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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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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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Multi-scale grouped dense network for VVC intra coding

arXiv

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arXiv 2020年

作者： Li, Xin Sun, Simeng Zhang, Zhizheng Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

Versatile Video Coding (H.266/VVC) standard achieves better image quality when keeping the same bits than any other conventional image codec, such as BPG, JPEG, and etc. However, it is still attractive and challenging to improve the image quality with high compression ratio on the basis of traditional coding techniques. In this paper, we design the multi-scale grouped dense network (MSGDN) to further reduce the compression artifacts by combining the multi-scale and grouped dense block, which are integrated as the post-process network of VVC intra coding. Besides, to improve the subjective quality of compressed image, we also present a generative adversarial network (MSGDN-GAN) by utilizing our MSGDN as generator. Across the extensive experiments on validation set, our MSGDN trained by MSE losses yields the PSNR of 32.622 on average with teams"IMC" and"haha" at the bit-rate of 0.15 in Low-rate track. Moreover, our MSGDN-GAN could achieve the better subjective performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Image quality

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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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Region Normalization for Image Inpainting

arXiv

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arXiv 2019年

作者： Yu, Tao Guo, Zongyu Jin, Xin Wu, Shilin Chen, Zhibo Li, Weiping Zhang, Zhizheng Liu, Sen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China China

Feature Normalization (FN) is an important technique to help neural network training, which typically normalizes features across spatial dimensions. Most previous image inpainting methods apply FN in their networks without considering the impact of the corrupted regions of the input image on normalization, e.g. mean and variance shifts. In this work, we show that the mean and variance shifts caused by full-spatial FN limit the image inpainting network training and we propose a spatial region-wise normalization named Region Normalization (RN) to overcome the limitation. RN divides spatial pixels into different regions according to the input mask, and computes the mean and variance in each region for normalization. We develop two kinds of RN for our image inpainting network: (1) Basic RN (RN-B), which normalizes pixels from the corrupted and uncorrupted regions separately based on the original inpainting mask to solve the mean and variance shift problem;(2) Learnable RN (RN-L), which automatically detects potentially corrupted and uncorrupted regions for separate normalization, and performs global affine transformation to enhance their fusion. We apply RN-B in the early layers and RN-L in the latter layers of the network respectively. Experiments show that our method outperforms current state-of-the-art methods quantitatively and qualitatively. We further generalize RN to other inpainting networks and achieve consistent performance improvements. Our code is available at https://***/geekyutao/RN. Copyright © 2019, The Authors. All rights reserved.

关键词： Pixels

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SwinIQA: Learned Swin Distance for Compressed Image Quality Assessment

arXiv

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arXiv 2022年

作者： Liu, Jianzhao Li, Xin Peng, Yanding Yu, Tao Chen, Zhibo CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China China

Image compression has raised widespread interest recently due to its significant importance for multimedia storage and transmission. Meanwhile, a reliable image quality assessment (IQA) for compressed images can not only help to verify the performance of various compression algorithms but also help to guide the compression optimization in turn. In this paper, we design a full-reference image quality assessment metric SwinIQA to measure the perceptual quality of compressed images in a learned Swin distance space. It is known that the compression artifacts are usually non-uniformly distributed with diverse distortion types and degrees. To warp the compressed images into the shared representation space while maintaining the complex distortion information, we extract the hierarchical feature representations from each stage of the Swin Transformer. Besides, we utilize cross attention operation to map the extracted feature representations into a learned Swin distance space. Experimental results show that the proposed metric achieves higher consistency with human's perceptual judgment compared with both traditional methods and learning-based methods on CLIC datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Image compression

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Curved-path SAR geolocation error analysis based on BP algorithm 38

Curved-path SAR geolocation error analysis based on BP algor...

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38th Annual IEEE International geoscience and Remote Sensing Symposium, IGARSS 2018

作者： Liu, Junbin Qiu, Xiaolan Huang, Lijia Ding, Chibiao Liu, Ming University of Chinese Academy of Sciences China Key Laboratory of Technology in Geo-spatial Information Processing and Application System CAS China Institute of Electronics Chinese Academy of Sciences China National Disaster Reduction Center Ministry of Civil Affairs NDRCC China

ISBN: (纸本)9781538671504

The theoretical modeling and analysis of SAR location error play an important role in SAR system design and error source budget. Existing SAR geolocation error models are mainly implicit, which are not easy to do analysis, especially in the curved-path case. In this paper, a theoretical explicit model of the relationship between image geolocation error and the path measurement error is established for curved-path SAR, based on BP imaging algorithm. Simulations are given which verify the correctness of the model. The explicit model and the analysis results provide an effective reference for understanding and budgeting the system-level geometric location error for curved-path SAR, such as geoSAR. © 2018 IEEE

关键词： geology Imaging Synthetic aperture radar Trajectory Mathematical model Analytical models Simulation

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caspatch: A SAR image simulation code to support ATR applications

CASpatch: A SAR image simulation code to support ATR applica...

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2009 Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009

作者： Zhang, Rui Hong, Jun Ming, Feng Key Laboratory of Spatial Information Processing and Application System Technology CAS China Institute of Electronics Chinese Academy of Sciences Beijing China Graduate University of the Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781424427314

In this paper, A SAR image simulation code of 3D complex targets named caspatch is introduced. This code is based on the high frequency technique of shooting and bouncing rays (SBR). The original purpose to design the code is for SAR automatic target recognition (ATR) applications, but it can also be used for RCS prediction and high resolution range profile (HRRP) generation. A GB-SAR indoor experiment is used to validate the caspatch system. The simulation results of complex targets show that high resolution, full polarimetry and wide bandwidth SAR images can be obtained via caspatch, which means our code can support multiple SAR ATR applications. What's more, an improved PolSAR ATR algorithm is also proposed in this paper. The full PolSAR ATR experiment based on simulated data is firstly reported in this paper, and higher recognition rate is achieved comparing with the single PolSAR result. ©2009 IEEE.

关键词： 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

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.

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