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International Journal of Digital Content Technology and its Applications 2012年第14期6卷 456-468页

作者： Xiaofei, Hu Xiuchang, Zhu Key Lab of Image Processing and Image Communication College of Geography and Biological Information Nanjing University of Posts and Telecommunications Nanjing 210003 China Key Lab of Image Processing and Image Communication Key Lab ofBroadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Nanjing 210003 China

Distributed video coding (DVC) is a novel video coding paradigm. One approach to DVC is Wyner-Ziv distributed video coding. The accuracy of the correlation noise model can influence the performance of the video coder directly. In order to enhance the accuracy of the distribution model, EM algorithm based mixture Laplace-uniform distribution model and basic Laplace-uniform distribution model for DCT alternating current coefficients are established. Then the model is selected adaptively using fuzzy inference. Experimental results suggest that the proposed mixture correlation noise model can describe the heavy tail and sudden change of the noise accurately at high rate and make significant improvement on the coding efficiency compared with the DISCOVER's noise model. Meanwhile, fuzzy inference based adaptive noise model selection method can reduce the operation complexity to some extent, while not influencing rate-distortion performance.

关键词： image coding

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Real-time foreground detection based on tempo-spatial consistency validation and Gaussian mixture model

Real-time foreground detection based on tempo-spatial consis...

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2010 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2010

作者： Yan, Qing Xu, Yi Yang, Xiaokang Traversoni, Leonardo Institute of Image Communication and Information Processing Shanghai Key Lab. of Digital Media Processing and Transmission Shanghai Jiao Tong University Shanghai China Ciencias Bsicas e Ingenieria Mexico Mexico

ISBN: (纸本)9781424444625

Robust foreground detection is a fundamental precursor of many video processing applications. Although various approaches were advanced, there still exist many factors making detection very challenging: 1) Dynamic background with gradual brightness changes, camera movement and large amount of noises. 2) Sharp illumination changes caused by shadows, light on-off, and so on. 3) Real-time requirement for practical systems. To overcome these problems, a new approach is proposed in this paper. It is based on the background of conventional Gaussian Mixed Model, incorporating tempo-spatial consistency validation to search genuine foreground seeds, so that foreground segments can be reliably acquired using region growth method. Experiments demonstrate that our approach achieves better performance than conventional GMM approach in detection accuracy, adaptability to sudden illumination changes and computation time.

关键词： Gaussian distribution

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Video smoke removal based on low-rank tensor completion via spatial-temporal continuity constraint

Video smoke removal based on low-rank tensor completion via ...

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作者： Zhu, Hu Xu, Guoxia Liu, Lu Deng, Lizhen Jiangsu Province Key Lab on Image Processing and Image Communication Nanjing University of Posts and Telecommunication Nanjing China Department of Computer Science Norwegian University of Science and Technology Gjovik Norway National Engineering Research Center of Communication and Network Technology Nanjing University of Posts and Telecommunication Nanjing China

Smoke has a very bad effect on the outdoor vision system. Not only are the videos with poor visual effects obtained, but also the quality and structure of the videos are reduced. In this paper, we propose a video smoke removal method based on low-rank tensor completion via spatial-temporal continuity constraint. The proposed method is based on the smoke mixing model and consider the sparseness of smoke and the global and local consistency of clean video. Then, the optimal solution of the smoke removal algorithm model is quickly realized by the Alternating Direction Method of Multiplier. Finally, we evaluate the experiment results of real-world data and simulated data from the visual effects and objective indicators. And the experiment results show that our proposed algorithm can achieve better smoke removal results. © 2021 John Wiley & Sons, Ltd.

关键词： Smoke

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image super-resolution based on sparse representation and nonlocal regularization

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Journal of Computational Information Systems 2014年第5期10卷 2107-2116页

作者： Li, Xin Zhu, Xiuchang Jiangsu Province Key Lab. on Image Processing and Image Communication Nanjing University of Posts and Telecommunications Nanjing 210003 China

To solve the super-resolution reconstruction problem for single-frame image, an algorithm based on sparse representation and nonlocal regularization is proposed. By training the joint dictionaries, this algorithm looks for sparse representation in low-resolution dictionary for the input low-resolution image patch, and maps the sparse representation to high-resolution dictionary to construct high-resolution image patch. Then the reconstructed image patch is further regularized by the image's nonlocal self-similarity a priori information. Experimental results show that this algorithm can effectively ensure the quality of the reconstructed image, and greatly reduce the computation time. © 2014 Binary Information Press.

关键词： image reconstruction

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A novel spatial information recovery algorithm based on fuzzy clustering in H.264

A novel spatial information recovery algorithm based on fuzz...

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IEEE International Symposium on Intelligent Signal processing and communication Systems (ISPACS)

作者： Han Shen Xiu-chang Zhu Jiangsu Province Key Lab on Image Processing & Image Communication Nanjing University of Posts and Telecommunications China

As no reference frame can be obtained in Intra-frame error concealment, reducing the degradation of spatial reconstructed image is one of the key techniques in error concealment. In this paper, a novel spatial error concealment algorithm based on fuzzy clustering for Intra-frame in H.264 is proposed. This proposed algorithm gives a new selection of eigenvectors and similarity measurement for fuzzy clustering. The experimental results show that the proposed algorithm can improve the edge details, achieve a better subjective quality of the recovered image and increase PSNR (Peak Signal-to-Noise Ratio) as well.

关键词： Clustering algorithms Signal processing algorithms image reconstruction Degradation PSNR Resilience Video sequences Frequency Discrete cosine transforms Fuzzy systems

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Approaches to image retrieval using fuzzy set theory

Approaches to image retrieval using fuzzy set theory

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2008 IEEE International Conference Neural Networks and Signal processing, ICNNSP

作者： Li, Yang Xuelong, Hu Jun, Pan School of Information Engineering Yangzhou University Yangzhou Jiangsu 225009 China Jiangsu Province Key Lab. for Image Processing and Image Communication Nanjing University of Posts and Communications Nanjing Jiangsu 210003 China Jiangsu Prov. Key Lab. for Computer Information Processing Technology Suzhou University Suzhou Jiangsu 215006 China

ISBN: (纸本)9781424423118

This paper mainly presents two approaches for image retrieval. There is some faintness in color locating in quantification boundary when image color is quantized. The membership function in fuzzy set theory can describe the fuzzy transition preferably. In this paper, the membership function in HSI color space is adopted to solve the problem above. At the same time, spatial information is integrated with color feature to describe the image content more truly. Due to the importance of texture feature, gray concurrence matrix is extracted. In the second part of this paper, retrieval scheme combining four image feature descriptors is presented, and then follows Fuzzy Hamming Distance (FHD) which is used as a fuzzy similarity measure. We have performed an experiment on a 1000 images database and our results show higher retrieval accuracy. © 2008 IEEE.

关键词： Fuzzy sets

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Spatial Error Concealment Based on Judgement on Edge Information

Spatial Error Concealment Based on Judgement on Edge Informa...

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2008 International Conference on Audio，Language and image processing(2008国际声音、语言、图像过程大会)

作者： XueFeng Zhan XiuChang Zhu Nanjing Univ.of Posts & Telecomm Image Processing and Image Communication Key Lab Nanjing 210003

An efficient spatial error concealment algorithm is proposed for corrupted image by transmission losses in this work. Firstly, information of edges in the neighboring blocks is extracted by judgements on which edges may traverse the loss block. Then, all detected edges are divided into relevant and irrelevant edge. Subsequently, one or more directions for interpolation can be selected from the relevant edge set by minimizing the boundary pixel difference for each direction. The method improves the capability of recovering more high-detailed contents of lost block with low complexity. Experimental results demonstrate the better performance of the proposed methods as compared to previous techniques.

关键词： image edge detection Pixel Interpolation image reconstruction PSNR image restoration Sun

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Effective bandwidth based scheduling for streaming multimedia

Effective bandwidth based scheduling for streaming multimedi...

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Proceedings: 2003 International Conference on image processing, ICIP-2003

作者： Kang, Sang H. Zakhor, Avideh Communication Networks Research Lab. Department of ECE University of Seoul Korea Republic of Video and Image Processing Lab. Department of EECS U.C. Berkeley United States

We propose a class of packet scheduling algorithms for streaming media. The importance level of a video packet is determined by its relative position within its group of pictures, taking into account the motion-texture discrimination and temporal scalability. We generate a number of nested substreams, with more important streams embedding less important ones in a progressive manner. We model the streaming system as a queueing system, compute the run-time decoding failure probability of a frame in each substream based on effective bandwidth, and determine the optimum substream to be sent at any moment in time. The data within optimum substream is sent based on earliest-deadline-first scheduling, until the next channel report arrives, at which time the optimum substream is recomputed. From experiments with real video data, we show that our proposed scheduling scheme outperforms the conventional sequential sending scheme.

关键词： Multimedia systems

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image demosaicing by non-local similarity and local correlation

Image demosaicing by non-local similarity and local correlat...

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International Conference on Signal processing Proceedings (ICSP)

作者： Guo-gang Wang Xiu-chang Zhu Zong-liang Gan Province Key Lab on Image Processing and Image Communication Nanjing University of Posts and Telecommunications Nanjing China

Conventional single-chip digital cameras use color filter arrays(CFA) to sample different spectral components. image demosaicing is a problem of interpolating these data to complete red, green, and blue values for each image pixel, to produce an RGB image. Many color demosaicing(CDM) methods assume that the high local spatial redundancy exists among the color samples. Such an assumption, however, may be fail for images with high color saturation and sharp color transitions. This paper presents an adaptive demosaicing algorithm by exploiting both the non-local similarity and the local correlation(NLS-LC) in the color filter array image. First, the most flattest nonlocal image patches are searched in the searching window centered on the estimated pixel. Second, the patch, which is the most similar to the current patch, is selected among the most smoothest nonlocal patches. Third, according to the similar degree and the local correlation degree, the obtained nonlocal image patch and the current patch are adaptively chosen to estimate the missing color samples. Experimental results indicate that the proposed method exhibits superior performance over many state-of-the-art color interpolation methods.

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Video object matching based on sift algorithm

Video object matching based on sift algorithm

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2008 IEEE International Conference Neural Networks and Signal processing, ICNNSP

作者： Xuelong, Hu Yingcheng, Tang Zhenghua, Zhang School of Information Engineering Yangzhou University Yangzhou Jiangsu 225009 China Jiangsu Prov. Key Lab for Image Processing and Image Communication Nanjing University of Posts and Communications Nanjing Jiangsu 210003 China Jiangsu Prov. Key Lab. for Computer Information Processing Technology Suzhou University Suzhou Jiangsu 215006 China

ISBN: (纸本)9781424423118

SIFT (Scale Invariant Feature Transform) is used to solve visual tracking problem, where the appearances of the tracked object and scene background change during tracking. The implementation of this algorithm has five major stages: scale-space extrema detection;keypoint localization;orientation assignment;keypoint descriptor;keypoint matching. From the beginning frame, object is selected as the template, its SIFT features are computed. Then in the following frames, the SIFT features are computed. Euclidean distance between the object's SIFT features and the frames' SIFT features can be used to compute the accurate position of the matched object. The experimental results on real video sequences demonstrate the effectiveness of this approach and show this algorithm is of higher robustness and real-time performance. It can solve the matching problem with translation, rotation and affine distortion between images. It plays an important role in video object tracking and video object retrieval. © 2008 IEEE.

关键词： Tracking (position)

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