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No-Reference Stereoscopic image Quality Assessment Based on Convolutional Neural Network with A Long-Term Feature Fusion

No-Reference Stereoscopic Image Quality Assessment Based on ...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Li, Sumei Wang, Mingyi Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728180687

With the rapid development of three-dimensional (3D) technology, the effective stereoscopic image quality assessment (SIQA) methods are in great demand. Stereoscopic image contains depth information, making it much more challenging in exploring a reliable SIQA model that fits human visual system. In this paper, a no-reference SIQA method is proposed, which better simulates binocular fusion and binocular rivalry. The proposed method applies convolutional neural network to build a dual-channel model and achieve a long-term process of feature extraction, fusion, and processing. What's more, both high and low frequency information are used effectively. Experimental results demonstrate that the proposed model outperforms the state-of-the-art no-reference SIQA methods and has a promising generalization ability.

关键词： stereoscopic image quality assessment (SIQA) convolution neural network (CNN) human visual system (HVS) binocular fusion

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Dynamic Mesh Commonality Modeling Using the Cuboidal Partitioning

Dynamic Mesh Commonality Modeling Using the Cuboidal Partiti...

引用

IEEE visual communications and image processing (VCIP)

作者： Ashek Ahmmed Manoranjan Paul Manzur Murshed Mark Pickering School of Engineering and Information Technology University of New South Wales Australia School of Computing and Mathematics Charles Sturt University Australia School of Engineering Information Technology and Physical Sciences Federation University Australia

ISBN: (纸本)9781665475938

For 3D object representation, volumetric contents like meshes and point clouds provide suitable formats. However, a dynamic mesh sequence may require significantly large amount of data because it consists of information that varies with time. Hence, for the facilitation of storage and transmission of such content, efficient compression technologies are required. MPEG has started standardization activities aiming to develop a mesh compression standard that would be able to handle dynamic meshes with time varying connectivity information and time varying attribute maps. The attribute maps are features associated with the mesh surface and stored as 2D images/videos. In this paper, we propose to capture the commonality information in the dynamic mesh attribute maps using the cuboidal partitioning algorithm. This algorithm is capable of modeling both the global and local commonality within an image in a compact and computationally efficient way. Experimental results show that the proposed approach can outperform the anchor HEVC codec, suggested by MPEG to encode such sequences, with a bit rate savings of up to 3.66%.

关键词： image coding Three-dimensional displays Codecs visual communication Heuristic algorithms Computational modeling Bit rate

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NIR image colorization with graph-convolutional neural networks

NIR image colorization with graph-convolutional neural netwo...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Valsesia, Diego Fracastoro, Giulia Magli, Enrico Politecn Torino Turin Italy

ISBN: (纸本)9781728180687

Colorization of near-infrared (NIR) images is a challenging problem due to the different material properties at the infared wavelenghts, thus reducing the correlation with visible images. In this paper, we study how graph-convolutional neural networks allow exploiting a more powerful inductive bias than standard CNNs, in the form of non-local self-similiarity. Its impact is evaluated by showing how training with mean squared error only as loss leads to poor results with a standard CNN, while the graph-convolutional network produces significantly sharper and more realistic colorizations.

关键词： NIR colorization graph neural network

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Flocking Birds of a Feather Together: Dual-step GAN Distillation via Realer-Fake Samples

Flocking Birds of a Feather Together: Dual-step GAN Distilla...

引用

IEEE visual communications and image processing (VCIP)

作者： Jingwen Ye Zunlei Feng Xinchao Wang National University of Singapore Zhejiang University

ISBN: (纸本)9781665475938

Recent Generative Adversarial Networks (GAN) models deliver visually authentic images and find their applications in various applications. Due to their heavy size, however, it remains cumbersome to deploy state-of-the-art GAN on the edge side, such as mobile terminals. In this paper, we propose a practical and very effective approach for distilling GAN models, so as to produce competent student models that come with smaller sizes. Unlike other convolutional neural network models whose final output spans a small or reasonably-sized space, based on which knowledge distillation is carried out, GAN produces images as output that span an enormous space, making the distillation much more challenging. To this end, we propose an effective dual-step distillation approach tailored for GAN. Instead of directly enforcing the student output distribution to imitate that of the teacher, we partition the generated images into two sets, the “realer” one and the “fake” one, based on the image quality, and sequentially minimize the discrepancy first between fake images of the student and the teacher, and then between realer ones. This seemingly simple strategy turns out highly effective in terms of exploring output space structure and further the knowledge transfer. Experimental results have shown that the proposed method can be easily generalized and demonstrate the learned portable generator produces competitive or even better results both in quality and quantity.

关键词： Training Knowledge engineering image quality visual communication image processing image edge detection Generative adversarial networks

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Center-free intuitionistic fuzzy c-means clustering algorithm based on similarity of hybrid spatial membership for image segmentation

Center-free intuitionistic fuzzy c-means clustering algorith...

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Natural Language processing (ICNLP), International conference on

作者： Lan Rong Wang Shumin He Hu Zhao Feng Yu Haiyan Zhang Lu School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China

In order to address the issue that the center-free fuzzy c-means (CFFCM) clustering algorithm does not consider the texture features and spatial information of pixels, and the time complexity is too high, a center-free intuitionistic fuzzy c-means clustering algorithm based on similarity of hybrid spatial membership for image segmentation is proposed. In the proposed algorithm, the voting model is used to generate intuitionistic fuzzy sets (IFS), and the generated hesitation degree and membership degree are combined with spatial information to design a spatial intuitionistic membership degree similarity model. This model can deal with the similarity between pixels and classes in gray information, so the segmentation efficiency is improved. At the same time, the intuitionistic fuzzy local binary pattern (IFLBP) operator is used to extract the image texture information and introduce it into the objective function. Spatial membership similarity model is used to process texture information and improve the segmentation accuracy of the algorithm. The results of simulation experiment show that the proposed has advantages in both visual effect and evaluation index.

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Exploring image-Based Approaches for Effective Synthetic Time-Series Data Generation

Exploring Image-Based Approaches for Effective Synthetic Tim...

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7th International conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Oh, Samgwon Oh, Seungmin Lee, Junghoon Kim, Doyeon Hahn, Minsoo Kim, Jinsul Chonnam National University Department of Intelligence Electronics Computer Engineering Gwangju Korea Republic of Chonnam National University Department of Intelligence Information Convergence Gwangju Korea Republic of Astana IT University Department of Computational and Data Science Astana Kazakhstan

ISBN: (纸本)9798331507022

The effective generation of synthetic time-series data is crucial for addressing data scarcity in various domains such as network monitoring, financial analysis, and healthcare. This study explores image-based approaches for transforming time-series data into visual representations, enabling the application of advanced image synthesis techniques. We systematically evaluate multiple transformation methods, including recurrence plots, Gramian Angular Fields (GAF), and Markov transition fields, to identify the most suitable techniques for preserving temporal and structural characteristics. The generated synthetic data are validated using statistical similarity metrics and tested on machine learning tasks, such as anomaly detection and forecasting, to assess their practical utility. Experimental results demonstrate that image-based synthetic data generation not only retains essential time-series features but also enhances model robustness under limited data conditions. This research contributes to the growing field of synthetic data generation by providing insights into the integration of time-series and image processing techniques. Future work will focus on refining these methods and exploring their scalability across diverse applications. © 2025 IEEE.

关键词： Photointerpretation

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UGNet: Underexposed images Enhancement Network based on Global Illumination Estimation

UGNet: Underexposed Images Enhancement Network based on Glob...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Fang, Yuan Zhu, Wenzhe Zhu, Qing Beijing Univ Technol Sch Software Engn Beijing Peoples R China

ISBN: (纸本)9781728180687

This paper proposes a new neural network for enhancing underexposed images. Instead of the decomposition method based on Retinex theory, we introduce smooth dilated convolution to estimate global illumination of the input image, and implement an end-to-end learning network model. Based on this model, we formulate a multi-term loss function that combines content, color, texture and smoothness losses. Our extensive experiments demonstrate that this method is superior to other methods in underexposed image enhancement. It can cover more color details and be applied to various underexposed images robustly.

关键词： underexposed enhancement global illumination estimation image restoration smooth dilated convolution

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Adaptive Kernelized Evidence C-Means Clustering Combining Spatial Information for Noisy image Segmentation

Adaptive Kernelized Evidence C-Means Clustering Combining Sp...

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Natural Language processing (ICNLP), International conference on

作者： Lan Rong Mi Haowen Qu Na Zhao Feng Yu Haiyan Zhang Lu School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China

Although the evidence c-means clustering (ECM) has the capability to process uncertain information, it is not suitable for noisy image segmentation, because the spatial information of pixels is not considered. To solve the problem, an adaptive kernelized evidence c-means clustering combining spatial information for noisy image segmentation algorithm is proposed. Firstly, an adaptive noise distance that can be iteratively updated is constructed using the local information of the pixels. Secondly, to improve the classification performance, an adaptive kernel function is proposed to measure the distance between the pixel and the cluster center. Simultaneously, the original, local and non-local information of pixels are introduced adaptively into the objective function to enhance the robustness to noise. In the iteration, the noise cluster is automatically recovered using the recovery factor constructed by the gray and spatial information of neighborhood pixels. Finally, the credal partition is transformed into a fuzzy partition by pignistic transformation, the classification of pixel be determined by the maximum membership principle. Experiments on synthetic images and real images demonstrate that the proposed algorithm has strong noise suppression ability. visual effects and evaluation indexes verify the effectiveness of the proposed algorithm for noisy image segmentation.

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Annotating Only at Definite Pixels: A Novel Weakly Supervised Semantic Segmentation Method for Sea Fog Recognition

Annotating Only at Definite Pixels: A Novel Weakly Supervise...

引用

IEEE visual communications and image processing (VCIP)

作者： Xun Zhu Mengqiu Xu Ming Wu Chuang Zhang Bin Zhang Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665475938

Sea fog recognition is a challenging and significant semantic segmentation task in remote sensing images. The fully supervised learning method relies on the pixel-level label, which is labor-intensive and time-consuming. Moreover, it is impossible to accurately annotate all pixels of the sea fog region due to the limited ability of the human eye to distinguish between low clouds and sea fog. In this paper, we propose a novel approach of point-based annotation for weakly supervised semantic segmentation with the auxiliary information of International Comprehensive Ocean-Atmosphere Data Set (ICOADS) visibility data. It only needs several definite points for both foreground and background, which significantly reduces the annotation cost of manpower. We conduct extensive experiments on Himawari-8 satellite remote sensing images to demonstrate the effectiveness of our annotation method. The mean intersection over union (mIoU) and overall recognition accuracy of our annotation method reach 82.72% and 95.18 %, respectively. Compared with the fully supervised learning method, the accuracy and the recognition rate of sea fog area are improved with a maximum increase of 7.69% and 9.69 %, respectively.

关键词： image recognition Satellites Costs Annotations visual communication Semantic segmentation Supervised learning

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Deep Reference Frame Interpolation based Inter Prediction Enhancement for Versatile Video Coding

Deep Reference Frame Interpolation based Inter Prediction En...

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IEEE visual communications and image processing (VCIP)

作者： Jianghao Jia Zizheng Liu Xiaozhong Xu Shan Liu Zhenzhong Chen School of Remote Sensing and Information Engineering Wuhan University Wuhan China Tencent Media Lab Shenzhen China Tencent Media Lab Palo Alto CA USA

ISBN: (纸本)9781665475938

In video coding, bi-directional inter prediction aims to remove temporal redundancy via two-sided previously coded frames as reference. High-quality reference frames are essential to reduce the prediction residuals and improve coding efficiency performance. In this paper, we propose a deep learning-based reference frame interpolation method to enhance bi-prediction by introducing a synthetic frame to reference picture lists. Specifically, reconstructed frames are fed into a well-designed interpolation and filtering network to synthesize a picture which can be regarded as an additional reference of to-be-coded frame. Then, the picture is inserted at the appropriate place in reference picture lists to provide a more reliable reference for subsequent motion estimation and motion compensation. Experimental results show that the proposed method achieves 2.03%/6.96%/6.40% coding efficiency improvements for Y/U/V components under random access configuration, when compared with the VVC reference software VTM-15.0.

关键词： Video coding Interpolation image coding Filtering visual communication Redundancy Bidirectional control

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