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arXiv 2021年

作者： Fu, Jun Hou, Chen Zhou, Wei Xu, Jiahua Chen, Zhibo The CAS Key Laboratory of Technology in GeoSpatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

In no-reference 360-degree image quality assessment (NR 360IQA), graph convolutional networks (GCNs), which model interactions between viewports through graphs, have achieved impressive performance. However, prevailing GCN-based NR 360IQA methods suffer from three main limitations. First, they only use high-level features of the distorted image to regress the quality score, while the human visual system (HVS) scores the image based on hierarchical features. Second, they simplify complex high-order interactions between viewports in a pairwise fashion through graphs. Third, in the graph construction, they only consider spatial locations of viewports, ignoring its content characteristics. Accordingly, to address these issues, we propose an adaptive hypergraph convolutional network for NR 360IQA, denoted as AHGCN. Specifically, we first design a multi-level viewport descriptor for extracting hierarchical representations from viewports. Then, we model interactions between viewports through hypergraphs, where each hyperedge connects two or more viewports. In the hypergraph construction, we build a location-based hyperedge and a content-based hyperedge for each viewport. Experimental results on two public 360IQA databases demonstrate that our proposed approach has a clear advantage over state-of-the-art full-reference and no-reference IQA models. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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Target Amplitude Characteristic Analysis With Neighborhood information Using C-band Circular SAR Data

Target Amplitude Characteristic Analysis With Neighborhood I...

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IEEE International Conference on Radar

作者： Xiaoyang Yue Fei Teng Yun Lin Wen Hong Key Laboratory of Spatial Information Processing and Application System Technology Areospace Information Research Institute Chinese Academy of Sciences Beijing China School of Electronic Information Engineering North China University of Technology Beijing China

ISBN: (纸本)9781665468893

The circular synthetic aperture radar (SAR) can observe the experimental scene from all angles. The backscatter intensity of the target in the scene can be obtained. Different targets in the imaging scene show different amplitude characteristics. For example, significant targets such as buildings and oil tanks show strong response characteristics at certain angles, and secondly, weak response characteristics for targets is shown such as grass and ground. In this article, the omnidirectional scattering intensity data of the target obtained from the radar is used to analyze the amplitude characteristics, and the model is established for different targets to obtain the final result. The fuzzy C-means (FCM) model combined with neighborhood information is used to analyze the target amplitude characteristics. The amplitude characteristics of the target are divided into two categories: strong response and weak response for analysis. The initial two types of cluster centers are set for iteration and finally the target amplitude characteristic reference values at different angles are obtained. C-band circular SAR data is used to validate our method. As a result, the amplitude characteristics of the whole scene using membership degree can be described.

关键词： Analytical models C-band Buildings Scattering Imaging Fuel storage Radar scattering

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Stereo Matching of VHR Remote Sensing Images via Bidirectional Pyramid Network

Stereo Matching of VHR Remote Sensing Images via Bidirection...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Rongshu Tao Yuming Xiang Hongjian You Key Laboratory of Technology in Geo-spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Science Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Stereo matching, an essential step in 3D reconstruction, still faces unignorable problems due to the very high resolution and complex structures of remote sensing images. Especially in occluded areas of high buildings and untextured areas of waters and woods, precise disparity estimation has become a difficult but important task. In this paper, we propose a novel method based on the pyramid stereo matching network to solve the aforementioned problems. Inspired by the classical optical flow estimation framework, we adopt the forward-backward consistency assumption to improve the accuracy. Moreover, we improve the construction of cost volume since the traditional deep-learning networks only work well for positive disparities and the disparity ranges in remote sensing images vary a lot. The proposed network is compared with two baselines. The experimental results show that our proposed method outperforms two baselines in terms of average endpoint error (EPE) and the fraction of erroneous pixels(D1), and the improvements in occluded areas are significant.

关键词： Estimation Remote sensing Optical sensors Optical imaging Three-dimensional displays Buildings Satellites

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An Energy Efficient Carry-Free Inner Product Unit

An Energy Efficient Carry-Free Inner Product Unit

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Wen Yan Miloš D. Ercegovac Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing China University of California Los Angeles CA USA

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

An energy efficient truncated inner product unit is proposed in this paper. The proposed unit is pipelined and processes the m pairs of n-bit operands in serial, so that only one unit is required and it can be reused in each recurrence. Truncated inner product is produced so that no accumulation is needed for the lower part. The array reduction unit is bit-level pipelined to improve throughput and reduce the initial delay. A two-level [4:2] adder is proposed to accumulate redundant vectors with non-uniform input arrival. The proposed inner product is compared with other truncated inner product units using fully merged arithmetic, partially merged arithmetic and the counter based inner product for different precision and vector length. Our proposed inner product unit has significant reduction in area, power and energy.

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An autofocus network for multi-channel phase errors with application to tomoSAR imaging

An autofocus network for multi-channel phase errors with app...

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IET International Radar Conference (IRC 2023)

作者： Muhan Wang Silin Gao Zhe Zhang Xiaolan Qiu Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing 100190 People's Republic of China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou 215123 People's Republic of China

ISBN: (数字)9781837240982

Synthetic aperture radar (SAR) tomography (TomoSAR) has garnered significant attention due to its capability for three-dimensional reconstruction. Compressed sensing (CS) methods are widely employed to address the TomoSAR inversion challenge. Nevertheless, practical applications reveal phase errors among different channels, resulting in defocusing and blurring when relying solely on CS for 3D reconstruction. Current state-of-the-art autofocus techniques suffer from prohibitive computational complexity, limiting their applicability to large-scale 3D imaging. In pursuit of efficient TomoSAR 3-D autofocusing, we proposed ASAMP-Net, an innovative deep unfolding network. Operating within a two-step framework, each layer comprises two stages: phase error estimation and iterative scattering coefficient reconstruction using the sparse adaptive matching pursuit (SAMP) algorithm. Additionally, phase error estimation is obtained through mathematical derivation, while challenges associated with fixed sparsity and limited efficiency in conventional methods are mitigated through deep learning techniques. Simulation experiments and real data validation affirm the effectiveness and superiority of the proposed method.

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ANISOTROPIC SCATTERING DETECTION FOR CHARACTERIZING POLARIMETRIC CIRCULAR SAR MULTI-ASPECT SIGNATURES

ANISOTROPIC SCATTERING DETECTION FOR CHARACTERIZING POLARIME...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Yang Li Yun Lin Wen Hong Ran Xu Zhimin Zhuo Qiang Yin Beijing Institute of Electronic System Engineering Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Science Beijing China Beijing University of Chemical Technology Beijing China

Circular synthetic aperture radar (CSAR) can provide distinctive multi-aspect anisotropic scattering signatures. However, it is impossible to retain the anisotropic signatures in a SAR image that combines all the subapertures coherently or incoherently. In this letter, we propose a polarimetric CSAR anisotropic scattering detection framework to characterize multi-aspect and fully polarimetric SAR signatures of point-like and distributed targets. We applied this framework to quantify and rank media polarimetric scattering dissimilarity over all aspects and to determine whether the most different one shows anisotropy by use of constant false alarm rate (CFAR) detection. Furthermore, we demonstrated the monotonicity of CFAR detection function and incorporated this function to decrease the complexity of the anisotropic scattering test. Our algorithm was validated and applied to a set of airborne P-band fully polarimetric circular SAR data acquired by the Institute of Electronics, Chinese Academy of Science (iecas). The results indicate the framework can retain anisotropic scattering and extract a series of new multi-aspect polarimetric SAR signatures for terrain classification.

关键词： Scattering Apertures Synthetic aperture radar Covariance matrices Radar polarimetry Image color analysis Optical imaging

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No-reference quality assessment for 360-degree images by analysis of multi-frequency information and local-global naturalness

arXiv

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arXiv 2021年

作者： Zhou, Wei Xu, Jiahua Jiang, Qiuping Chen, Zhibo CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China School of Information Science and Engineering Ningbo University Ningbo315211 China

360-degree/omnidirectional images (OIs) have achieved remarkable attentions due to the increasing applications of virtual reality (VR). Compared to conventional 2D images, OIs can provide more immersive experience to consumers, benefitting from the higher resolution and plentiful field of views (FoVs). Moreover, observing OIs is usually in the head mounted display (HMD) without references. Therefore, an efficient blind quality assessment method, which is specifically designed for 360-degree images, is urgently desired. In this paper, motivated by the characteristics of the human visual system (HVS) and the viewing process of VR visual contents, we propose a novel and effective no-reference omnidirectional image quality assessment (NR OIQA) algorithm by Multi-Frequency information and Local-Global Naturalness (MFILGN). Specifically, inspired by the frequency-dependent property of visual cortex, we first decompose the projected equirectangular projection (ERP) maps into wavelet subbands by using discrete Haar wavelet transform (DHWT). Then, the entropy intensities of low-frequency and high-frequency subbands are exploited to measure the multi-frequency information of OIs. Besides, except for considering the global naturalness of ERP maps, owing to the browsed FoVs, we extract the natural scene statistics (NSS) features from each viewport image as the measure of local naturalness. With the proposed multi-frequency information measurement and local-global naturalness measurement, we utilize support vector regression (SVR) as the final image quality regressor to train the quality evaluation model from visual quality-related features to human ratings. To our knowledge, the proposed model is the first no-reference quality assessment method for 360-degreee images that combines multi-frequency information and image naturalness. Experimental results on two publicly available OIQA databases demonstrate that our proposed MFILGN outperforms state-of-the-art full-reference (FR) a

关键词： Helmet mounted displays

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Copyright protection in named data networking

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International Conference on Wireless Communications and Signal processing (WCSP)

作者： Xiaobin Tan Zifei Zhou Cliff Zou Yukun Niu Xin Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China China Department of Electrical Engineering and Computer Science University of Central Florida USA

As a new Internet architecture, Named Data Networking (NDN) decouples location from the data itself to achieve security, scalability, and mobility. Although router-side data caching used in NDN reduces data acquisition delay, it introduces a new copyright protection challenge: how to prevent unauthorized users to retrieve data cached in routers that are out of the control of its publisher? Current approaches that rely on a common encryption key among authorized users cannot protect copyright well since if one authorized user secretly leaks the key, we cannot tell who has leaked the key out. In this paper, we present a split-based scheme to solve this copyright protection problem for large-sized data. The data is split into a large part that could be cached in routers for all users to retrieve, and a small part that is unique for each authorized user. This scheme exploits the fact that in the bit-wise OR operation, both bit 0 and bit 1 can OR with 1 to generate the same result of bit 1. The analysis of our scheme shows that it has a good performance in terms of copyright protection, data retrieval efficiency, and overhead.

关键词： Copyright protection Encryption Watermarking Internet Computer architecture Receivers

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Efficient background picture coding for videos obtained from static cameras

Efficient background picture coding for videos obtained from...

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IEEE Visual Communications and Image processing (VCIP)

作者： Fangdong Chen Li Li Dong Liu Houqiang Li Zhuoyi Lv Haitao Yang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China Huawei Technologies Co. Ltd. Shenzhen China

With the exponential growth of surveillance videos, conference videos and sports videos, videos with static cameras present an unprecedented challenge for high-efficiency video coding technology. The existing schemes developed for these videos mostly encode the background as the long-term reference (LTR) to further improve the coding efficiency. However, since the bit allocation of the long-term background reference is not intensively studied, the coding efficiency is still unsatisfactory. Based on the stability analysis of the video content, an efficient background picture coding algorithm for videos obtained from static cameras, which is embedded with the basic unit level bit allocation, is proposed in this paper. Experimental results reveal that on top of the default mode in HEVC, our method offers the performance with 10.8% BD-rate reduction on average. Compared with the state-of-the-art algorithm, it still outperforms for kinds of test sequences with negligible increases of computational complexity in both encoder and decoder.

关键词： Encoding Videos Cameras Distortion Stability analysis Video coding Delays

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Object Segmentation-Assisted Inter Prediction for Versatile Video Coding

arXiv

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arXiv 2024年

作者： Li, Zhuoyuan Yuan, Zikun Li, Li Liu, Dong Tang, Xiaohu Wu, Feng CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China Information Security and National Computing Grid Laboratory Southwest Jiaotong University Chengdu610031 China

In modern video coding standards, block-based inter prediction is widely adopted, which brings high compression efficiency. However, in natural videos, there are usually multiple moving objects of arbitrary shapes, resulting in complex motion fields that are difficult to represent compactly. This problem has been tackled by more flexible block partitioning methods in the Versatile Video Coding (VVC) standard, but the more flexible partitions require more overhead bits to signal and still cannot be made arbitrarily shaped. To address this limitation, we propose an object segmentation-assisted inter prediction method (SAIP), where objects in the reference frames are segmented by some advanced technologies. With a proper indication, the object segmentation mask is translated from the reference frame to the current frame as the arbitrary-shaped partition of different regions without any extra signal. Using the segmentation mask, motion compensation is separately performed for different regions, achieving higher prediction accuracy. The segmentation mask is further used to code the motion vectors of different regions more efficiently. Moreover, the segmentation mask is considered in the joint rate-distortion optimization for motion estimation and partition estimation to derive the motion vector of different regions and partition more accurately. The proposed method is implemented into the VVC reference software, VTM version 12.0. Experimental results show that the proposed method achieves up to 1.98%, 1.14%, 0.79%, and on average 0.82%, 0.49%, 0.37% BD-rate reduction for common test sequences, under the Low-delay P, Low-delay B, and Random Access configurations, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Video signal processing

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