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Low-Rank and Locally Linear Embedding Approach to image Inpainting 33

Low-Rank and Locally Linear Embedding Approach to Image Inpa...

引用

33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Sasaki, Ryohei Konishi, Katsumi Takahashi, Tomohiro Furukawa, Toshihiro Tokyo Univ Sci Grad Sch Engn Tokyo Japan Hosei Univ Grad Sch Informat Sci Tokyo Japan Tokai Univ Grad Sch Informat Sci & Technol Hiratsuka Kanagawa Japan

ISBN: (纸本)9781538644584

This paper proposes a new algorithm for image inpainting algorithm based on the matrix rank minimization and locally linear embedding (LLE) method. Assuming that intensity values of an image belong to an unknown manifold, which can be mapped into a linear subspace by an unknown function, the image inpainting problem is formulated as minimizing sum of rank of submatrices of Hankel matrix. In order to solve the problem, this paper modifies the iterative partial matrix shrink-age (IPMS) algorithm and provides an inpainting algorithm. Numerical examples show that the proposed algorithm recovers missing pixels efficiently.

关键词： image inpainting 2D AR model matrix rank minimization manifold learning locally linear embedding

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Hierarchical Subspace Regression for Compressed Face image Restoration 10

Hierarchical Subspace Regression for Compressed Face Image R...

引用

10th International conference on Wireless communications and Signal processing (WCSP)

作者： Liu, Xinyu Gan, Zongliang Liu, Feng Nanjing Univ Posts & Telecommun Jiangsu Prov Key Lab Image Proc & Image Commun Nanjing 210003 Jiangsu Peoples R China

ISBN: (纸本)9781538661192

In this paper, we propose a novel hierarchical subspace regression algorithm based on edge orientations of compressed face image patches which includes two parts: training and restoration phases. In the training phase, the rule of the face edge-orientation (EO) distribution is used to classify the image patches into shallow subspaces. Then, the k-means clustering is used to cluster the deep subspaces of each EO-based shallow subspace, and corresponding linear mapping training is performed for each deep subspace. In restoration phase, an appropriate linear mapping selected based on the EO of compressed input image patch is applied to generate the restored output image patch. The experimental results show that the peak signal to noise ratio (PSNR) and structural similarity index (SSIM) are better than the existing popular algorithm, and they can effectively remove the blocking artifact and zigzag effect, so as to improve the visual effect.

关键词： compressed face images subspace regression edge-orientation(EO) shallow subspace deep subspace

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Rate-mixed HEVC Tile based 360 Video Streaming System 33

Rate-mixed HEVC Tile based 360 Video Streaming System

引用

33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Luo, Ying Liu, Xu Zhu, Chen Xie, Rong Song, Li Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Cooperat Medianet Innovat Ctr Shanghai Peoples R China

ISBN: (纸本)9781538644584

Recently, tile-based viewport adaptation is a popular method for 360 video streaming. Our demonstration adopts a rate-mixed transmission approach utilizing a VR adaptation agent at the server end for viewport-based streaming, which is client-compatible and can be scalable to different users. The FOV prediction is applied to improve the viewing experience. The feasibility of our system to head-mounted displays is verified, which can reduce bandwidth consumption by up to 36%.

关键词： virtual reality 360 video streaming rate-mixed scheme bandwidth saving field of view(FOV)

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An improved Real-Time Video Communication System 33

An improved Real-Time Video Communication System

引用

33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Ma, Zhaoliang Yu, Shengwei Huang, Yongcheng Xie, Rong Song, Li Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Cooperat Medianet Innovat Ctr Shanghai Peoples R China

ISBN: (纸本)9781538644584

In this paper, we optimized the Linphone-based real-time video communication system. Firstly, we used HEVC to replace the H.264 in order to reduce the bandwidth pressure while reducing the buffer delay by configuring the appropriate encoding parameters. Secondly, we added an effective bitrate adaptive algorithm based on additive increase and multiplicative decrease (AIMD) in the system, which can effectively reduce the packet loss rate in the network with high bandwidth fluctuations.

关键词： Real Time Communication HEVC VoIP Linphone MediaCodec

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Semantic Segmentation via Domain Adaptation with Global Structure Embedding

Semantic Segmentation via Domain Adaptation with Global Stru...

引用

IEEE visual communications and image processing conference

作者： Tianyi Zhang Guosheng Lin Jianfei Cai Alex C. Kot Rapid-Rich Object Search (ROSE) Lab Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore

In this paper we focus on the problem of unsupervised domain adaptation for semantic segmentation. The previous works usually focus on adversarial learning either in pixel-level or feature-level. However, global structure knowledge is often neglected in the adversarial learning due to the possible reasons: First, the result of pixel-level adversarial learning does not necessarily preserve the semantic consistency of the input image. Second, global structure knowledge is not embedded to regularize the feature-level adversarial learning. In this work, we propose a framework for unsupervised domain adaptation in semantic segmentation which effectively incorporates pixel-level, feature-level adversarial learning and self-training strategy. Our framework embeds the global structure knowledge into the adversarial training step to tackle the problem of structure misalignment. Consequently, our proposed framework achieves the state-of-the-art semantic segmentation domain adaptation results on the task of transferring GTA5 to Cityscapes.

关键词： image segmentation Semantics Training Task analysis Adaptation models Generators Feature extraction

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Direct Application of Convolutional Neural Network Features to image Quality Assessment 33

Direct Application of Convolutional Neural Network Features ...

引用

33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Hou, Xianxu Sun, Ke Liu, Bozhi Gong, Yuanhao Garibaldi, Jonathan Qiu, Guoping Shenzhen Univ Coll Informat Engn Shenzhen Peoples R China Univ Nottingham Sch Comp Sci Nottingham England

ISBN: (纸本)9781538644584

We take advantage of the popularity of deep convolutional neural networks (CNNs) and have developed a very simple image quality assessment method that rivals state of the art. We show that convolutional layer outputs (deep features) of a CNN compute the local structural information of spatial regions of different sizes in the input image. The learned convolutional kernels contain a much richer set of weights thus capturing much more local structural information than hand crafted ones. As the deep features learned from large datasets already contain very rich multi-resolutional structural image information, they can be directly used to calculate visual distortion of an image and it is not necessary to introduce further complicated computational process. We will present experimental results to demonstrate that this is indeed the case, and that simple cosine distance of the deep features is as good as state the art methods for full reference image quality assessment.

关键词： CNN deep features image quality assessment

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Weakly Supervised Learning for Blind image Quality Assessment

Weakly Supervised Learning for Blind Image Quality Assessmen...

引用

IEEE visual communications and image processing conference

作者： Weiquan He Xinb Gao Wen Lu Ruoxuan Guan School of Electronic Engineering Xidian University Xian China

The blind image quality assessment (BIQA) metric based on deep neural network (DNN) achieves the best evaluation accuracy at present, and the depth of neural networks plays a crucial role for deep learning-based BIQA metric. However, training a DNN for quality assessment is known to be hard because of the lack of labeled data, and getting quality labels for a large number of images is very time consuming and costly. Therefore, training a deep BIQA metric directly will lead to over-fitting in all likelihood. In order to solve this problem, we introduced a weakly supervised approach for learning a deep BIQA metric. First, we pre-trained a novel encoder-decoder architecture by using the training data with weak quality annotations. The annotation is the error map between the distorted image and its undistorted version, which can roughly describes the distribution of distortion and can be easily acquired for training. Next, we fine-tuned the pre-trained encoder on the quality labeled data set. Moreover, we used the group convolution to reduce the parameters of the proposed metric and further reduce the risk of over-fitting. These training strategies, which reducing the risk of over-fitting, enable us to build a very deep neural network for BIQA to have a better performance. Experimental results showed that the proposed model had the state-of-art performance for various images with different distortion types.

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Improving Person Re-Identification Performance Using Body Mask Via Cross-Learning Strategy

Improving Person Re-Identification Performance Using Body Ma...

引用

IEEE visual communications and image processing conference

作者： Junyi Wu Lingxiang Yao Yan Huang Jingsong Xu Qiang Wu Liqin Huang College of Physics and Information Fuzhou University Fuzhou China School of Electrical and Data Engineering University of Technology Sydney Sydney Australia

The task of person re-identification (re-id) is to find the same pedestrian across non-overlapping cameras. Normally, the performance of person re-id can be affected by background clutters. However, existing segmentation algorithms are hard to obtain perfect foreground person images. To effectively leverage the body (foreground) cue, and in the meantime pay attention to discriminative information in the background (e.g., companion or vehicle), we propose to use a cross-learning strategy to take both foreground and other discriminative information into account. In addition, since currently existing foreground segmentation result always involves noise, we use Label Smoothing Regularization (LSR) to strengthen the generalization capability during our learning process. In experiments, we pick up two state-of-the-art person re-id methods to verify the effectiveness of our proposed cross-learning strategy. Our experiments are carried out on two publicly available person re-id datasets. Obvious performance improvements can be observed on both datasets.

关键词： image segmentation Correlation Training Feature extraction Cameras Interference Task analysis

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Near-Duplicate image Retrieval Based on Multiple Features 33

Near-Duplicate Image Retrieval Based on Multiple Features

引用

33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Zhang, Xueqing Nanjing Univ Sci & Technol Nanjing 260294 Jiangsu Peoples R China

ISBN: (纸本)9781538644584

In this paper, we propose a near-duplicate image retrieval method based on multiple features. Combining the deep features extracted from the VGG relu6 layer with the improved local feature descriptors, we attempt to simulate the nearduplicate image retrieval process of the human brain through a two-layer retrieval structure. Inspired by the proposed CROW feature, we calculate the weights on VGG shallow pooling layer and extract the interest domains for screening surf feature points. At the same time, a center weight is proposed to improve the VLAD algorithm. Experiments show that our method can not only obtain the visually similar results of an image, but also obtain the results that contain the visually prominent parts of the image.

关键词： near-duplicate retrieval interest domain center weights multiple features convolutional neural networks (CNN)

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Super-Resolution Based on Back-Projection of Interpolated image

Super-Resolution Based on Back-Projection of Interpolated Im...

引用

International conference on Advanced Technologies for communications, ATC

作者： Sawiya Kiatpapan Takuro Yamaguchi Masaaki Ikehara Graduate School of Science and Technology Keio University Japan

image upscaling to obtain high quality digital image is one of the active research topics as it is applicable in the consumer electronics industries. Traditional image upscaling techniques have low computational complexity and applicable for real-time processing, but reconstructed image often contains artifacts and undesirable visual effect. The relationship between image interpolation and super-resolution leads our assumption that the interpolated image can be further optimized and may be considered as a part of super-resolution algorithm. In this paper, we propose a new image super-resolution method to combine fast image interpolation with iterative back-projection. This method does not require any external pre-trained datasets and has low computation time while the quality of the reconstructed image can be measured up to the high programming complexity methods such as the dictionary and deep convolutional neural networks.

关键词： Interpolation image reconstruction Spatial resolution Dictionaries image edge detection Microsoft Windows

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