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An openstack-based flexible video transcoding framework in live

AIP Conference Proceedings 2017年第1期1864卷

作者： Qisen Shi Jianxin Song Key Laboratory of Image Processing and Image Communication Nanjing University of Posts and Telecommunications Nanjing 210000 China

With the rapid development of mobile live business, transcoding HD video is often a challenge for mobile devices due to their limited processing capability and bandwidth-constrained network connection. For live service providers, it’s wasteful for resources to delay lots of transcoding server because some of them are free to work sometimes. To deal with this issue, this paper proposed an Openstack-based flexible transcoding framework to achieve real-time video adaption for mobile device and make computing resources used efficiently. To this end, we introduced a special method of video stream splitting and VMs resource scheduling based on access pressure prediction,which is forecasted by an AR model.

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Interactive object segmentation using iterative adjustable graph cut

Interactive object segmentation using iterative adjustable g...

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IEEE Visual communications and image processing (VCIP)

作者： Ran Shi Zhi Liu Yinzhu Xue Xiang Zhang School of Communication and Information Engineering Shanghai University Shanghai China Key Laboratory of Advanced Display and System Application Ministry of Education Shanghai University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

Interactive object segmentation is widely used for extracting any user-interested objects from natural images. A common problem with many interactive segmentation approaches is that the object segmentation quality is degraded due to inaccurate object/background seeds provided by the user. This paper proposes an iterative adjustable graph cut to efficiently solve this problem. First, object/background seeds are initialized based on the object segmentation result obtained with the user-specified scribbles as the interactive input. Then, an iterative seed adjustment scheme is exploited to correct inaccurate seeds and extract new suitable seeds via graph cut, in which the balancing weight between energy terms are adaptively updated to protect stable seeds and speedup the iteration process. Finally, suitable seeds are obtained and graph cut is used to segment the objects. Experimental results demonstrate the better segmentation performance of our approach even if user provides rather rough seeds.

关键词： Object segmentation image segmentation Reliability Iterative methods Educational institutions image color analysis Error analysis

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Speed up HEVC encoder by precoding with H.264

Speed up HEVC encoder by precoding with H.264

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Asia-Pacific Signal and Information processing Association Annual Summit and Conference (APSIPA)

作者： Fangshun Mul Li Song Zhenyi Luo Xiangwen Wang Xiaokang Yang Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China School of Electronics and Information Engineering Shanghai University of Electric Power Shanghai China

ISBN: (纸本)9781479942503

The new video coding standard, High Efficiency Video Coding (HEVC), was developed to replace H264/AVC and achieves high efficiency by introducing a new coding structure in adoption of coding unit (CU), prediction unit (PU) and transform unit (TU). However, it also imposes great computation burden on the mode decision of encoders. In this paper, a conceptual HEVC encoder architecture with cascaded H264/AVC encoder is proposed to speed up the CTU splitting process of HEVC encoder. First, the input frame is precoded with an efficient H264/AVC encoder to gather the partition and prediction information of H264/AVC macroblocks (MBs). Then based on the gathered information, CU and PU modes are carefully screened during HEVC encoding. Two schemes are proposed to reduce the CU and PU candidate modes to accelerate the Coding Tree Unit (CTU) splitting process. At last, the HEVC encoder tries all combinations of candidate CU and PU mode to determine the optimal CU, PU and TU mode by computing the rate-distortion (R-D) cost. The experimental results on highly-optimized open source codecs - x264 and x265 show that the proposed framework can effectively reduce encoding time with negligible loss in terms of PSNR and bitrate.

关键词： Video coding Encoding PSNR Bit rate Decision support systems Abstracts Standards

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Intra-Guided Dual-Energy Material Decomposition Method for Dual-Source CBCT Systems 46

Intra-Guided Dual-Energy Material Decomposition Method for D...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Zhong, Xinyun Lyu, Tianling Wu, Zhan Gao, Wei Xi, Yan Zhao, Wei Zhu, Wentao Chen, Yang Zhejiang Lab Hangzhou China First-Imaging Tech. Shanghai China Beihang University Beijing China Southeast University School of Computer Science and Engineering Laboratory of Image Science and Technology Nanjing China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China

ISBN: (纸本)9798350371499

Dual-energy CBCT imaging plays a crucial role in advanced imaging applications due to its ability to quantify material components. Although there are multiple established systems for dual-energy imaging, they often come with high deployment costs. This study investigates the feasibility of dual-energy material decomposition on a less expensive dual-source CBCT system. Due to the specific geometry of dual-source CBCT, cone-beam (CB) artifacts seriously degrade the material decomposition quality when the direct inversion method is adopted. In this paper, a novel dual-source intra-guided material decomposition framework is proposed to suppress CB artifacts in the material maps. Firstly, a dual-source two-pass algorithm is developed for artifact pre-correction during image reconstruction. Then, the structure of small cone-angle regions from one X-ray source is utilized to guide cone beam artifact removal in large cone-angle regions from another source. Finally, direct matrix inverse material decomposition is applied to the filtered image. Extensive experiments have been conducted on the authentic human phantom and demonstrate that the proposed framework achieves excellent performance in cone beam artifact reduction compared to other methods, and significantly enhances the quality of the material maps. © 2024 IEEE.

关键词： image reconstruction

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An Active Landing Recovery Method for Quadrotor UAV: Localization, Tracking and Buffering Landing

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IFAC-PapersOnLine 2023年第2期56卷 3366-3372页

作者： Yongkang Xu Zhihua Chen Shoukun Wang Junzheng Wang National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing CO 100081 P.R.China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology School of Information Engineering Nanchang Hangkong University Nanchang CO 330063 P.R. China

This paper proposes a principle of fully autonomous ground mobile landing recovery of Unmanned Aerial Vehicles (UAV) for the problems of relatively fixed landing point, passive recovery, poor flexibility, and environmental adaptability, which mainly includes localization, landing point tracking, and buffering landing for quadrotor UAV. Firstly, aiming at the problem that it is difficult to accurately obtain the position of a UAV in dynamic mobile landing recovery, a target location method based on Asynchronous Multisensor Information Fusion(AMIF) and servo turntable focus tracking is proposed. Secondly, to achieve fast and high-precision tracking of UAVs, a tracking control strategy of an independently driven landing recovery system and a Stewart six-degree of freedom platform is proposed. Then, to solve the problems of large impact force and center of gravity instability in the landing process of UAV, a stationarity control algorithm based on model prediction and a compliance control algorithm based on adaptive variable impedance are designed to achieve active compliance control while adjusting the position and attitude of the receiving surface in real-time. Finally, a quadrotor unmanned landing and recovery experimental platform is built to verify the feasibility of the ground mobile landing and recovery strategy proposed in this paper and the effectiveness of the control algorithm.

关键词： Quadrotor UAV Mobile Autonomous recovery Target localization Falling point tracking Buffering landing

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Performance evaluation of H.265/MPEG-HEVC encoders for 4K video sequences

Performance evaluation of H.265/MPEG-HEVC encoders for 4K vi...

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Asia-Pacific Signal and Information processing Association Annual Summit and Conference (APSIPA)

作者： Qingxiong Huangyuan Li Song Zhengyi Luo Xiangwen Wang Yanan Zhao Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai China Instiuue of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China School of Electronics and Information Engineering Shanghai University of Electric Power Shanghai China

ISBN: (纸本)9781479942503

The H.265/MPEG-HEVC is the latest video coding standard, which achieves an increase of about 50% in coding efficiency compared to its predecessor H.264/MPEG-AVC. Ever since H.265/MPEG-HEVC was designed to replace almost all existing H.264/ MPEG-AVC codecs, high-resolution video coding beyond High Definition (4K, 8K, etc.) has drawn more attention. On the other hand, it's well known that reference implementation of HEVC codec, HM, acts an important role during standardization, particularly for evaluation of rate distortion performance of different tools. However, HM is far from a practical codec because of very slow coding speed even on modern multi-core computers. Up to now except for HM few comparisons are known about both the coding performance and the coding speed of practical HEVC encoders for high resolution video sequences. To address this issue, this paper conducts a comprehensive evaluation of latest high performance H.265/MPEG-HEVC encoders, including the open source encoder-x265 and the commercial encoder-DivX265, based on default parameters and a new open 4K video database. Furthermore, latest HM and x264 are also included for performance anchors. The experimental results show DivX265 provides average bit-rate savings of 4.79% relative to HM while x265 with default preset achieves an average reduction of 3.21% in terms of BD-BR saving. In addition, different presets of x265 make a good tradeoff between coding speed and R-D performance while DivX265 is almost as fast as x265 ultrafast preset. We believe such evaluation information could provide a more comprehensive picture of state-of-the-art H.265/MPEG-HEVC encoders.

关键词： Transform coding Encoding Decision support systems Codecs Video coding Abstracts Standards

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RGB-D based action recognition with light-weight 3D convolutional networks

arXiv

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arXiv 2018年

作者： Zhang, Haokui Li, Ying Wang, Peng Liu, Yu Shen, Chunhua Shaanxi Provincial Key Lab of Speech and Image Information Processing School of Computer Science Northwestern Polytechnical University Xi’an710129 China School of Computer Science University of Adelaide AdelaideSA5005 Australia

Different from RGB videos, depth data in RGB-D videos provide key complementary information for tristimulus visual data which potentially could achieve accuracy improvement for action recognition. However, most of the existing action recognition models solely using RGB videos limit the performance capacity. Additionally, the state-of-the-art action recognition models, namely 3D convolutional neural networks (3D-CNNs) contain tremendous parameters suffering from computational inefficiency. In this paper, we propose a series of 3D lightweight architectures for action recognition based on RGB-D data. Compared with conventional 3D-CNN models, the proposed lightweight 3D-CNNs have considerably less parameters involving lower computation cost, while it results in favorable recognition performance. Experimental results on two public benchmark datasets show that our models can approximate or outperform the state-of-the-art approaches. Specifically, on the RGB+DNTU (NTU) dataset, we achieve 93.2% and 97.6% for cross-subject and cross-view measurement, and on the Northwestern-UCLA Multiview Action 3D (N-UCLA) dataset, we achieve 95.5% accuracy of cross-view. Copyright © 2018, The Authors. All rights reserved.

关键词： Convolution

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GANHead: Towards Generative Animatable Neural Head Avatars

arXiv

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arXiv 2023年

作者： Wu, Sijing Yan, Yichao Li, Yunhao Cheng, Yuhao Zhu, Wenhan Gao, Ke Li, Xiaobo Zhai, Guangtao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Alibaba Group China

To bring digital avatars into people’s lives, it is highly demanded to efficiently generate complete, realistic, and animatable head avatars. This task is challenging, and it is difficult for existing methods to satisfy all the requirements at once. To achieve these goals, we propose GANHead (Generative Animatable Neural Head Avatar), a novel generative head model that takes advantages of both the fine-grained control over the explicit expression parameters and the realistic rendering results of implicit representations. Specifically, GANHead represents coarse geometry, fine-gained details and texture via three networks in canonical space to obtain the ability to generate complete and realistic head avatars. To achieve flexible animation, we define the deformation filed by standard linear blend skinning (LBS), with the learned continuous pose and expression bases and LBS weights. This allows the avatars to be directly animated by FLAME [22] parameters and generalize well to unseen poses and expressions. Compared to state-of-the-art (SOTA) methods, GANHead achieves superior performance on head avatar generation and raw scan fitting. Copyright © 2023, The Authors. All rights reserved.

关键词： Textures

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Learning from Noisy labels with Coarse-to-Fine Sample Credibility Modeling

arXiv

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arXiv 2022年

作者： Zhang, Boshen Li, Yuxi Tu, Yuanpeng Peng, Jinlong Wang, Yabiao Wu, Cunlin Xiao, Yang Zhao, Cairong YouTu Lab Tencent Shanghai China Tongji University Shanghai China Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China

Training deep neural network (DNN) with noisy labels is practically challenging since inaccurate labels severely degrade the generalization ability of DNN. Previous efforts tend to handle part or full data in a unified denoising flow via identifying noisy data with a coarse small-loss criterion to mitigate the interference from noisy labels, ignoring the fact that the difficulties of noisy samples are different, thus a rigid and unified data selection pipeline cannot tackle this problem well . In this paper, we first propose a coarse-to-fine robust learning method called CREMA, to handle noisy data in a divide-and-conquer manner. In coarse-level, clean and noisy sets are firstly separated in terms of credibility in a statistical sense. Since it is practically impossible to categorize all noisy samples correctly, we further process them in a fine-grained manner via modeling the credibility of each sample. Specifically, for the clean set, we deliberately design a memory-based modulation scheme to dynamically adjust the contribution of each sample in terms of its historical credibility sequence during training, thus alleviating the effect from noisy samples incorrectly grouped into the clean set. Meanwhile, for samples categorized into the noisy set, a selective label update strategy is proposed to correct noisy labels while mitigating the problem of correction error. Extensive experiments are conducted on benchmarks of different modality, including image classification (CIFAR, Clothing1M etc) and text recognition (IMDB), with either synthetic or natural semantic noises, demonstrating the superiority and generality of CREMA. © 2022, CC BY.

关键词： Deep neural networks

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Multimodal dimensional affect recognition using deep bidirectional long short-term memory recurrent neural networks

Multimodal dimensional affect recognition using deep bidirec...

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International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII

作者： Ercheng Pei Le Yang Dongmei Jiang Hichem Sahli NPU-VUB Joint AVSP Research Lab Shaanxi Key Lab on Speech and Image Information Processing 127 Youyi Xilu Xi'an China Deptartment of Electronics & Informatics (ETRO) Belgium Interuniversity Microelectronics Centre (IMEC) Brussel Belgium

In this paper we propose the deep bidirectional long short-term memory recurrent neural network (DBLSTM-RNN) based single modal and multi-modal affect recognition frameworks. In the single modal framework DBLSTM with moving average (MA), audio or visual features are input into the DBLSTM-RNN model, whose output estimations of a dimension are smoothed by the moving average filter. After the smoothed estimations are expanded to the frame rate of the ground truth labels, another MA is adopted for smoothing the final results. In the multi-modal framework DBLSTM-DBLSTM-MA, the initial estimations from the audio and visual modalities via the first layer of DBLSTM-RNNs are input into a second layer of DBLSTM-RNN, whose outputs are smoothed by MA. The smoothed estimations are then expanded to the frame rate of the ground truth labels and smoothed again by another MA. Affect recognition experiments are carried out on the training set and development set of the AVEC2014 database, results show that the proposed DBLSTM-MA framework outperforms linear regression, support vector regression (SVR), and BLSTM for single modal dimension estimation. For audio visual multi-modal affect recognition, DBLSTM-DBLSTM-MA obtains better or comparable performance than the state of the art results in the competition of AVEC2014, with the average correlation coefficient (COR) reaches 0.599 on the Freeform database, 0.630 on the Northwind database, and 0.615 on the Freeform-Northwind database.

关键词： Hidden Markov models Visualization Estimation Feature extraction Emotion recognition Visual databases Speech recognition

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