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检索条件"任意字段=Conference on Visual Communications and Image Processing"

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Infrared image Noise Reduction and Edge Enhancement Algorithm Based on Local Intuitionistic Fuzzy Entropy 2

Infrared Image Noise Reduction and Edge Enhancement Algorith...

引用

2nd International conference on Networking, communications and Information Technology, NetCIT 2022

作者： Niu, Yihan Lanzhou Resources&Environment Voc-Tech University Gansu Lanzhou730021 China

ISBN: (纸本)9781665492737

Aiming at the deficiency that the traditional P-M diffusion model can't distinguish the flat area and the detailed area with similar gradient changes, a new diffusion model is proposed. In image engineering, the actual image is flawed. On the one hand, due to the imperfection of image acquisition equipment, transmission equipment and receiving equipment, the image will be disturbed by various noises in the process of acquisition, transmission and storage, and the image will become blurred, resulting in incomplete image information. On the other hand, due to the limitation of various image processing algorithms, some negative effects will be brought. Firstly, the optimal threshold of infrared image is obtained by using the fuzzy maximum straightness criterion. On this basis, three spatial domain enhancement algorithms are proposed, including nonlinear stretching algorithm, improved plateau histogram equalization algorithm and double closed-value adaptive piecewise linear transformation algorithm for the target and background area. Experiments show that their processing effects have their own advantages. Then, with the help of wavelet theory, this paper proposes an infrared image contrast enhancement algorithm based on fuzzy logic and wavelet transform in the frequency domain. The algorithm can not only significantly improve the contrast of the image, but also have a better noise reduction effect. The experimental results show that, compared with the traditional model, the new model keeps the details more completely, removes the noise more cleanly, and has excellent visual and quantitative effects. © 2022 IEEE.

关键词： Wavelet transforms

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Quadrature Color Vibration Method for Improving Robustness of Imperceptible Markers Embedded in Video

Quadrature Color Vibration Method for Improving Robustness o...

引用

IEEE visual communications and image processing (VCIP)

作者： Yuta Kibayashi Kota Araki Yasunori Akashi Changyo Han Takeshi Naemura The Univ. of Tokyo Tokyo Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

The imperceptible color vibration method, which embeds imperceptible patterns in display images through rapid chromatic changes, enables the presentation of information to machines while maintaining the viewing experience of users. However, when markers are embedded in video images, decoding becomes challenging during periods with significant interframe differences, which adds noise to the decoding. In this study, we enhanced the signal-point distance by embedding two quadrature chromatic changes within the color space across the entire image. Additionally, we employed soft-decision decoding, which involves accumulating multiple confidence values per pixel, thereby enabling robust decoding during periods with significant interframe differences in video images. Our evaluation confirmed that the proposed method significantly improves the decoding rate compared to conventional techniques. Furthermore, we measured the time required for decoding and verified the effectiveness of the proposed soft-decision decoding method.

关键词： Vibrations image coding image color analysis visual communication Cameras Time measurement Robustness Decoding Colored noise

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MTE: Learned image Compression with a Merge-Then-Estimate Entropy Model

MTE: Learned Image Compression with a Merge-Then-Estimate En...

引用

IEEE visual communications and image processing (VCIP)

作者： Liu Shizhan Shanghai Jiao Tong University China

Recent advancements in learning-based image compression methods have shown promising results. The success of these methods heavily relies on the entropy model, which predicts the probability distribution of the quantized latent representation of the image based on available knowledge. However, most existing entropy models follow an estimate-then-merge pipeline, leading to two potential issues: limited flexibility in modeling spatial context and inadequate fusion of different prior sources. In this paper, we propose a novel approach called the MergeThen-Estimate (MTE) entropy model. Our method addresses these issues by first uniformly merging available priors into a ’prior token’ using a Prior Embedding Module for each spatial location in the quantized latent representation. Next, we introduce a Content-aware Context Model to dynamically capture the dependencies of the currently coding representation on its neighboring available priors. Experiments on the Kodak dataset demonstrate the superiority of our proposed MTE entropy model.

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Perceptual image Compression With Conditional Diffusion Transformers

Perceptual Image Compression With Conditional Diffusion Tran...

引用

IEEE visual communications and image processing (VCIP)

作者： Rui Mao Xinmin Feng Changsheng Gao Li Li Dong Liu Xiaoyan Sun MOE Key Laboratory of Brain-Inspired Intelligent Perception and Cognition University of Science and Technology of China Hefei China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Generative models have significantly advanced generative AI, particularly in image and video generation. Recognizing their potential, researchers have begun exploring their application in image compression. However, existing methods face two primary challenges: limited performance improvement and high model complexity. In this paper, to address these two challenges, we propose a perceptual image compression solution by introducing a conditional diffusion model. Given that compression performance heavily depends on the decoder’s generative capability, we base our decoder on the diffusion transformer architecture. To address the model complexity problem, we implement the diffusion transformer architecture with Swin transformer. Equipped with enhanced generative capability, we further augment the decoder with informative features using a multi-scale feature fusion module. Experimental results demonstrate that our approach surpasses existing perceptual image compression methods while achieving lower model complexity.

关键词： image coding image recognition visual communication Generative AI Face recognition Transformers Diffusion models Complexity theory Decoding Iterative decoding

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Learned image Compression with Quantization Error Compensator

Learned Image Compression with Quantization Error Compensato...

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IEEE visual communications and image processing (VCIP)

作者： Nianxiang Fu Zhenzhong Chen Huairui Wang Shan Liu Hubei Luojia Laboratory Wuhan University Wuhan China School of Remote Sensing and Information Engineering Wuhan University Wuhan China Tencent Shenzhen China Tencent America Palo Alto CA USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Recent advancements in learned image compression methods have demonstrated superior rate-distortion performance and remarkable potential compared to traditional compression techniques. However, the core operation of quantization, inherent to lossy image compression, introduces errors that can degrade the quality of the reconstructed image. To address this challenge, we propose a novel Quantization Error Compensator (QEC), which leverages spatial context within latent representations and hyperprior information to effectively mitigate the impact of quantization error. Moreover, we propose a tailored quantization error optimization training strategy to further improve rate-distortion performance. Notably, QEC serves as a lightweight, plug-and-play module, offering high flexibility and seamless integration into various learned image compression methods. Extensive experimental results consistently demonstrate significant coding efficiency improvements achievable by incorporating the proposed QEC into state-of-the-art methods, with a slight increase in runtime.

关键词： Training image coding Quantization (signal) Runtime Codecs visual communication Rate-distortion Distortion Optimization image reconstruction

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Leveraging image Gradients for Robust GAN-Generated image Detection in OSN context

Leveraging Image Gradients for Robust GAN-Generated Image De...

引用

IEEE visual communications and image processing (VCIP)

作者： Tanusree Ghosh Ruchira Naskar Department of Information Technology Indian Institute of Engineering Science and Technology

Creating hyper-realistic synthetic images has become effortless with tremendous development in Generative Artificial Intelligence technologies. Generative Adversarial Networks (GAN) generated synthetic images, especially non-existent face images that are visually indistinguishable from real faces, pose a severe social threat by enabling misinformation dissemination, often over online social networks and through fake social profiles. In spite of successful solutions being reported in the recent literature for detecting GAN-generated synthetic images, the performance of such schemes degrades considerably with the launch of post-processing attacks. In this work, we employ gradient of an image as the key component to detect synthetic images. According to our results, gradient proves to be a considerably efficient image derivative for synthetic image detection as well as to achieve robustness against post-processing attacks. We explore two different gradient operators and design four unique deep learning-based detection networks utilizing different gradient-based feature sets. Our solution achieves state-of-the-art (SOTA) detection accuracy (above 99%) on the test set consisting of STYLEGAN2 images and outperforms SOTA solutions for detecting post-processed and compressed images.

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Differential image-based Fast and Compatible Convolutional Layers for Multi-core Processors 5

Differential Image-based Fast and Compatible Convolutional L...

引用

5th International conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Hong, Sunghoon Park, Daejin Kyungpook Natl Univ Sch Elect & Elect Engn Daegu South Korea

ISBN: (纸本)9781665456456

Convolutional neural networks with powerful visual image analysis for artificial intelligence are gaining popularity in many research fields, leading to the development of various high-performance algorithms for convolution operators present in these networks. One of these approaches is implemented with general matrix multiplication (GEMM) using the well-known im2col transform for fast convolution operations. In this paper, we propose a multi-core processor-based convolution technique for high-speed convolutional neural networks (CNNs) using differential images. The proposed method improves the convolutional layer's response speed by reducing the computational complexity and using multi-thread technology. In addition, the proposed algorithm has the advantage of being compatible with all types of CNNs. We use the darknet network to evaluate the convolutional layer's performance and show the best performance of the proposed algorithm when using 4-thread parallel processing.

关键词： Convolutional neural networks Convolution techniques Deep learning Fast convolution Multicore processors

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STVP: A Spatiotemporal visual Perception Method for User-generated Content Video Quality Assessment

STVP: A Spatiotemporal Visual Perception Method for User-gen...

引用

IEEE visual communications and image processing (VCIP)

作者： Da Ai Mingyue Lu Jiahao Wang Ying Liu Center for Image and Information Processing Xi’an University of Posts and Telecommunications Xi’an China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China

With the popularity and development of short video applications, the behavior of using mobile devices to shoot and share user-generated content (UGC) videos has become increasingly common. Video quality assessment (VQA) is critical in guaranteeing end-user viewing experiences. UGC-VQA is a challenging problem due to the complexity and variety of distortion types of UGC videos and the absence of reference videos. To improve the consistency of UGC-VQA results and human subjective ratings, in this paper, we propose a UGC-VQA method based on spatiotemporal visual perception (STVP). Firstly, a hierarchical feature fusion module was added to the feature extraction network to realize the fusion of low-level visual features and high-level semantic features, and obtain the quality perception features with rich visual information. Then, we use the self-attention to weight different frames to distinguish their importance. The long short-term memory (LSTM) network and the time pool are used to model long-term dependencies and temporal memory effects. Experimental results on UGC-VQA datasets show that the proposed method achieves a performance improvement of nearly 2%, and its evaluation results are more consistent with human visual perception.

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Fast Machine Learning Aided Intra Mode Decision for Real-Time VVC Intra Coding

Fast Machine Learning Aided Intra Mode Decision for Real-Tim...

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IEEE visual communications and image processing (VCIP)

作者： Joose Sainio Baran Ataman Alban Marie Alexandre Mercat Jarno Vanne Ultra Video Group Tampere University Finland

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Reducing the huge computational complexity of intra mode decision is the key to real-time Video Coding (VVC). This paper proposes a fast intra mode decision scheme that takes advantage of lightweight machine learning (ML) models to classify intra modes into fifteen clusters. The cluster is further refined using one of the three proposed strategies to select the most optimal mode. Our experimental results with the fastest configuration of the practical uvg266 encoder show that the proposed methods yield a competitive rate-distortion-complexity trade-off over a conventional rough mode decision (RMD). To the best of our knowledge, this is the first work to successfully reduce the complexity of RMD in a practical VVC encoder with the use of ML techniques.

关键词： Video coding Logistic regression image coding visual communication Computational modeling Machine learning Predictive models Real-time systems Proposals Computational complexity

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JADD-GAN: A Joint Attention Generative Adversarial Data Fusion Network for Object Detection and Tracking 24

JADD-GAN: A Joint Attention Generative Adversarial Data Fusi...

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24th IEEE International conference on High Performance Computing and communications, 8th IEEE International conference on Data Science and Systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Xu, Guoxia Wang, Hao Zhao, Meng Zhu, Hu Norwegian University of Science and Technology Department of Computer Science Gjovik Norway School of Computer Science and Engineering Tianjin University of Technology Tianjin China School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9798350319934

image fusion is the fusion of images captured by different sensors to generate a single image with enhanced information, and fusion technology, as one of the important branches in the field of information fusion, mainly realizes the processing of multi-source image information. However, many commonly used fusion methods usually ignore the visual naturalness and information fidelity of the fused images and lack emphasis on the salient information, which makes the fused images unsuitable for human visual perception. To address these shortcomings of existing methods, in this paper, we propose the Joint Attention and Dual Discriminator Generative Adversarial Data Fusion Network JADD-GAN. In the generator module, to increase the extraction of multi-level information by the network, we firstly adopt a dual encoder structure and give information fusion in the decoder part. Secondly, different discriminators are used for infrared and visible images in order to highlight the thermal radiation information and key textures. The effectiveness of the method is verified by experiments on four datasets, and the results show that the method can effectively highlight the thermal radiation information and key texture details of the fused images, fully demonstrating its great potential and performance in solving the infrared and visible images fusion (IVF) problem. © 2022 IEEE.

关键词： Generative adversarial networks

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