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arXiv 2020年

作者： Guo, Zongyu Wu, Yaojun Feng, Runsen Zhang, Zhizheng Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

In this paper, we present our image compression framework designed for CLIC 2020 competition. Our method is based on Variational AutoEncoder (VAE) architecture which is strengthened with residual structures. In short, we make three noteworthy improvements here. First, we propose a 3-D context entropy model which can take advantage of known latent representation in current spatial locations for better entropy estimation. Second, a light-weighted residual structure is adopted for feature learning during entropy estimation. Finally, an effective training strategy is introduced for practical adaptation with different resolutions. Experiment results indicate our image compression method achieves 0.9775 MS-SSIM on CLIC validation set and 0.9809 MS-SSIM on test set. Copyright © 2020, The Authors. All rights reserved.

关键词： Image compression

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3-D Context Entropy Model for Improved Practical Image Compression

3-D Context Entropy Model for Improved Practical Image Compr...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Zongyu Guo Yaojun Wu Runsen Feng Zhizheng Zhang Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

关键词： Context modeling Solid modeling Image coding Entropy Training Transforms Image resolution

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Action recognition with novel high-level pose features

Action recognition with novel high-level pose features

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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Jiayi Fan Zhengjun Zha Xinmei Tian University of Science and Technology of China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System

ISBN: (纸本)9781509015535

Recently high-level pose features (HLPF) have been shown to be efficient for action recognition in joint-annotated tasks. However, the relative positions between pairs of joints in actual situations and the spatio-temporal information are not considered in constructing HLPF. To tackle their problems, we propose a set of novel high-level pose features (NHLPF). Specifically, considering that the distances between adjacent pairs of joints usually remain unchanged, we propose a horizontally relative position feature and a vertically relative position feature. In addition, a joint inner product feature is proposed to code the spatial information among each triplet of joints. To code temporal information, we calculate the trajectories of the above-mentioned three types of features as corresponding trajectory features. Furthermore, to combine the spatial and temporal information, we present a joint energy change feature, which is designed using observations of the magnitude and direction of the force between joints. We evaluate our NHLPF on a benchmark dataset. The results show that NHPLF are superior features for action recognition.

关键词： Trajectory Pose estimation Detectors Joining processes Hip Force Three-dimensional displays

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Learned video compression with feature-level residuals

arXiv

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arXiv 2020年

作者： Feng, Runsen Wu, Yaojun Guo, Zongyu Zhang, Zhizheng Jin, Xin Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

In this paper, we present an end-to-end video compression network for P-frame challenge on CLIC. We focus on deep neural network (DNN) based video compression, and improve the current frameworks from three aspects. First, we notice that pixel space residuals is sensitive to the prediction errors of optical flow based motion compensation. To suppress the relative influence, we propose to compress the residuals of image feature rather than the residuals of image pixels. Furthermore, we combine the advantages of both pixel-level and feature-level residual compression methods by model ensembling. Finally, we propose a step-by-step training strategy to improve the training efficiency of the whole framework. Experiment results indicate that our proposed method achieves 0.9968 MS-SSIM on CLIC validation set and 0.9967 MS-SSIM on test set. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Learned Video Compression with Feature-level Residuals

Learned Video Compression with Feature-level Residuals

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Runsen Feng Yaojun Wu Zongyu Guo Zhizheng Zhang Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

关键词： Image coding Training Optical imaging Adaptive optics Motion compensation Optical distortion Decoding

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GUID-based mobile visual communication using NDN mechanism

GUID-based mobile visual communication using NDN mechanism

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IEEE Visual Communications and Image processing (VCIP)

作者： Yuanzun Zhang Xiaobin Tan Hao Liu Weiping Li CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

ISBN: (纸本)9781509053179

With the explosive growth in the number of mobile terminals, the demand for visual communication with mobility is increasing. However, traditional solutions for mobility over IP network cannot always meet the demand of satisfying visual communication. Named Data Networking (NDN) is a new communication model that aims to replace IP model brings a different background to mobile visual communication problems. In this paper, we take advantage of the NDN model to realize seamless mobile visual communication. We introduce a delegate with calculation functions and a globally unique identifier (GUID) which can provide native identity indication into the NDN mechanism. The use of GUID benefits real-time applications like visual communication and further works with the delegate to decrease unnecessary routing update. We also specify the naming rule and design a FIB+ to support seamless mobile visual communication. To test the performance of our solutions, we build a proof-of-concept prototype and run experiments on it. The experiments demonstrate that our solution can provide real-time video communication with seamless mobility experience.

关键词： Routing Mobile communication Visual communication IP networks Artificial neural networks Mobile computing Real-time systems

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Hybrid digital-analog scheme for video transmission over fading channel

Hybrid digital-analog scheme for video transmission over fad...

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International Symposium on Circuits and systems

作者： Jian Shen Lei Yu Houqiang Li CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

ISBN: (纸本)9781479953424

In this paper, we consider video communication over fading channel, where the perfect instantaneous channel state information (CSI) is available at both sender and receiver. Most of existing coding schemes are inefficient in this communication scenario. The reason is that for digital coding scheme, it has high coding efficiency but unavoidably leads to the cliff effect;while for analog scheme, it has graceful video quality variation with channel varying, but has low coding efficiency. Hence, to integrate the advantages of digital coding and analog coding, we propose a hybrid digital-analog (HDA) scheme. In our scheme, we have adopted adaptive power allocation and adaptive forward error coding (FEC) in digital part to accommodate instantaneous channel quality. The evaluation results show that the proposed HDA scheme outperforms ParCast (a state-of-the-art analog scheme) 0.3~2.2dB under the channel Signal-to-Noise Ratio (SNR) from 3dB to 20dB.

关键词： Encoding Resource management Signal to noise ratio Receivers Decoding Forward error correction Fading channels

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Compressive tracking with adaptive color feature selection and foreground modeling

Compressive tracking with adaptive color feature selection a...

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IEEE Visual Communications and Image processing (VCIP)

作者： Tianqi Zheng Chao Xie Wengang Zhou Houqiang Li CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

ISBN: (纸本)9781509053179

Part-based trackers have achieved promising performance in many tracking tasks. However, most part-based trackers use the same feature representation for all parts and simply combine them together to form an integral representation for the tracking target. It may not guarantee that all parts of the tracking target can well distinguish the foreground from the background. Better performance is expected by exploring different feature representations on different parts of the tracking target. In this paper, following the framework of the classic Compressive Tracker (CT), we model each part of the target adaptively by using a multi-dimensional color representation. By using color name, we select the color feature presentation that best distinguishes the foreground from background. In order to better handle deformation and illumination change, we use multi-Gaussian to model different appearance changes of the tracking target. Both qualitative and quantitative evaluations demonstrate that the proposed method makes a consistent performance improvement compared with the conventional Compressive Tracker on tracking benchmark dataset. Besides, it also outperforms many state-of-the-art trackers while running at averagely 20 frames per second (FPS).

关键词： Target tracking Image color analysis Color Gaussian distribution Computed tomography Adaptation models

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An interactive system for low-poly illustration generation from images using adaptive thinning

An interactive system for low-poly illustration generation f...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yiting Ma Xuejin Chen Yu Bai University of Science and Technology of China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System

ISBN: (纸本)9781509060689

Low-poly style illustrations, which have 3D abstract appearance, have become a popular stylish recently. Most previous methods require special knowledges in 3D modeling and need tedious interactions. We present an interactive system for non-expert users to easily manipulate the low-poly style illustration. Our system consists of two parts: vertex sampling and mesh rendering. In the vertex sampling stage, we extract a set of candidate points from the image and rank them according to their importance of structure preserving using adaptive thinning. Based on the pre-ranked point list, the user can select an arbitrary number of vertices for the triangle mesh construction. In the mesh rendering stage, we optimize triangle colors to create stereo-looking low-polys. We also provide three tools for exible modication of vertex numbers, color contrast, and local region emphasis. The experiment results demonstrate that our system outperforms state-of-the-art method via simple user interactions.

关键词： Image color analysis Rendering (computer graphics) Tools Approximation error Interactive systems Three-dimensional displays Image edge detection

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SAR Automatic Target Recognition Based on Slow Feature Analysis

SAR Automatic Target Recognition Based on Slow Feature Analy...

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2015 IEEE International Conference on Progress in Informatics and Computing(PIC 2015)

作者： Rentuo Tao Bin Li CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

This paper presents a new Synthetic Aperture Radar（SAR） Automatic Target Recognition（ATR） method based on slow feature analysis. Slow feature analysis（SFA） is a method for learning invariant or slowly varying features from multi-dimensional input signal. The SFA-based SAR ATR system does not require any pre-processing, such as filtering or pose estimation of the image. The performance of the method is evaluated via three classification experiments on Moving and Stationary Target Acquisition and Recognition（MSTAR） database. The experiment results show the effectiveness of the proposed method on SAR ATR problem.

关键词： Synthetic Aperture Rader Slow Feature Analysis Nonlinear expansion Sphering Principal Component Analysis

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