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

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CURVELET TRANSFORM BASED MOVING OBJECT SEGMENTATION

CURVELET TRANSFORM BASED MOVING OBJECT SEGMENTATION

引用

IEEE International conference on image processing

作者： Manish Khare Rajneesh Kumar Srivastava Ashish Khare Moongu Jeon Department of Electronics and Communication University of Allahabad Allahabad India School of Information and Communications Gwangju Institute of Science and Technology Gwangju Republic of Korea

ISBN: (纸本)9781479923427

In this paper, we have proposed a new method for segmentation of moving objects, which is based on single change detection applied on curvelet coefficients of two consecutive frames. The wavelet transform is widely used in moving object segmentation but it can not describe curve discontinuities. Therefore we have used curvelet transform for segmentation of moving objects. The proposed method is simple and does not require any other parameter except curvelet coefficients. Results after applying the proposed method for segmentation of moving object are compared with other state-of-the-art methods in terms of visual as well as quantitative performance measures viz. Misclassification penalty, Relative position based measure and Structural content. The proposed method is found to be better than other methods.

关键词： Moving object segmentation Curvelet transform Single change detection segmentation Moving Wavelet Analysis Object segmentation Segmentation wavelet transforms new methods visualization

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Advanced Learning-Based Inter Prediction for Future Video Coding

Advanced Learning-Based Inter Prediction for Future Video Co...

引用

IEEE visual communications and image processing (VCIP)

作者： Yanchen Zhao Wenhong Duan Chuanmin Jia Shanshe Wang Siwei Ma School of Computer Science Peking University China Wangxuan Institute of Computer Technology Peking University China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

In the fourth generation Audio Video coding Standard (AVS4), the Inter Prediction Filter (INTERPF) reduces discontinuities between prediction and adjacent reconstructed pixels in inter prediction. The paper proposes a low complexity learning-based inter prediction (LLIP) method to replace the traditional INTERPF. LLIP enhances the filtering process by leveraging a lightweight neural network model, where parameters can be exported for efficient inference. Specifically, we extract pixels and coordinates utilized by the traditional INTERPF to form the training dataset. Subsequently, we export the weights and biases of the trained neural network model and implement the inference process without any third-party dependency, enabling seamless integration into video codec without relying on Libtorch, thus achieving faster inference speed. Ultimately, we replace the traditional handcraft filtering parameters in INTERPF with the learned optimal filtering parameters. This practical solution makes the combination of deep learning encoding tools with traditional video encoding schemes more efficient. Experimental results show that our approach achieves 0.01%, 0.31%, and 0.25% coding gain for the Y, U, and V components under the random access (RA) configuration on average.

关键词： Video coding Training image coding Filtering visual communication Computational modeling Neural networks Complexity theory Video codecs Standards

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BVI-AOM: A New Training Dataset for Deep Video Compression Optimization

BVI-AOM: A New Training Dataset for Deep Video Compression O...

引用

IEEE visual communications and image processing (VCIP)

作者： Jakub Nawała Yuxuan Jiang Fan Zhang Xiaoqing Zhu Joel Sole David Bull Visual Information Laboratory University of Bristol Bristol United Kingdom Netflix Inc. Los Gatos CA USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Deep learning is now playing an important role in enhancing the performance of conventional hybrid video codecs. These learning-based methods typically require diverse and representative training material for optimization in order to achieve model generalization and optimal coding performance. However, existing datasets either offer limited content variability or come with restricted licensing terms constraining their use to research purposes only. To address these issues, we propose a new training dataset, named BVI-AOM, which contains 956 uncompressed sequences at various resolutions from 270p to 2160p, covering a wide range of content and texture types. The dataset comes with more flexible licensing terms and offers competitive performance when used as a training set for optimizing deep video coding tools. The experimental results demonstrate that when used as a training set to optimize two popular network architectures for two different coding tools, the proposed dataset leads to additional bitrate savings of up to 0.29 and 2.98 percentage points in terms of PSNR-Y and VMAF, respectively, compared to an existing training dataset, BVI-DVC, which has been widely used for deep video coding. The BVI-AOM dataset is available at https://***/fan-aaron-zhang/bvi-aom.

关键词： Training Video coding image coding visual communication Video compression Performance gain Video codecs Spatial resolution Optimization Standards

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Agent swarm classification network ASCN

Agent swarm classification network ASCN

引用

IEEE International conference on Systems, Man and Cybernetics

作者： Chi-kin Chow Hung-tat Tsui Visual Signal Processing and Communications Laboratory Department of Electronic Engineering Chinese University of Hong Kong Hong Kong China

ISBN: (纸本)0780385675

In this paper we introduced a new RBF classification network - "agent swarm classification network ASCN", which is trained by a multi-agent system (MAS) approach. MAS can be regarded as a swarm of independent software agents interact with each other to achieve common goals, complete concurrent distributed tasks under autonomous control. By treating each neuron as an agent, the weights of neurons can be determined through a set of pre-defined simple agent behavior. Three sets of experiments are examined to observe the effectiveness of the proposed method.

关键词： Multiagent systems Neurons Radial basis function networks Speech recognition image recognition Low earth orbit satellites Signal processing Laboratories Neural networks image processing

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Flexible face animation using MPEG-4/SNHC parameter streams

引用

IEEE International conference on image processing

作者： Jialin Zhong Multimedia Communications Research Laboratory Bell Laboratories Lucent Technology Murray Hill NJ USA

ISBN: (纸本)0818688211

We describe a new approach to animating generic 3D wireframe models of human heads using MPEG-4 synthetic/natural hybrid coding (MPEG4/SNHC) facial definition parameters (FDP) points and facial animation parameters (FAP) streams. The FDP points are used to partition a head model into sections that have approximately conforming motion with human muscles. Using the borders of these sections as features, a 3D morphing algorithm is developed to morph vertices from one frame to the next while preserving the qualities of facial expressions such as those of human visual speech. A comparison is made with the facial animation table (FAT) approach proposed in MPEG-4/SNHC. Experimental results on standard FAP sequences and on a proprietary system are shown.

关键词： Facial animation MPEG 4 Standard Humans Biological system modeling Financial advantage program Decoding Muscles Streaming media Speech Face

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A Novel Approach to image Denoising in Multi-wavelet Domain

A Novel Approach to Image Denoising in Multi-wavelet Domain

引用

2006 International conference on communications,Circuits and Systems(第四届国际通信、电路与系统学术会议)

作者： Shengli Lai Yongming Zhou Peizhuo Lv Leian Liu Jian Yu School of Electronic and Information Engineering South China University of Technology Guangzhou 510 Department of Electronic Engineering Shaoguan University Shaoguan 512005 China

This paper presents a new algorithm of image de-noising. This new algorithm introduces both a new threshold function and multilevel threshold, and has not only the de-noising ability of the energy-adaptation, but also the capability of protecting edge information of image signal during the de-noising process. Simulation results based on CL2 multi-wavelet transformation indicate that the de-noising approach with both the new threshold function and multilevel threshold outperforms the previous image de-noising methods in both visual effect and signal-to-noise ratio.

关键词： image denoising Noise reduction Signal processing algorithms Signal to noise ratio Wavelet coefficients Noise level Power engineering and energy Signal processing visual effects Signal denoising

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Subband/VQ coding in perceptually uniform color spaces

Subband/VQ coding in perceptually uniform color spaces

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： R.E. Van Dyck S.A. Rajala Center for Communications and Signal Processing North Carolina State University Raleigh NC USA

A subband/VQ coder that combines the three color components of each subband pixel into a length 3 vector and then vector quantizes each subband is presented. Perceptual weights derived from subjective visual experiments are used along with the rate-distortion curves to determine a perceptually optimal bit allocation. Advantages of this method are that fewer codebooks need to be designed and the resulting bit allocation problem is simplified. Preliminary results show that this method achieves results comparable to the more standard subband/VQ method where the color components of each subband are vector quantized separately using the perceptually optimal bit allocation algorithm. To obtain the required compression ratio and the high color fidelity required for HDTV applications, the vector quantization is done in a perceptually uniform color space.< >

关键词： Color Pixel Bit rate Distortion measurement Displays Filter bank Signal processing image coding image reconstruction Vector quantization

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引用

IEEE visual communications and image processing (VCIP)

作者： Ziyin Huang Yui-Lam Chan Ngai-Wing Kwong Sik-Ho Tsang Kin-Man Lam Wing-Kuen Ling Department of Electronic and Information Engineering The Hong Kong Polytechnic University School of Information Engineering Guangdong University of Technology

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Compressed screen content videos often exhibit artifacts in edge areas and suffer from distortions during scene switches, where content abruptly changes between frames. Existing multi-frame models, which use a fixed range of neighbor frames, struggle with these switches. To address this, we propose a novel method that effectively handles scene switches. Our approach utilizes Long-term Feature Extraction (LFE) to capture contextual information, while the Frame Similarity-based Short-term Feature Extraction (FSFE) focuses on texture information to manage fast motion and scene switches. In FSFE, a Similarity-based Neighbor Frame Selector (SNFS) is designed to choose relevant neighbor frames for the short-term stream, enhancing the quality of scene switch frames. To fuse short-term and long-term features adaptively, we introduce a local-spatial and global-channel attention module, which recalibrates spatial and channel-wise feature responses. Experimental results show that our Frame Similarity-Based via Adaptive Long Short-Term Fusion (FSLST) method significantly improves the quality of compressed videos, outperforming current state-of-the-art methods.

关键词： image coding Fuses visual communication Streaming media Feature extraction Distortion Quality assessment Videos

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Frame reconstruction of the Laplacian pyramid

Frame reconstruction of the Laplacian pyramid

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： M.N. Do M. Vetterli Laboratory for Audio-visual Communications Swiss Federal Institute of Technology Lausanne Switzerland Department of EECS University of California Berkeley Berkeley CA USA

We study the Laplacian pyramid (LP) as a frame operator, and this reveals that the usual reconstruction is suboptimal. With orthogonal filters, the LP is shown to be a tight frame, thus the optimal linear reconstruction using the dual frame operator has a simple structure as symmetrical with the forward transform. For more general cases, we propose an efficient filter bank for reconstruction in the LP that is shown to perform better than the usual method. Numerical results indicate that gains of more than 1 dB are actually achieved.

关键词： Laplace equations image reconstruction Filter bank image coding Reconstruction algorithms Frequency Nonlinear filters Filtering Wavelet transforms Multidimensional systems

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Enhancing Privacy-Utility Tradeoff with Few-Round Strategy in Heterogeneous Federated Learning

Enhancing Privacy-Utility Tradeoff with Few-Round Strategy i...

引用

IEEE visual communications and image processing (VCIP)

作者： Qingbin Wei Feilong Zhang Yuanchao Bai Deming Zhai Junjun Jiang Xianming Liu Faculty of Computing Harbin Institute of Technology

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Federated learning inherently provides a certain level of privacy protection, which however is often inadequate in many real-world scenarios. Existing privacy-preserving methods frequently incur unbearable time overheads or result in non-negligible deterioration to model performance, thus suffering from the tradeoff between performance and privacy. In this work, we propose a novel Federated Privacy-Preserving Knowledge Transfer framework, namely FedPPKT, which employs data-free knowledge distillation in a meta-learning manner to rapidly generates pseudo data and performs privacy-preserving knowledge transfer. FedPPKT establishes a protective barrier between the original private data and the federated model, thereby ensuring user privacy. Furthermore, leveraging the few-round strategy of FedPPKT, it has the capability to reduce the number of communication rounds, further mitigating the risk of privacy exposure for user data. With the help of the meta generator, the problem of uneven local label distribution on clients is alleviated, mitigating data heterogeneity and improving model performance. Experiments show that FedPPKT outperforms the state-of-the-art privacy-preserving federated learning methods. Our code is publicly available at https://***/HIT-weiqb/FedPPKT.

关键词： Data privacy Privacy image coding Federated learning visual communication Generators Data models Servers Protection Knowledge transfer

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