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24th ACM Multimedia Conference, MM 2016

作者： Shen, Xu Tian, Xinmei He, Anfeng Sun, Shaoyan Tao, Dacheng CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui230027 China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia

ISBN: (纸本)9781450336031

Convolutional neural networks (CNNs) have achieved stateof-the-art results on many visual recognition tasks. However, current CNN models still exhibit a poor ability to be invariant to spatial transformations of images. Intuitively, with sufficient layers and parameters, hierarchical combinations of convolution (matrix multiplication and nonlinear activation) and pooling operations should be able to learn a robust mapping from transformed input images to transform-invariant representations. In this paper, we propose randomly transforming (rotation, scale, and translation) feature maps of CNNs during the training stage. This prevents complex dependencies of specific rotation, scale, and translation levels of training images in CNN models. Rather, each convolutional kernel learns to detect a feature that is generally helpful for producing the transforminvariant answer given the combinatorially large variety of transform levels of its input feature maps. In this way, we do not require any extra training supervision or modification to the optimization process and training images. We show that random transformation provides significant improvements of CNNs on many benchmark tasks, including small-scale image recognition, large-scale image recognition, and image retrieval. © 2016 ACM.

关键词： Mathematical transformations

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3-D context entropy model for improved practical image compression

arXiv

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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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A Multicast Architecture of SVC Streaming Over OpenFlow Networks

A Multicast Architecture of SVC Streaming Over OpenFlow Netw...

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IEEE Global Communications Conference

作者： Enzhong Yang Yongyi Ran Shuangwu Chen Jian Yang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

ISBN: (纸本)9781479935130

In video streaming service, multicast mode is a promising way to complement unicast delivery of content, since it deliveries the video content to a broad range of receivers. It is considered as an efficient scheme to reduce redundant traffic in the networks. In this paper, we propose an OpenFlow based architecture for implementing Scalable Video Coding (SVC) multicast streaming. It enables in-network identifying, processing and manipulating the media streams, which makes prompt bitrate adaptation possible in response to network fluctuations. The heterogeneous video quality demand from the heterogeneous device also can be satisfied by customizing the multicast traffic through a centralized OpenFlow controller. We implement a testbed following the proposed architecture in our campus. With OpenFlow, we deploy IP multicast in a new way without Internet Group Management Protocol (IGMP) or any multicast addresses. Experiments implemented on the testbed show that our approach can provide a flexible and controllable video multicast streaming service and improve the usage of bandwidth resource in the condition of guaranteeing multicast receivers' Quality of Experience (QoE).

关键词： IP networks multicast communication telecommunication traffic video coding video streaming IP multicast OpenFlow networks bandwidth resource usage improvement bitrate adaptation centralized OpenFlow controller controllable video multicast streaming service heterogeneous device heterogeneous video quality demand media streams multicast SVC streaming architecture multicast receivers quality-of-experience multicast traffic network fluctuations redundant traffic reduction scalable video coding multicast streaming unicast content delivery Control systems Ports (Computers) Receivers Servers Static VAr compensators Streaming media

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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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Accelerate Neural Image Compression with Channel-Adaptive Arithmetic Coding

Accelerate Neural Image Compression with Channel-Adaptive Ar...

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IEEE International Symposium on Circuits and systems

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

We have witnessed the revolutionary progress of learned image compression despite a short history of this field. Some challenges still remain such as computational complexity that prevent the practical application of learning-based codecs. In this paper, we address the issue of heavy time complexity from the view of arithmetic coding. Prevalent learning-based image compression scheme first maps the natural image into latent representations and then conduct arithmetic coding on quantized latent maps. Previous arithmetic coding schemes define the start and end value of the arithmetic codebook as the minimum and maximum of the whole latent maps, ignoring the fact that the value ranges in most channels are shorter. Hence, we propose to use a channel-adaptive codebook to accelerate arithmetic coding. We find that the latent channels have different frequency-related characteristics, which are verified by experiments of neural frequency filtering. Further, the value ranges of latent maps are different across channels which are relatively image-independent. The channel-adaptive characteristics allow us to establish efficient prior codebooks that cover more appropriate ranges to reduce the runtime. Experimental results demonstrate that both the arithmetic encoding and decoding can be accelerated while preserving the rate-distortion performance of compression model.

关键词： Image coding Runtime Filtering Rate-distortion Acceleration Integrated circuit modeling Time complexity

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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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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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Learned video compression with feature-level residuals

arXiv

引用

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

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

引用

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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