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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.
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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