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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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Fast genetic multi-operator image retargeting

Fast genetic multi-operator image retargeting

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IEEE Visual Communications and Image processing (VCIP)

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

ISBN: (纸本)9781509053179

Content-aware image retargeting has attracted substantial research interests in the related research community. However, so far there is still no method can adequately preserve important image contents and structure well without introducing conspicuous visible deformation in a relatively short period of time. To address this problem, we propose a Fast Genetic Multi-operator (FGM) method which integrates multiple retargeting operators. To improve the efficiency, FGM method utilizes Genetic Algorithms (GAs) to reach the optimal operator ratio, which adopts saliency and Gray-Level Co-occurrence Matrix (GLCM) as its energy function. FGM method not only can well preserve salient contents and structure, but also can greatly reduce the computational complexity. Experimental results demonstrated that our method outperforms state-of-art image retargeting methods.

关键词： Genetic algorithms Biological cells Genetics Distortion Computational complexity Visualization

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SDM: Semantic Distortion Measurement for Video Encryption

SDM: Semantic Distortion Measurement for Video Encryption

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International Conference on Automatic Face and Gesture Recognition

作者： Yongquan Hu Wei Zhou Shuxin Zhao Zhibo Chen Weiping Li CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

Semantic information is important in video encryption. However, existing image quality assessment (IQA) methods, such as the peak signal to noise ratio (PSNR), are still widely applied to measure the encryption security. Generally, these traditional IQA methods aim to evaluate the image quality from the perspective of visual signal rather than semantic information. In this paper, we propose a novel semantic-level full-reference image quality assessment (FR-IQA) method named Semantic Distortion Measurement (SDM) to measure the degree of semantic distortion for video encryption. Then, based on a semantic saliency dataset, we verify that the proposed SDM method outperforms state-of-the-art algorithms. Furthermore, we construct a Region Of Semantic Saliency (ROSS) video encryption system to demonstrate the effectiveness of our proposed SDM method in the practical application.

关键词： Semantics Encryption Distortion Distortion measurement Visualization Object segmentation

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Improving triplet-wise training of convolutional neural network for vehicle re-identification

Improving triplet-wise training of convolutional neural netw...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yiheng Zhang Dong Liu Zheng-Jun Zha CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781509060689

Vehicle re-identification (re-id) plays an important role in the automatic analysis of the drastically increasing urban surveillance videos. Similar to the other image retrieval problems, vehicle re-id suffers from the difficulties caused by various poses of vehicles, diversified illuminations, and complicated environments. Triplet-wise training of convolutional neural network (CNN) has been studied to address these challenges, where the CNN is adopted to automate the feature extraction from images, and the training adopts triplets of (query, positive example, negative example) to capture the relative similarity between them to learn representative features. The traditional triplet-wise training is weakly constrained and thus fails to achieve satisfactory results. We propose to improve the triplet-wise training at two aspects: first, a stronger constraint namely classification-oriented loss is augmented with the original triplet loss; second, a new triplet sampling method based on pairwise images is designed. Our experimental results demonstrate the effectiveness of the proposed methods that achieve superior performance than the state-of-the-arts on two vehicle re-id datasets, which are derived from real-world urban surveillance videos.

关键词： Training Feature extraction Streaming media Videos Sampling methods Licenses Cameras

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Robust Beamformer based on Magnitude Response Constraint and Sparse Constraint

Robust Beamformer based on Magnitude Response Constraint and...

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2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Lei, Songlin Qiu, Xiaolan DIng, Chibiao Zhang, Yueting Aerospace Information Research Institute Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System CAS China Institute of Electronics Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781728123455

Beamformer with magnitude response constraint can flexibly control the response region by specified beamwidth and response ripple, which has a significant performance against steering vector mismatch. However, a high sidelobe level of the beam is accompanied, resulting in performance degradation. To solve this problem, a novel robust beamformer based on magnitude response constraint and sparse constraint is proposed. This method adds the sparse constraint, that is, Lp-norm to the beamformer with magnitude response constraint, then the non-convex cost function can be formulated as a semidefinite programming (SDP) problem, finally the matrix decomposition theory is used to get the array weight vector. Simulation results demonstrate that the proposed method can not only produce large controlled region against steering vector mismatch and reduce the sidelobe level of the beampattern, but also achieve good performance in Signal to Interference plus Noise Ratio (SINR) enhancement. © 2019 IEEE.

关键词： Vectors

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M-LVC: Multiple frames prediction for learned video compression

arXiv

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arXiv 2020年

作者： Lin, Jianping Liu, Dong Li, Houqiang Wu, Feng Cas Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027

We propose an end-to-end learned video compression scheme for low-latency scenarios. Previous methods are limited in using the previous one frame as reference. Our method introduces the usage of the previous multiple frames as references. In our scheme, the motion vector (MV) field is calculated between the current frame and the previous one. With multiple reference frames and associated multiple MV fields, our designed network can generate more accurate prediction of the current frame, yielding less residual. Multiple reference frames also help generate MV prediction, which reduces the coding cost of MV field. We use two deep auto-encoders to compress the residual and the MV, respec-tively. To compensate for the compression error of the autoencoders, we further design a MV refinement network and a residual refinement network, taking use of the multiple ref-erence frames as well. All the modules in our scheme are jointly optimized through a single rate-distortion loss func-tion. We use a step-by-step training strategy to optimize the entire scheme. Experimental results show that the proposed method outperforms the existing learned video compression methods for low-latency mode. Our method also performs better than H.265 in both PSNR and MS-SSIM. Our code and models are publicly available. Copyright © 2020, The Authors. All rights reserved.

关键词： Image compression

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Chain Code-Based Occupancy Map Coding for Video-Based Point Cloud Compression

Chain Code-Based Occupancy Map Coding for Video-Based Point ...

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IEEE Visual Communications and Image processing (VCIP)

作者： Runyu Yang Ning Yan Li Li Dong Liu Feng Wu CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

In video-based point cloud compression (V-PCC), occupancy map video is utilized to indicate whether a 2-D pixel corresponds to a valid 3-D point or not. In the current design of V-PCC, the occupancy map video is directly compressed losslessly with High Efficiency Video Coding (HEVC). However, the coding tools in HEVC are specifically designed for natural images, thus unsuitable for the occupancy map. In this paper, we present a novel quadtree-based scheme for lossless occupancy map coding. In this scheme, the occupancy map is firstly divided into several coding tree units (CTUs). Then, the CTU is divided into coding units (CUs) recursively using a quadtree. The quadtree partition is terminated when one of the three conditions is satisfied. Firstly, all the pixels have the same value. Secondly, the pixels in the CU only have two kinds of values and they can be separated by a continuous edge whose endpoints lie on the side of the CU. The continuous edge is then coded using chain code. Thirdly, the CU reaches the minimum size. This scheme simplifies the design of block partitioning in HEVC and designs simpler yet more effective coding tools. Experimental results show significant reduction of bit-rate and complexity compared with the occupancy map coding scheme in V-PCC. In addition, this scheme is also very efficient to compress the semantic map.

关键词： Encoding Three-dimensional displays Image coding Semantics Markov processes geometry Copper

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Multiscale Progressive Image Compression Network Guided by Learnable Just Noticeable Distortion

Multiscale Progressive Image Compression Network Guided by L...

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IEEE Visual Communications and Image processing (VCIP)

作者： Xin Jin Runchun Ye Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781538644591;9781538644584

One key challenge to the learning-based image compression is that adaptive bit allocation is crucial for compression effectiveness but can hardly be trained into a neural network. Hereby, in this work, We presents an end-to-end trainable image compression framework, named Multi-scale Progressive Network (MPN) to achieve spatially variant bit allocation and rate control through the guidance of a novel learnable just noticeable distortion (JND) map. Specifically, MPN's encoder archives multi-scale feature representation through a three-branched structure. Each branch employs an independent feature extraction strategy for the specific receptive field and merge progressively under the guidance of corresponding learnable JND maps that generated by our proposed Bit-Allocation sub-Network (BAN), which make MPN focus on the areas where attract the human visual system (HVS) and preserve more texture of the image during the compression procedure. Finally, a hybrid objective function is introduced to further make MPN more efficient and mimic the discriminative characteristics of the human visual system (HVS). Experiments show that MPN significantly outperforms traditional JPEG, JPEG 2000 and few state-of-art learning-based methods by multi-scale structural similarity (MS-SSIM) index, and has the ability to produce the much better visual result with rich textures, sharp edges, and fewer artifacts.

关键词： Image coding Feature extraction Transform coding Distortion Image reconstruction Bit rate Visualization

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First assessment of Noise-Equivalent Sigma-Zero in GF3-02 TOPSAR mode with sea surface wind speed retrieval

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Acta Oceanologica Sinica 2023年第10期42卷 84-96页

作者： Junxin Yang Lihua Zhong Xinzhe Yuan Xiaochen Wang Bing Han Yuxin Hu Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Chinese Academy of SciencesBeijing 100190China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100049China National Satellite Ocean Application Service Beijing 100081China

Gaofen-3-02(GF3-02)is the first C-band synthetic aperture radar(SAR)satellite with terrain observation with progressive scans of SAR(TOPSAR)imaging mode in China,which plays an essential role in marine environment *** the weak scattering characteristics of the ocean,the system thermal noise superimposed on SAR images has significant interference,especially in cross-polarization ***-Equivalent Sigma-Zero(NESZ)is a measure of the sensitivity of the radar to areas of low *** NESZ is defined to be the scattering cross-section coefficient of an area which contributes a mean level in the image equal to the signal-independent additive noise *** TOPSAR,NESZ exhibits the shape of the SAR scanning gain curve in the azimuth and the shape of the antenna pattern in the ***,the accurate measurement of NESZ plays a vital role in the application of spaceborne SAR sea surface cross-polarization *** paper proposes a theoretical calculation method for the NESZ curve in GF3-02 TOPSAR mode based on SAR noise inner calibration data and the imaging algorithm.A method for correcting the error existing in the theoretical curve of NESZ is also proposed according to the relationship between sea surface backscattering and wind speed and the same characteristics of target scattering in the overlapping area of adjacent *** to assessment with wide-swath TOPSAR cross-polarization data,the GF3-02 TOPSAR mode has a very low thermal noise level,which is better than−33 dB at the edge of each beam,and controlled below−38 dB at the center of the *** two-dimensional reference curves of the NESZ of each beam are provided to the GF3-02 TOPSAR *** discussing the relationship between normalized radar cross section(NRCS)and wind speed,we provide a formula for NRCS related to wind speed and radar incidence *** with the NRCS derived from this formula and the NESZ-subtracted NRCS of SAR images,the bias is−0.0048

关键词： GF3-02 TOPSAR Noise-Equivalent Sigma-Zero(NESZ) cross-polarization normalized radar cross section(NRCS) sea surface wind

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Interpreting the Latent Space of GANs via Correlation Analysis for Controllable Concept Manipulation

arXiv

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arXiv 2020年

作者： Li, Ziqiang Tao, Rentuo Niu, Hongjing Li, Bin CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems University of Science and Technology of China Anhui China

Generative adversarial nets (GANs) have been successfully applied in many fields like image generation, inpainting, super-resolution and drug discovery, etc., by now, the inner process of GANs is far from been understood. To get deeper insight of the intrinsic mechanism of GANs, in this paper, a method for interpreting the latent space of GANs by analyzing the correlation between latent variables and the corresponding semantic contents in generated images is proposed. Unlike previous methods that focus on dissecting models via feature visualization, the emphasis of this work is put on the variables in latent space, i.e. how the latent variables affect the quantitative analysis of generated results. Given a pretrained GAN model with weights fixed, the latent variables are intervened to analyze their effect on the semantic content in generated images. A set of controlling latent variables can be derived for specific content generation, and the controllable semantic content manipulation be achieved. The proposed method is testified on the datasets Fashion-MNIST and UT Zappos50K, experiment results show its effectiveness. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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