
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- Locally Adaptive Structure and Texture Similarity for Image Quality Assessment76
A locally adaptive structure and texture similarity index for full-reference IQA, which is term A-DISTS, which relies on a single statistical feature, namely the dispersion index, to localize texture regions at different scales.
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- Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models62
A first-of-its-kind computational analysis of VQA datasets via designing minimalistic BVQA models that cast doubt on the current progress in BVQA, and shed light on good practices of constructing next-generation VQA datasets and models.
- Learning To Blindly Assess Image Quality In The Laboratory And Wild62
A BIQA model and an approach of training it on multiple IQA databases (of different distortion scenarios) simultaneously are developed, demonstrating that the optimized model by the proposed training strategy is effective in blindly assessing image quality in the laboratory and wild, outperforming previous BIZA methods by a large margin.
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- Modular Blind Video Quality Assessment50
A modular BVQA model that comprises a base quality predictor, a spatial rectifier, and a temporal rectifier, responding to the visual content and distortion, spatial resolution, and frame rate changes on video quality, respectively is proposed.
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- Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessment43
This work first pre-train a DNN-based BIQA model using multiple noisy annotators, and fine-tune it on multiple synthetically distorted images, resulting in a “top-performing” baseline model, which is then fine-tuned on the combination of human-rated images from gMAD and existing databases.
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- Quality-of-Experience of Adaptive Video Streaming38
A new adaptive streaming video database is built and a series of subjective experiments are carried out to understand human QoE behaviors in this multi-dimensional adaptation space, suggesting an interesting Weber's law effect.
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- Troubleshooting Blind Image Quality Models in the Wild30
Inspired by recent findings that difficult samples of deep models may be exposed through network pruning, a set of "self-competitors" is constructed, as random ensembles of pruned versions of the target model to be improved.
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- Measuring Perceptual Color Differences of Smartphone Photographs26
This article puts together so far the largest image dataset for perceptual CD assessment, and makes one of the first attempts to construct an end-to-end learnable CD formula based on a lightweight neural network, as a generalization of several previous metrics.
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- Image Quality Assessment in the Modern Age25
This tutorial provides the audience with the basic theories, methodologies, and current progresses of image quality assessment (IQA), and presents in detail the design principles of objective quality assessment models, supplemented by an in-depth analysis of their advantages and disadvantages.
- Exposing Semantic Segmentation Failures via Maximum Discrepancy Competition23
This paper exposes failures of existing semantic segmentation methods in the open visual world under the constraint of very limited human labeling effort, and conducts a thorough MAD diagnosis of ten PASCAL VOC semantic segmentsation algorithms.
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- Perceptual Quality Assessment of Virtual Reality Videos in the Wild16
The VR Video Quality in the Wild (VRVQW) database is constructed, containing 502 user-generated videos with diverse content and distortion characteristics, and an objective quality assessment model for VR videos based on pseudocylindrical representation and convolution is developed.
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- Semantics-Oriented Multitask Learning for DeepFake Detection: A Joint Embedding Approach15
This paper proposes an automated dataset expansion technique that broadens current face forgery datasets to support semantics-oriented DeepFake detection tasks at both the global face attribute and local face region levels, and resorts to the joint embedding of face images and labels for prediction.
- Semantic Contextualization of Face Forgery: A New Definition, Dataset, and Detection Method14
A semantics-oriented face forgery detection method that captures label relations and prioritizes the primary task (i.e., real or fake face detection) is proposed and shown to be superior over traditional binary and multi-class classification-based detectors.
- Debiased Subjective Assessment of Real-World Image Enhancement14
This work takes steps towards debiasing conventional subjective assessment by automatically sampling a set of adaptive and diverse images for subsequent testing, and casting sample selection into a joint maximization of the discrepancy between the enhancers and the diversity among the selected input images.
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- Self-Supervised AI-Generated Image Detection: A Camera Metadata Perspective11
Extensive experiments across various generative models demonstrate that the EXIF-induced detectors substantially advance the state of the art, delivering strong generalization to in-the-wild samples and robustness to common benign image perturbations.
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- Bi-Level Optimization for Self-Supervised AI-Generated Face Detection7
A self-supervised method based on bi-level optimization that significantly outperform existing approaches in both one-class and binary classification settings, exhibiting strong generalization to unseen generators.
- Self-Supervised Learning for Detecting AI-Generated Faces as Anomalies7
An anomaly detection method for AI-generated faces is described by leveraging self-supervised learning of camera-intrinsic and face-specific features purely from photographic face images using a Gaussian mixture model.
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- Troubleshooting image segmentation models with human-in-the-loop7
The feasibility of the framework using the semantic segmentation task in PASCAL VOC is demonstrated, and it is found that the fine-tuned model exhibits significantly improved generalization when applied to real-world images with greater content diversity.
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- A Perceptually Optimized and Self-Calibrated Tone Mapping Operator5
A two-stage neural network-based TMO that is self-calibrated and perceptually optimized that produces images with consistently better visual quality while ranking among the fastest local TMOs.
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- Special Section Guest Editorial: Perceptually Optimized Imaging–
This guest editorial summarizes the Special Section on Perceptually Optimized Imaging and describes the current state of the art in perception-based image-guided medicine.
- Omnidirectional Images as Moving Camera Videos.–
This paper first transforms an omnidirectional image to several video representations using viewing behavior of different users, and then leverage the recent advances in full-reference 2D image/video quality assessment to compute the perceived quality of the panorama.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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