Hierarchical Graph Attention Network for No-Reference Omnidirectional Image Quality Assessment

• URL: http://arxiv.org/abs/2508.09843v1
• Date: Wed, 13 Aug 2025 14:25:24 GMT
• Title: Hierarchical Graph Attention Network for No-Reference Omnidirectional Image Quality Assessment
• Authors: Hao Yang, Xu Zhang, Jiaqi Ma, Linwei Zhu, Yun Zhang, Huan Zhang,
• Abstract summary: Current Omnidirectional Image Quality Assessment (OIQA) methods struggle to evaluate locally non-uniform distortions.<n>We propose a graph neural network-based OIQA framework that explicitly models structural relationships between viewports.
• Score: 21.897948374713163
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current Omnidirectional Image Quality Assessment (OIQA) methods struggle to evaluate locally non-uniform distortions due to inadequate modeling of spatial variations in quality and ineffective feature representation capturing both local details and global context. To address this, we propose a graph neural network-based OIQA framework that explicitly models structural relationships between viewports to enhance perception of spatial distortion non-uniformity. Our approach employs Fibonacci sphere sampling to generate viewports with well-structured topology, representing each as a graph node. Multi-stage feature extraction networks then derive high-dimensional node representation. To holistically capture spatial dependencies, we integrate a Graph Attention Network (GAT) modeling fine-grained local distortion variations among adjacent viewports, and a graph transformer capturing long-range quality interactions across distant regions. Extensive experiments on two large-scale OIQA databases with complex spatial distortions demonstrate that our method significantly outperforms existing approaches, confirming its effectiveness and strong generalization capability.

Related papers

• Hierarchical Graph Feature Enhancement with Adaptive Frequency Modulation for Visual Recognition [6.580655899524989]
  Convolutional neural networks (CNNs) have demonstrated strong performance in visual recognition tasks.<n>We propose a novel framework that integrates graph-based rea soning into CNNs to enhance both structural awareness and feature representation.<n>The proposed HGFE module is lightweight, end-to-end trainable, and can be seamlessly integrated into standard CNN backbone networks.
  arXiv  Detail & Related papers  (2025-08-15T14:19:50Z)
• GCRPNet: Graph-Enhanced Contextual and Regional Perception Network for Salient Object Detection in Optical Remote Sensing Images [68.33481681452675]
  We propose a graph-enhanced contextual and regional perception network (GCRPNet)<n>It builds upon the Mamba architecture to simultaneously capture long-range dependencies and enhance regional feature representation.<n>It performs adaptive patch scanning on feature maps processed via multi-scale convolutions, thereby capturing rich local region information.
  arXiv  Detail & Related papers  (2025-08-14T11:31:43Z)
• Graph Neural Networks for Jamming Source Localization [0.23408308015481666]
  We introduce the first application of graph-based learning for jamming source localization.<n>Our approach integrates structured node representations that encode local and global signal aggregation.<n>Results demonstrate that our novel graph-based learning framework significantly outperforms established localization baselines.
  arXiv  Detail & Related papers  (2025-06-01T14:29:25Z)
• Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation [11.751952500567388]
  This study focuses on the challenge of predicting network traffic within complex topological environments.<n>It introduces a Graph Contemporalal Networks (GCN) with Gated Recurrent Units (GRU) modeling approach.<n>The effectiveness of the proposed model is validated through comprehensive experiments on the real-world Abilene network traffic dataset.
  arXiv  Detail & Related papers  (2025-05-12T15:38:19Z)
• Brain Network Classification Based on Graph Contrastive Learning and Graph Transformer [0.6906005491572401]
  This paper proposes a novel model named PHGCL-DDGformer that integrates graph contrastive learning with graph transformers.<n> Experimental results on real-world datasets demonstrate that the PHGCL-DDGformer model outperforms existing state-of-the-art approaches in brain network classification tasks.
  arXiv  Detail & Related papers  (2025-04-01T13:26:03Z)
• Depth-Adaptive Graph Neural Networks via Learnable Bakry-'Emery Curvature [7.2716257100195385]
  Graph Neural Networks (GNNs) have demonstrated strong representation learning capabilities for graph-based tasks.<n>Recent advances on GNNs leverage geometric properties, such as curvature, to enhance its representation capabilities.<n>We propose integrating Bakry-'Emery curvature, which captures both structural and task-driven aspects of information propagation.
  arXiv  Detail & Related papers  (2025-03-03T00:48:41Z)
• TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation [1.2186950360560143]
  We introduce an attentional cross-scale graph neural network (ACS-GNN) to enhance skip connection framework.<n>ACS-GNN converts cross-scale feature maps into a graph structure and captures complex anatomical structures through node attention.<n>Our framework, TransGUNet, comprises ACS-GNN and EFS-based spatial attentio to enhance domain generalizability across various modalities.
  arXiv  Detail & Related papers  (2025-02-14T05:54:13Z)
• Spatiotemporal Graph Learning with Direct Volumetric Information Passing and Feature Enhancement [62.91536661584656]
  We propose a dual-module framework, Cell-embedded and Feature-enhanced Graph Neural Network (aka, CeFeGNN) for learning.<n>We embed learnable cell attributions to the common node-edge message passing process, which better captures the spatial dependency of regional features.<n>Experiments on various PDE systems and one real-world dataset demonstrate that CeFeGNN achieves superior performance compared with other baselines.
  arXiv  Detail & Related papers  (2024-09-26T16:22:08Z)
• T-GAE: Transferable Graph Autoencoder for Network Alignment [79.89704126746204]
  T-GAE is a graph autoencoder framework that leverages transferability and stability of GNNs to achieve efficient network alignment without retraining. Our experiments demonstrate that T-GAE outperforms the state-of-the-art optimization method and the best GNN approach by up to 38.7% and 50.8%, respectively.
  arXiv  Detail & Related papers  (2023-10-05T02:58:29Z)
• Mixed Graph Contrastive Network for Semi-Supervised Node Classification [63.924129159538076]
  We propose a novel graph contrastive learning method, termed Mixed Graph Contrastive Network (MGCN)<n>In our method, we improve the discriminative capability of the latent embeddings by an unperturbed augmentation strategy and a correlation reduction mechanism.<n>By combining the two settings, we extract rich supervision information from both the abundant nodes and the rare yet valuable labeled nodes for discriminative representation learning.
  arXiv  Detail & Related papers  (2022-06-06T14:26:34Z)
• Data-heterogeneity-aware Mixing for Decentralized Learning [63.83913592085953]
  We characterize the dependence of convergence on the relationship between the mixing weights of the graph and the data heterogeneity across nodes. We propose a metric that quantifies the ability of a graph to mix the current gradients. Motivated by our analysis, we propose an approach that periodically and efficiently optimize the metric.
  arXiv  Detail & Related papers  (2022-04-13T15:54:35Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)
• Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification [63.56018768401328]
  We propose a Multi-level Graph Convolutional Network (GCN) with Automatic Graph Learning method (MGCN-AGL) for HSI classification. By employing attention mechanism to characterize the importance among spatially neighboring regions, the most relevant information can be adaptively incorporated to make decisions. Our MGCN-AGL encodes the long range dependencies among image regions based on the expressive representations that have been produced at local level.
  arXiv  Detail & Related papers  (2020-09-19T09:26:20Z)
• Adversarial Bipartite Graph Learning for Video Domain Adaptation [50.68420708387015]
  Domain adaptation techniques, which focus on adapting models between distributionally different domains, are rarely explored in the video recognition area. Recent works on visual domain adaptation which leverage adversarial learning to unify the source and target video representations are not highly effective on the videos. This paper proposes an Adversarial Bipartite Graph (ABG) learning framework which directly models the source-target interactions.
  arXiv  Detail & Related papers  (2020-07-31T03:48:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.