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- GraphIQA: Learning Distortion Graph Representations for Blind Image Quality Assessment185
A Distortion Graph Representation (DGR) learning framework for IQA is proposed, in which each distortion is represented as a graph, i.e., DGR, and one can distinguish distortion types by learning the contrast relationship between these different DGRs, and can infer the ranking distribution of samples from different levels in a DGR.
- Blind Omnidirectional Image Quality Assessment With Viewport Oriented Graph Convolutional Networks162
This paper proposes a novel Viewport oriented Graph Convolution Network (VGCN) for blind omnidirectional image quality assessment (IQA) and demonstrates that the proposed model outperforms state-of-the-art full-reference and no-reference IQA metrics on two public omniddirectional IQA databases.
- Tensor Oriented No-Reference Light Field Image Quality Assessment120
The proposed Tensor-NLFQ model outperforms state-of-the-art 2D, 3D, multi-view, and LFI quality assessment algorithms and measures spatial-dimensional quality of LFI considering its global naturalness and local frequency properties.
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- No-Reference Light Field Image Quality Assessment Based on Spatial-Angular Measurement103
A No-Reference Light Field image Quality Assessment (NR-LFQA) scheme, where the main idea is to quantify the LFI quality degradation through evaluating the spatial quality and angular consistency, and the weighted local binary pattern is proposed to capture the characteristics of local angular consistency degradation.
- No-Reference Quality Assessment for 360-Degree Images by Analysis of Multifrequency Information and Local-Global Naturalness101
This model is the first no-reference quality assessment method for 360-degree images that combines multifrequency information and image naturalness and outperforms state-of-the-art full-reference (FR) and NR approaches.
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- LIQA: Lifelong Blind Image Quality Assessment87
This work proposes a novel Lifelong blind Image Quality Assessment (LIQA) approach, targeting to achieve the lifelong learning of BIQA, and adopts the Split-and-Merge distillation strategy to train a single-head network that makes task-agnostic predictions.
- Unsupervised Single Image Deraining with Self-Supervised Constraints87
An Unsupervised Deraining Generative Adversarial Network (UD-GAN) is proposed to tackle single image deraining problems by introducing self-supervised constraints from the intrinsic statistics of unpaired rainy and clean images.
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- Blind quality assessment for image superresolution using deep two-stream convolutional networks82
Experimental results on three publicly available SR image quality databases demonstrate the effectiveness and generalization ability of the proposed DeepSRQ method compared with state-of-the-art image quality assessment algorithms.
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- Quality Assessment of Image Super-Resolution: Balancing Deterministic and Statistical Fidelity64
An uncertainty weighting scheme based on content-dependent sharpness and texture assessment that merges the two fidelity measures into an overall quality prediction named the Super Resolution Image Fidelity (SRIF) index, which demonstrates superior performance against state-of-the-art IQA models when tested on subject-rated datasets.
- Reduced-Reference Quality Assessment of Point Clouds via Content-Oriented Saliency Projection61
This letter proposes a novel and efficient Reduced-Reference quality metric for point clouds, which is based on Content-oriented sAliency Projection (RR-CAP), and makes the first attempt to simplify reference and distorted point clouds into projected saliency maps with a downsampling operation.
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- Stereoscopic Omnidirectional Image Quality Assessment Based on Predictive Coding Theory54
This article proposes a stereoscopic omnidirectional image quality evaluator (SOIQE) to cope with the characteristics of 3D 360-degree images and is a parametric model without necessary of regression learning, which ensures its interpretability and generalization performance.
- Perceptual Quality Assessment of Internet Videos52
A spatio-temporal distortion-aware model based on the properties of Internet videos originated from Youku, which outperforms the state-of-the-art methods and exhibits promising generalization ability is proposed.
- Adaptive Mixed-Scale Feature Fusion Network for Blind AI-Generated Image Quality Assessment50
A novel blind image quality assessment (IQA) network, named AMFF-Net, for AGIs, inspired by the characteristics of the human visual system and motivated by the observation that “visual quality” and “authenticity” are characterized by both local and global aspects.
- Image Super-Resolution Quality Assessment: Structural Fidelity Versus Statistical Naturalness50
This paper assesses the quality of SISR generated images in a two-dimensional (2D) space of structural fidelity versus statistical naturalness, and finds that a simple linear combination of a straightforward local structural fidelity and a global statisticalNaturalness measures produce surprisingly accurate predictions of S ISR image quality when tested using public subject-rated SISr image datasets.
- Adaptive Hypergraph Convolutional Network for No-Reference 360-degree Image Quality Assessment49
An adaptive hypergraph convolutional network for NR 360IQA, denoted as AHGCN, which first design a multi-level viewport descriptor for extracting hierarchical representations from viewports, and model interactions between viewports through hypergraphs, where each hyperedge connects two or more viewports.
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- Learning Disentangled Feature Representation for Hybrid-Distorted Image Restoration49
This work introduces the concept of Disentangled Feature Learning to achieve the feature-level divide-and-conquer of hybrid distortions and proposes the feature disentanglement module (FDM) to distribute feature representations of different distortions into different channels by revising gain-control-based normalization.
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- Blind Quality Assessment of Dense 3D Point Clouds with Structure Guided Resampling43
The proposed Structure Guided Resampling (SGR) is a general-purpose blind quality assessment method without the assistance of any reference information that can compete with state-of-the-art full-reference, reduced-reference, and no-reference quality assessment algorithms.
- Vision-Language Consistency Guided Multi-Modal Prompt Learning for Blind AI Generated Image Quality Assessment39
This letter introduces learnable textual and visual prompts in language and vision branches of CLIP models, respectively, and designs a text-to-image alignment quality prediction task, whose learned vision-language consistency knowledge is used to guide the optimization of the above multi-modal prompts.
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- No-reference quality assessment of underwater image enhancement36
This paper proposes a highly effective yet accurate no-reference quality assessment method to evaluate UIE results by analyzing the statistics of underwater images, and first extracts quality-aware features in terms of three key aspects: colorfulness; contrast; 3) visibility.
- A Method for the Evaluation of Image Quality According to the Recognition Effectiveness of Objects in the Optical Remote Sensing Image Using Machine Learning Algorithm30
A new IQA method of standardizing the target object recognition rate (ORR) is presented to reflect quality, using a machine learning algorithm to extract regional gray scale features of typical objects in the image for analysis, and quantitatively assessing quality of ORSI according to the difference.
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- A brief survey on adaptive video streaming quality assessment28
It is found that existing video streaming QoE assessment models still have limited performance, which makes it difficult to be applied in practical communication systems, and based on the success of deep learned feature representations for traditional video quality prediction, the off-the-shelf deep convolutional neural network (DCNN) is applied.
- Quality Assessment of Stereoscopic 360-degree Images from Multi-viewports28
A multi-viewport based full-reference stereo 360 IQA model is proposed that achieves a significant improvement over some well-known IQA metrics and can accurately reflect the overall QoE of perceived images.
- Subjective Quality Assessment of Stereoscopic Omnidirectional Image28
This paper establishes the very first Stereoscopic OmnidirectionaL Image quality assessment Database (SOLID), and results demonstrate that the objective overall QoE assessment is more challenging compared to IQA in terms of stereoscopic omnidirectional images.
- LIRA: Lifelong Image Restoration from Unknown Blended Distortions27
Experimental results show that the proposed approach can not only achieve state-of-the-art performance on blended distortions removal tasks in both PSNR/SSIM metrics, but also maintain old expertise while learning new restoration tasks.
- Deep Multi-Scale Features Learning for Distorted Image Quality Assessment26
This model is based on both residual maps and distorted images in luminance domain, where the proposed network contains spatial pyramid pooling and feature pyramid from the network structure, and is optimized in a deep end-to-end supervision manner.
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- No-Reference Light Field Image Quality Assessment Based on Micro-Lens Image24
This paper proposes a No-Reference Light Field image Quality assessment model based on MLI (LF-QMLI), which utilizes Global Entropy Distribution and Uniform Local Binary Pattern descriptor (ULBP) to extract features from the MLI, and pool them together to measure angular consistency.
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- EEmo-Bench: A Benchmark for Multi-modal Large Language Models on Image Evoked Emotion Assessment21
This work introduces EEmo-Bench, a novel benchmark dedicated to the analysis of the evoked emotions in images across diverse content categories and adopts an emotion ranking strategy and employs the Valence-Arousal-Dominance as emotional attributes for emotional assessment.
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- Deep Local and Global Spatiotemporal Feature Aggregation for Blind Video Quality Assessment19
An efficient VQA method named Deep SpatioTemporal video Quality assessor (DeepSTQ) to predict the perceptual quality of various distorted videos in a no-reference manner and outperforms state-of-the-art quality assessment algorithms.
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- Visual Comfort Assessment for Stereoscopic Image Retargeting16
A Visual Comfort Assessment metric for Stereoscopic Image Retargeting (VCA-SIR) is proposed, which introduces novel features like disparity range, boundary disparity as well as disparity intensity distribution into the assessment model.
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- Stereoscopic Video Quality Prediction Based on End-to-End Dual Stream Deep Neural Networks15
A no-reference stereoscopic video quality assessment (NR-SVQA) method based on an end-to-end dual stream deep neural network (DNN), which incorporates left and right view sub-networks, which outperforms state-of-the-art algorithms.
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- A Decomposed Dual-Cross Generative Adversarial Network for Image Rain Removal.9
The proposed Decomposed Dual-Cross Generative Adversarial Network (DDC-GAN) shows performance improvement compared with state-of-the-art methods on both synthetic and real-world images in terms of qualitative and quantitative measures.
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- Exploiting independent query information for few-shot image segmentation7
This work proposes a prior extractor to learn the query information from the unlabeled images with the proposed global–local contrastive learning and generates the prior region maps for query images, which locate the objects, as guidance to perform cross-interaction with support features.
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- Physically-guided open vocabulary segmentation with weighted patched alignment loss6
This work proposes a unified pipeline that leverages physical structure regularization to enhance the generalizability and robustness of open vocabulary segmentation and introduces the weighted patched alignment loss, inspired by the effectiveness of comparative learning in human cognition.
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- Predictive Auto-Encoding Network for Blind Stereoscopic Image Quality Assessment.6
A Predictive Auto-encoDing Network (PAD-Net) for blind/No-Reference stereoscopic image quality assessment (NR-SIQA) is proposed and the Siamese framework is designed to mimic the binocular rivalry in the 3D HVS based on the likelihood and prior maps generated from the predictive coding process.
- None Ghosting Artifacts Stitching Based on Depth Map for Light Field Image6
Compared with state-of-the-art stitching methods aiming to alleviate ghosting artifacts, the proposed None Ghosting Artifacts (NGA) stitching method not only stitches images without ghosted artifacts, but also achieves realistic perspective transformation.
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- From Global to Granular: Revealing IQA Model Performance via Correlation Surface5
Granularity-Modulated Correlation (GMC) is proposed, which provides a structured, fine-grained analysis of IQA performance and maps correlation values as a joint function of MOS and <inline-formula><tex-math notation="LaTeX">$|\Delta$</tex-math><alternatives><mml:mrow><mml:mo>|</mml:mo>
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- Augmented Coarse-to-Fine Video Frame Synthesis with Semantic Loss5
This paper introduces augmented inputs to compensate texture details and motion information and presents a coarse-to-fine guidance scheme with a well-designed semantic loss to improve the capability of video frame synthesis.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; 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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