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2019 IEEE Visual Communications and Image Processing (VCIP)最新文献

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Water-Bottom Video Coding Based on Coding- Oriented Reference Frame Generation 基于编码导向的参考帧生成的水下视频编码
2019 IEEE Visual Communications and Image Processing (VCIP) Pub Date : 2019-12-01 DOI: 10.1109/VCIP47243.2019.8965927
Seishi Takamura, A. Shimizu
{"title":"Water-Bottom Video Coding Based on Coding- Oriented Reference Frame Generation","authors":"Seishi Takamura, A. Shimizu","doi":"10.1109/VCIP47243.2019.8965927","DOIUrl":"https://doi.org/10.1109/VCIP47243.2019.8965927","url":null,"abstract":"The video contents containing water scenes, such as seashore, water-sports, river-side, waving pool etc. are rich in sensation of presence. From the video coding point of view, those sequences are quite difficult to encode efficiently because of random movement and nonlinear deformation of the objects seen through waving water. This paper aims to tackle this problem by generating one additional frame from input video sequence. We additionally encode the frame and store as longterm reference frame and then encode the input sequence while referring to the additional frame. The key idea of it is to generate as good reference frame as possible in terms of motion compensation accuracy, and hence, in terms of coding efficiency. Our novel approach yielded BD-Rate reduction of 12 to 42% compared to VTM1.1 and 13 to 48% compared to VTM4.0, next generation video coding test model.","PeriodicalId":388109,"journal":{"name":"2019 IEEE Visual Communications and Image Processing (VCIP)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121931635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Global and Local Deep Feature Representation Fusion for Vehicle Re-Identification 基于全局和局部深度特征表示的车辆再识别
2019 IEEE Visual Communications and Image Processing (VCIP) Pub Date : 2019-12-01 DOI: 10.1109/VCIP47243.2019.8965856
Xing Zhao Lee, Hao Wang, Jiangtao Kong, Chi Su, Junliang Xing, S. Shen
{"title":"Global and Local Deep Feature Representation Fusion for Vehicle Re-Identification","authors":"Xing Zhao Lee, Hao Wang, Jiangtao Kong, Chi Su, Junliang Xing, S. Shen","doi":"10.1109/VCIP47243.2019.8965856","DOIUrl":"https://doi.org/10.1109/VCIP47243.2019.8965856","url":null,"abstract":"This paper introduces our submission to the Grand Challenges on Vehicle Re-Identification (ReID) held in the VCIP 2019. Vehicle Re-Identification, which aims to retrieve images of a query vehicle from a large-scale vehicle database, is of great significance to the urban security and city management. Although significant progress has been made in the last decade, vehicle ReID in the wild remains a very challenging problem due to the large intra-class variations of one vehicle instance from viewpoint, illumination, occlusion patterns, and the possibly small inter-class differentiation of different vehicle instances (e.g., two different vehicles of the same model and color). To deal with these challenges, we in this work present a vehicle ReID framework that integrates both the global visual cues along with the local part-based cues to learn discriminative feature representations. In addition, the proposed framework also makes use of the extra information such as brands, models and colors to further improve the performance. Experimental results are performed on the VCIP 2019 VehicleReID dataset and the proposed framework achieved the second place in the competition.","PeriodicalId":388109,"journal":{"name":"2019 IEEE Visual Communications and Image Processing (VCIP)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124769561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Novel Approach to Image Steganography Based on the Image Colorization 一种基于图像着色的图像隐写新方法
2019 IEEE Visual Communications and Image Processing (VCIP) Pub Date : 2019-12-01 DOI: 10.1109/VCIP47243.2019.8965732
Kazunori Uruma, K. Konishi, Tomohiro Takahashi, T. Furukawa
{"title":"A Novel Approach to Image Steganography Based on the Image Colorization","authors":"Kazunori Uruma, K. Konishi, Tomohiro Takahashi, T. Furukawa","doi":"10.1109/VCIP47243.2019.8965732","DOIUrl":"https://doi.org/10.1109/VCIP47243.2019.8965732","url":null,"abstract":"This paper proposes a novel image steganography algorithm for color image. Recently, colorization-based image coding technique has been studied. In order to compress the color image effectively, this technique transform the chrominance image to a vector in a low-dimensional subspace via the colorization matrix. This paper utilizes the colorization-based image coding for steganography algorithm, where the secret data is embedded into the null space of the colorization matrix. Because the null space is high dimension enough, a large capacity data can be embedded. Numerical examples show that the proposed algorithm embeds large capacity secret data such as grayscale image into color image effectively.","PeriodicalId":388109,"journal":{"name":"2019 IEEE Visual Communications and Image Processing (VCIP)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123783493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
An End-to-End Future Frame Prediction Method for Vehicle-Centric Driving Videos 以车辆为中心的驾驶视频端到端未来帧预测方法
2019 IEEE Visual Communications and Image Processing (VCIP) Pub Date : 2019-12-01 DOI: 10.1109/VCIP47243.2019.8965824
Li Du, Kaikun Ji, Zhicheng Zhao, Fei Su, Bojin Zhuang
{"title":"An End-to-End Future Frame Prediction Method for Vehicle-Centric Driving Videos","authors":"Li Du, Kaikun Ji, Zhicheng Zhao, Fei Su, Bojin Zhuang","doi":"10.1109/VCIP47243.2019.8965824","DOIUrl":"https://doi.org/10.1109/VCIP47243.2019.8965824","url":null,"abstract":"In the field of autonomous driving, training an agent to watch and think similar to human drivers is an efficient way to solve self-driving problems. Inspired by NVIDIA’s frame-level command generation task [2] and the discovery of humans memory capacity [13], we propose a future frame prediction method for vehicle-centric driving videos. An end-to-end deep learning architecture called future frame prediction (FFPRE) network is proposed, which can generate a future frame following the input video sequence. In particular, we develop a general memory preserving module to extract meaningful history information from input data. This module consists of two parts, namely, memory recall and memory refine. We train this module to generate the short-term spatiotemporal information of a given video batch, which is a concatenation of history appearance and temporal clues. Thereafter, the two history clues will be transformed into future representations by a long-term prediction module. Thus, humans’ driving prediction progress is mimicked in a completely modular manner. Given the FFPRE network’s effective long-short spatiotemporal feature learning ability, the proposed network can construct an internal representation (content and dynamic) of vehicle-centric driving videos without tracking the trajectory of every pixel. Experimental results on publicly released datasets of NVIDIA and DR(eye)VE indicate that our proposed method is efficient.","PeriodicalId":388109,"journal":{"name":"2019 IEEE Visual Communications and Image Processing (VCIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130604894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Occlusion Deep Alignment Network 遮挡深度对准网络
2019 IEEE Visual Communications and Image Processing (VCIP) Pub Date : 2019-12-01 DOI: 10.1109/VCIP47243.2019.8966009
Yang Li, Chang Shu, Ning Zhou, Jianyu Hong, Xiaofeng Li
{"title":"Occlusion Deep Alignment Network","authors":"Yang Li, Chang Shu, Ning Zhou, Jianyu Hong, Xiaofeng Li","doi":"10.1109/VCIP47243.2019.8966009","DOIUrl":"https://doi.org/10.1109/VCIP47243.2019.8966009","url":null,"abstract":"The performance of face localization system often degenerates when occlusions occur. In this paper, we propose Occlusion Deep Alignment Network(ODAN), which is robust to occlusion. Occlusion detection and rough face alignment are employed simultaneously, where the occlusion detection of landmarks is treated as a regression problem. Based on occlusion information, localization offset scaling is employed to transform the difference between the groundtruth and the rough landmark estimation to another linear space to generate target positions for precise face alignment, reducing the difficulty of the regression task. Finally, the outputs of the proposed model are transformed back to Euclidean Space. Experiments on two public datasets show that the proposed method achieves state-of-art performance under severe occlusion both in accuracy and efficiency.","PeriodicalId":388109,"journal":{"name":"2019 IEEE Visual Communications and Image Processing (VCIP)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123338644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
No-Reference Stereoscopic Image Quality Assessment Based On Shuffle-Convolutional Neural Network 基于shuffle -卷积神经网络的无参考立体图像质量评价
2019 IEEE Visual Communications and Image Processing (VCIP) Pub Date : 2019-12-01 DOI: 10.1109/VCIP47243.2019.8965759
Sumei Li, Mingyi Wang, Chunping Hou
{"title":"No-Reference Stereoscopic Image Quality Assessment Based On Shuffle-Convolutional Neural Network","authors":"Sumei Li, Mingyi Wang, Chunping Hou","doi":"10.1109/VCIP47243.2019.8965759","DOIUrl":"https://doi.org/10.1109/VCIP47243.2019.8965759","url":null,"abstract":"With the development of stereoscopic imaging technology, stereoscopic image quality assessment (SIQA) has been gaining great attention. In this work, to find a better SIQA method conforming to the perceptual characteristics of our brain, we propose a two-channel convolutional neural network (CNN) based on shuffle unit, which is called SCNN, for no-reference SIQA. The shuffle unit is used to mix up the features extracted from the left and right views to complete information communication between the two views. Different from other SIQA methods, the four shuffle units among proposed model achieve the multiple binocular fusions while processing the left and right views. Moreover, the Shuffle v2 block before the global pooling layer further improves the accuracy of SCNN. In addition, it is worth noting that we employ decorrelated batch normalization (DBN) to obtain the better generalization ability. Experimental results demonstrate that the proposed model outperforms the state-of-the-art no-reference SIQA methods.","PeriodicalId":388109,"journal":{"name":"2019 IEEE Visual Communications and Image Processing (VCIP)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134326201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Deep quad path quality evaluator combined aesthetic estimation for Multi-exposure image fusion 多曝光图像融合的深度四路质量评价方法
2019 IEEE Visual Communications and Image Processing (VCIP) Pub Date : 2019-12-01 DOI: 10.1109/VCIP47243.2019.8966056
R. Guan, Wen Lu, Weiquan He, Furui Bai
{"title":"Deep quad path quality evaluator combined aesthetic estimation for Multi-exposure image fusion","authors":"R. Guan, Wen Lu, Weiquan He, Furui Bai","doi":"10.1109/VCIP47243.2019.8966056","DOIUrl":"https://doi.org/10.1109/VCIP47243.2019.8966056","url":null,"abstract":"There are limitations in existing multi-exposure fused image quality assessment (MEF-IQA) approaches. The first is that the existing approaches are hard to estimate the consistency of luminance distribution in fused images, which can be easily destroyed in fusion process. The second is that the aesthetics estimation of fused images are neglected in existing approaches, which should be considered for the images produced by fusion algorithms to give people an aesthetic experience. In order to solve these problems, a MEF-IQA approach based on the deep network was proposed. First, we adapted a quad path network as luminance error quality evaluator (LE) to extract the features, which can capture the luminance distribution patterns, from fused images. Then, we trained an aesthetic evaluator (AE) to obtain the feature which can describe the aesthetic attributes of fused image. Finally, the two extracted features are combined for predicting the perceptual quality scores. Experimental result clearly shows that the proposed approach can quantify the image quality reliably, and it is highly correlated with subjective quality scores.","PeriodicalId":388109,"journal":{"name":"2019 IEEE Visual Communications and Image Processing (VCIP)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133260872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Improving Person Re-Identification Performance Using Body Mask Via Cross-Learning Strategy 通过交叉学习策略提高人体面具的再识别性能
2019 IEEE Visual Communications and Image Processing (VCIP) Pub Date : 2019-12-01 DOI: 10.1109/VCIP47243.2019.8965655
Junyi Wu, Lingxiang Yao, Y. Huang, Jingsong Xu, Qiang Wu, Liqin Huang
{"title":"Improving Person Re-Identification Performance Using Body Mask Via Cross-Learning Strategy","authors":"Junyi Wu, Lingxiang Yao, Y. Huang, Jingsong Xu, Qiang Wu, Liqin Huang","doi":"10.1109/VCIP47243.2019.8965655","DOIUrl":"https://doi.org/10.1109/VCIP47243.2019.8965655","url":null,"abstract":"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.","PeriodicalId":388109,"journal":{"name":"2019 IEEE Visual Communications and Image Processing (VCIP)","volume":"23 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113984646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Scale-Informed Density Estimation for Dense Crowd Counting 基于尺度的密集人群计数密度估计
2019 IEEE Visual Communications and Image Processing (VCIP) Pub Date : 2019-12-01 DOI: 10.1109/VCIP47243.2019.8965799
Zirui Li, Yuexian Zou, Guoshuai Wang, Jian Zhang
{"title":"Scale-Informed Density Estimation for Dense Crowd Counting","authors":"Zirui Li, Yuexian Zou, Guoshuai Wang, Jian Zhang","doi":"10.1109/VCIP47243.2019.8965799","DOIUrl":"https://doi.org/10.1109/VCIP47243.2019.8965799","url":null,"abstract":"Dense crowd counting (DCC) remains challenging due to the scale variation and occlusion. Several deep learning based DCC methods have achieved the state-of-arts on public datasets. However, experimental results show that the scale variation is still the main factor to hinder the DCC performance. In this work, we propose a scale-informed dense crowd counting method focusing on handling the negative effect caused by scale variation. More specifically, we propose a method to obtain the scale information of the patch from its GT density maps via estimating the mean value of the Gaussian kernel width and then a scale-classifier is deigned and trained accordingly. Moreover, with the estimated scale information, two sub-nets are dedicatedly deigned to learn the density maps for large-scale head patch and small-scale patch separately. Experimental results validate the performance of our proposed method which achieves the best performance on three dense crowd datasets.","PeriodicalId":388109,"journal":{"name":"2019 IEEE Visual Communications and Image Processing (VCIP)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116458976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
VCIP 2019 Conference Events VCIP 2019会议活动
2019 IEEE Visual Communications and Image Processing (VCIP) Pub Date : 2019-12-01 DOI: 10.1109/vcip47243.2019.8965718
{"title":"VCIP 2019 Conference Events","authors":"","doi":"10.1109/vcip47243.2019.8965718","DOIUrl":"https://doi.org/10.1109/vcip47243.2019.8965718","url":null,"abstract":"","PeriodicalId":388109,"journal":{"name":"2019 IEEE Visual Communications and Image Processing (VCIP)","volume":"8 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134034998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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