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56th IEEE International Symposium on Circuits and systems, IScas 2023

作者： Xue, Dongmei Dong, Cunhui Ye, Fan Chen, Hang Kang, Bowei Li, Li Liu, Dong University of Science and Technology of China Cas Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Hefei230027 China

ISBN: (纸本)9781665451093

End-to-end image coding methods based on wavelet-like transform have made great progress in recent years. The most advanced one is iWave++, which adopts multi-level lifting schemes based on convolutional neural networks. However, iWave++ still has many unresolved problems. First, the independent entropy coding of each component makes it impossible to use the correlation between components better. Secondly, additive wavelet transform limits the nonlinear ability of learnable wavelet transform. Moreover, the offline training strategy makes the iWave++ unable to adjust according to the content. In this paper, we propose an improved framework for iWave++ called iWave-Pro. iWavePro is designed with several techniques to overcome the problems mentioned above. These techniques are the joint multi-component Gaussian mixture entropy coding, the affine wavelet-like transform, and the online training. Experimental results show that our method can save 10.73% bit rate compared with iWave++ at the same quality. © 2023 IEEE.

关键词： Convolutional neural networks

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High-Resolution Urban Flows Forecasting With Coarse-Grained Spatiotemporal Data

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2023年第2期4卷 315-327页

作者： Xu, Zhenyi Kang, Yu Cao, Yang Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Department of Automation University of Science and Technology of China Hefei230026 China Chinese Academy of Sciences Key Laboratory of Technology in GeoSpatial Information Processing and Application System Beijing100192 China

Urban flow monitoring and forecasting systems play important roles in smart city management. However, due to the long-lasting and enormous deployment cost of ubiquitous traffic monitoring devices (e.g., loop detectors, traffic video detection), it is very difficult to predict flow in high-resolution (HR) with limited monitoring devices. The existing spatiotemporal network based methods usually predict the urban flow in the same spatial scale without considering the spatial correlations between coarse-grained and fine-grained urban flows. To tackle these issues, we propose a HR spatiotemporal transformer network (HRSTT) to predict fine-grained urban flow. Specifically, residual convolution units are employed to constructs the high-level features of three-view temporal (e.g., closeness, period, and trend) flow data. Then, a transformer block is designed to jointly learn the spatiotemporal dynamic features of each temporal flow with self-attention mechanism. For the external factors (e.g., holidays, weather conditions) are extracted by embedding dense networks, which are fused with high level coarse-grained flow feature maps with gated-fusion scheme. Finally, the coarse-grained fusion feature maps are transferred to the distributional upsampling module, generating the fined-grained flow map of target predicted time. Furthermore, the proposed model is evaluated with several baselines on real-world TaxiBJ datasets, demonstrating the state-of-the-art performance of our approach on the fine-grained urban flow forecasting problem. © 2020 IEEE.

关键词： Deep neural networks

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AUTOMATIC MODULATION RECOGNITION BASED ON SST AND DEEP LEARNING MODEL

AUTOMATIC MODULATION RECOGNITION BASED ON SST AND DEEP LEARN...

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IET International Radar Conference 2023, IRC 2023

作者： Xiang, Jianbing Lv, Xiaolei Han, Bing Zhong, Lihua Fang, Linlin Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in GeoSpatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781839539954

As the electromagnetic environment in battlefields becomes increasingly complex, automatic modulation recognition for noncooperation radiation source signal is becoming vital and challenging. Most previous works were more likely to cause low recognition accuracy with high computational complexity in low signal-to-noise ratio (SNR), and only considered spatial or temporal attributes of signal which ignores the feature interaction. In this paper, we firstly apply synchrosqueezing transform (SST) to extract high resolution and robustness time-frequency feature as one of signal representation, and convert the signal into the in-phase/quadrature (I/Q) components as another signal representation, which enhances the diversity of signal representation. Then, we propose a deep learning model to achieve automatic recognition, which has two streams and each stream consists of the convolutional neural network (CNN), bistatic long short-term memory (BiLSTM) and temporal attention (TAT). It efficiently explores the feature from each signal representation and enhances the key features by the introduction of TAT, furthermore, it implements feature interaction to form a new feature matrix. Finally, experiments and analysis were conducted using the RadioML2016.10a dataset. The experimental results showed that compared to other algorithms, this method has higher robustness and perform better recognition accuracy under low SNR. © The Institution of Engineering & technology 2023.

关键词： Convolutional neural networks

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Light Field Compression Based on Implicit Neural Representation

arXiv

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arXiv 2024年

作者： Wang, Henan Zhu, Hanxin Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

Light field, as a new data representation format in multimedia, has the ability to capture both intensity and direction of light rays. However, the additional angular information also brings a large volume of data. Classical coding methods are not effective to describe the relationship between different views, leading to redundancy left. To address this problem, we propose a novel light field compression scheme based on implicit neural representation to reduce redundancies between views. We store the information of a light field image implicitly in an neural network and adopt model compression methods to further compress the implicit representation. Extensive experiments have demonstrated the effectiveness of our proposed method, which achieves comparable rate-distortion performance as well as superior perceptual quality over traditional methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Redundancy

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A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for Efficient Gridless Line Spectrum Estimation With applications in Tomographic SAR Imaging

A Novel Gradient Descent Least-Squares (GDLSs) Algorithm for...

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作者： Shi, Ruizhe Zhang, Zhe Qiu, Xiaolan Ding, Chibiao University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100190 China Suzhou Aerospace Information Research Institute Suzhou Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou Jiangsu215123 China CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Beijing100190 China

This article presents a novel efficient method for gridless line spectrum estimation problems with single snapshot and sparse signals, namely the gradient descent least-squares (GDLS) method. Conventional single-snapshot (a.k.a. single-measure vector or SMV) line spectrum estimation methods either rely on smoothing techniques that sacrifice the range and/or azimuth resolution or adopt the sparsity constraint and utilize the compressed sensing (CS) method by defining prior grids and resulting in the off-grid problem. Recently emerged atomic norm minimization (ANM) methods achieved gridless SMV line spectrum estimation, but its computational complexity is extremely high;thus, it is practically infeasible in real applications with large problem scales. Our proposed GDLS method reformulates the line spectrum estimations problem into a least-squares (LSs) estimation problem and solves the corresponding objective function via a gradient descent algorithm in an iterative fashion with efficiency. The convergence guarantee, computational complexity, and performance analysis for evenly distributed antenna array cases are discussed in this article. Numerical simulations show that the proposed GDLS algorithm outperforms the state-of-the-art methods, for example, CS and ANM, in terms of estimation performances. It can completely avoid the off-grid problem, and its computational complexity is significantly lower than ANM. Our method has been tested in tomographic SAR (TomoSAR) imaging applications via simulated and real experimental data. Results show the great potential of the proposed method in terms of better cloud point performance and eliminating the gridding effect. © 1980-2012 IEEE.

关键词： Spectrum analysis

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Gifts from Gallery: Advancing Image Retrieval via Unsupervised Asymmetric Feature Fusion

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Wengang Wu, Hui Wang, Min Li, Houqiang University of Science and Technology of China CAS Key Laboratory of Technology in Geospatial Information Processing and Application System Department of Electronic Engineering and Information Science Hefei230027 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230030 China

Asymmetric retrieval systems, characterized by the deployment of models with varying capacities on platforms with differing computational and storage resources, pose a challenge in balancing retrieval efficiency and accuracy. The recent introduction of the Asymmetric Feature Fusion (AFF) paradigm has shown promise by enhancing existing asymmetric retrieval systems through feature fusion on the gallery side. However, its reliance on extensive human-annotated data hinders practical applicability. To this end, we introduce an innovative unsupervised training method tailored for AFF. Leveraging multiple gallery models as feature extractors, our approach exploits similarities among images encoded by these models as pseudolabels. For each gallery model, we calculate its adaptive weight through an in-depth exploration of contextual relationships among ranking list images in its embedding spaces. Subsequently, these weights are utilized to effectively fuse the image similarities encoded by different gallery models into a more robust one. The fused image similarities provide powerful pseudo-supervision for training AFF. Our unsupervised training approach enhances the generality and utility of AFF in real-world scenarios, particularly when labeled data is limited or expensive to obtain. Exhaustive experiments on various landmark retrieval datasets demonstrate the superiority of our method. © 1999-2012 IEEE.

关键词： Multimedia systems

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Spatial Decomposition and Temporal Fusion based Inter Prediction for Learned Video Compression

arXiv

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arXiv 2024年

作者： Sheng, Xihua Li, Li Liu, Dong Li, Houqiang The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Video compression performance is closely related to the accuracy of inter prediction. It tends to be difficult to obtain accurate inter prediction for the local video regions with inconsistent motion and occlusion. Traditional video coding standards propose various technologies to handle motion inconsistency and occlusion, such as recursive partitions, geometric partitions, and long-term references. However, existing learned video compression schemes focus on obtaining an overall minimized prediction error averaged over all regions while ignoring the motion inconsistency and occlusion in local regions. In this paper, we propose a spatial decomposition and temporal fusion based inter prediction for learned video compression. To handle motion inconsistency, we propose to decompose the video into structure and detail (SDD) components first. Then we perform SDD-based motion estimation and SDD-based temporal context mining for the structure and detail components to generate short-term temporal contexts. To handle occlusion, we propose to propagate long-term temporal contexts by recurrently accumulating the temporal information of each historical reference feature and fuse them with short-term temporal contexts. With the SDD-based motion model and long short-term temporal contexts fusion, our proposed learned video codec can obtain more accurate inter prediction. Comprehensive experimental results demonstrate that our codec outperforms the reference software of H.266/VVC on all common test datasets for both PSNR and MS-SSIM. Copyright © 2024, The Authors. All rights reserved.

关键词： Image compression

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Wavelet-Like Transform-Based technology in Response to the Call for Proposals on Neural Network-Based Image Coding

arXiv

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arXiv 2024年

作者： Dong, Cunhui Ma, Haichuan Zhang, Haotian Gao, Changsheng Li, Li Liu, Dong The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Neural network-based image coding has been developing rapidly since its birth. Until 2022, its performance has surpassed that of the best-performing traditional image coding framework – H.266/VVC. Witnessing such success, the IEEE 1857.11 working subgroup initializes a neural network-based image coding standard project and issues a corresponding call for proposals (CfP). In response to the CfP, this paper introduces a novel wavelet-like transform-based end-to-end image coding framework – iWaveV3. iWaveV3 incorporates many new features such as affine wavelet-like transform, perceptual-friendly quality metric, and more advanced training and online optimization strategies into our previous wavelet-like transform-based framework iWave++. While preserving the features of supporting lossy and lossless compression simultaneously, iWaveV3 also achieves state-of-the-art compression efficiency for objective quality and is very competitive for perceptual quality. As a result, iWaveV3 is adopted as a candidate scheme for developing the IEEE Standard for neural-network-based image coding. Copyright © 2024, The Authors. All rights reserved.

关键词： Image coding

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A New Approach to Airborne High Resolution SAR Motion Compensation for Large Trajectory Deviations*

引用

Chinese Journal of Electronics 2023年第4期21卷 764-769页

作者： Dadi MENG Donghui HU Chibiao DING Key Laboratory of Technology in Geospatial Information Processing and Application System Chinese Academy of Sciences Beijing China Institute of Electronics Chinese Academy of Sciences Beijing China

The Two-step algorithm (TSA) is widely used for trajectory deviation compensation of airborne Synthetic aperture radar (SAR), by which most of the motion errors are compensated before Range curve migration compensation (RCMC) and the residual after the RCMC. We found that the RCMC in the presence of the residual motion errors results in additional range shift errors hard to be compensated for. Based on theoretical investigations, this shortage of TSA is reported and a new compensation scheme which greatly alleviates the RCMC induced errors is proposed. Besides, range resampling considering the residual motion errors is very convinent, which, however, is boresome in TSA. The new method is effective to compensate the high resolution SAR systems for large trajectory deviations, which is hard to be achieved by TSA because of the uncompensated errors. Results on the simulated data are provided to demonstrate the effectiveness of the new method.

关键词： Image resolution Azimuth Motion compensation Trajectory Synthetic aperture radar

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iWavePro: An improved framework for iWave++

iWavePro: An improved framework for iWave++

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IEEE International Symposium on Circuits and systems (IScas)

作者： Dongmei Xue Cunhui Dong Fan Ye Hang Chen Bowei Kang Li Li Dong Liu CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China

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