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检索条件"任意字段=Conference on Visual Communications and Image Processing 2005"

共 4633 条记录，以下是411-420 订阅

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image Coding for Machines based on Non-Uniform Importance Allocation

Image Coding for Machines based on Non-Uniform Importance Al...

引用

IEEE visual communications and image processing (VCIP)

作者： Yunpeng Qi Ruoyu Feng Zhizheng Zhang Zhibo Chen University of Science and Technology of China Hefei China Microsoft Research Beijing China

In the Internet era, the explosive growth of media data processing poses significant challenges for the research of image Coding for Machines (ICM) in improving the efficiency of AI models while reducing the burdens of data storage and transmission. Existing ICM methods face challenges in achieving sufficient generalization ability when developing a single codec to handle diverse downstream tasks. To address these issues, we propose a unified ICM framework that facilitates diverse downstream tasks with a novel importance allocation mechanism. Equipped with a spatially variable-rate image compression codec, we introduce two options: online updating and offline predicting the non-uniform quality map, which governs the quality distribution of reconstructed images based on specific downstream tasks. Our proposed method is rigorously evaluated through extensive experiments on diverse and comprehensive fine-grained image classification datasets. The experiment results conclusively demonstrate the effectiveness of the proposed method in achieving a superior rate-distortion trade-off for ICM.

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Coding of Multi-Focused Plenoptic image using Disparity Shift and Sharpness-Aware Constraints

Coding of Multi-Focused Plenoptic Image using Disparity Shif...

引用

IEEE visual communications and image processing (VCIP)

作者： Thuc Nguyen Huu Vinh Van Duong Jonghoon Yim Byeungwoo Jeon Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea

Multi-focused plenoptic images possess many special characteristics related to the micro-images (MIs) array, which are expected to be useful in further increasing its compression performance. Those special characteristics come from the much overlap and sharpness variance among its micro-images, and proper handling of such properties can lead to better patch-based prediction. In this paper, for multi-focused plenoptic image data, we design a new prediction model taking into account the disparity shift constraint coming from the overlaps and the sharpness variation. Experiment results show coding gain respectively of 21% over the HEVC Intra and 27% when the proposed method is combined with the Intra Block Copy (IBC) tool which is reported very effective in plenoptic image coding.

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Foreground-Background Disentanglement based on image and Feature Co-Learning for 3D-Aware Generative Models

Foreground-Background Disentanglement based on Image and Fea...

引用

IEEE visual communications and image processing (VCIP)

作者： Sanghyuk Lee Daeha Kim Byung Cheol Song Department of Electrical and Computer Engineering Inha University Incheon Republic of Korea

Recently, studies on generative models using 3D information are active. GIRAFFE, one of the latest 3D-aware generative models, shows better feature disentanglement than existing generative models because it generates an image through volume rendering of independently formed 3D neural feature fields. However, GIRAFFE still suffers from an issue where foreground and background disentanglement is not smooth. In order to accomplish better disentanglement performance than GIRAFFE, we propose co-adversarial learning of the generative model at both image- and feature-levels. As a result of rich simulation experiments, the proposed generative model can produce photo-realistic images with only fewer parameters than existing 3D-aware generative models, along with excellent foreground-background disentanglement performance.

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3rd International conference on Computing and Communication Networks, ICCCN 2023

3rd International Conference on Computing and Communication ...

引用

3rd International conference on Computing and Communication Networks, ICCCN 2023

ISBN: (纸本)9789819708918

The proceedings contain 60 papers. The special focus in this conference is on Computing and Communication Networks. The topics include: Enhancing Security in Wireless Sensor Networks: A Broadcast/Multicast Authentication Framework with Identity-Based Signature Schemes;TSFTO: A Two-Stage Fuzzy-Based Tasks Orchestration Algorithm for Edge and Fog Computing Environments;tracking Climatic Variations Through Smart IoT-Driven Approach: An Exploratory Analysis;Design and Manufacturing of an Efficient Low-Cost Fire Surveillance System Based on GSM communications and IoT Technology;a Comparative Analysis of Optimized Routing Protocols for High-Performance Mobile Ad Hoc Networks;integrating Sandpiper Optimization Algorithm and Secure Aware Routing Protocol for Efficient Cluster Head Selection in Wireless Sensor Networks;the Effects of Weather Conditions on Data Transmission in Free Space for Wireless Sensor Networks;Using Wireless Sensor Network Performance and Optimizing in Underground Mines with Virtual MIMO Antenna;a Novel Logistic Regression-Based Fire Detection Model Using IoT in Underground Coal Mines;An MQTT IoT Intrusion Detection System Using Deep-Learning;multi-objective Optimal Feature Selection for Cyber Security Integrated with Deep Learning;application of Gradient Boosting Classifier-Based Computational Intelligence to Detect Drug Addiction Threat in Society;insurance 4.0: Smart Insurance Technologies and Challenges Ahead;review on Vision Transformer for Satellite image Classification;primitive Roots and Euler Totient Function-Based Progressive visual Cryptography;approach for Fire Detection Using image processing;review of Various Approaches for Authorship Identification in Digital Forensics;machine Learning Model for Traffic Prediction and Pattern Extraction in High-Speed Optical Networks;improved Markov Decision Process in Wireless Sensor Network for Optimal Energy Consumption.

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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...

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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...

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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...

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

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

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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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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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