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7th International Conference on Geoinformatics in Sustainable Ecosystem and Society, GSES 2019, and 1st International Conference on geospatial Artificial Intelligence for Urban Computing, GeoAI 2019

作者： Chen, Yuchan Zhang, Zhengdong Yang, Chuanxun Yang, Yang Zhang, Chen Han, Liusheng Yang, Ji Han, Xiangyu Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangzhou510070 China School of Geography South China Normal University Guangzhou510631 China Guangzhou511458 China University of Chinese Academy of Sciences Beijing100049 China State Grid Chengdu Electric Supply Company Chengdu610000 China

ISBN: (纸本)9789811561054

The scale research of landscape pattern is an important basis for the study of spatiotemporal evolution of landscape pattern and the scientific and reasonable allocation of landscape pattern. This paper takes the middle and upper reaches of Meijiang river as the research area. Combined with spatial grain size analysis and extent size analysis, the spatial scale effect was studied to select the optimal research scale in this research area. The results show that most of the appropriate grain size of landscape pattern indexes are in range of 90–150 m, and the optimal grain size scale is 150 m in Middle and Upper Reaches of Meijiang River Basin. At the grain size scale of 150 m, the spatial self-correlation of landscape pattern index over 50% is the highest at the extent size of 300 m, and the optimal extent size scale is 300 m in this basin. This paper determines the optimal scale of landscape pattern in the middle and upper reaches of Meijiang river basin, which is of great significance to the ecological balance and sustainable development of the basin. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Rivers

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Reinforced Bit Allocation under Task-Driven Semantic Distortion Metrics

Reinforced Bit Allocation under Task-Driven Semantic Distort...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Jun Shi 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: (数字)9781728133201

ISBN: (纸本)9781728133218

Rapid growing intelligent applications require optimized bit allocation in image/video coding to support specific task-driven scenarios such as detection, classification, segmentation, etc. Some learning-based frameworks have been proposed for this purpose due to their inherent end-to-end optimization mechanisms. However, it is still quite challenging to integrate these task-driven metrics seamlessly into traditional hybrid coding framework. To the best of our knowledge, this paper is the first work trying to solve this challenge based on reinforcement learning (RL) approach. Specifically, we formulate the bit allocation problem as a Markovian Decision Process (MDP) and train RL agents to automatically decide the quantization parameter (QP) of each coding tree unit (CTU) for HEVC intra coding, according to the task-driven semantic distortion metrics. This bit allocation scheme can maximize the semantic level fidelity of the task, such as classification accuracy, while minimizing the bit-rate. We also employ gradient class activation map (Grad-CAM) and Mask R-CNN tools to extract task-related importance maps to help the agents make decisions. Extensive experimental results demonstrate the superior performance of our approach by achieving 43.1% to 73.2% bit-rate saving over the anchor of HEVC under the equivalent task-related distortions.

关键词： Task analysis Distortion Bit rate Image coding Semantics Encoding

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Deep Local and Global Spatiotemporal Feature Aggregation for Blind Video Quality Assessment

Deep Local and Global Spatiotemporal Feature Aggregation for...

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IEEE Visual Communications and Image Processing (VCIP)

作者： Wei Zhou 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: (数字)9781728180687

ISBN: (纸本)9781728180694

In recent years, deep learning has achieved promising success for multimedia quality assessment, especially for image quality assessment (IQA). However, since there exist more complex temporal characteristics in videos, very little work has been done on video quality assessment (VQA) by exploiting powerful deep convolutional neural networks (DCNNs). In this paper, we propose an efficient VQA method named Deep SpatioTemporal video Quality assessor (DeepSTQ) to predict the perceptual quality of various distorted videos in a no-reference manner. In the proposed DeepSTQ, we first extract local and global spatiotemporal features by pre-trained deep learning models without fine-tuning or training from scratch. The composited features consider distorted video frames as well as frame difference maps from both global and local views. Then, the feature aggregation is conducted by the regression model to predict the perceptual video quality. Finally, experimental results demonstrate that our proposed DeepSTQ outperforms state-of-the-art quality assessment algorithms.

关键词： Quality assessment Video recording Feature extraction Spatiotemporal phenomena Databases Indexes Deep learning

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Edge-Guided Panoramic Video Stitching with Limited Overlap

Edge-Guided Panoramic Video Stitching with Limited Overlap

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

作者： Xie, Chaoyu Chen, Xuejin 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)9781728123455

Video stitching remains a challenging problem in computer vision. In this paper, we propose a novel edge-guided method to stitch multiple videos that have small overlapped regions. Our algorithm consists of three steps: (1) spherical projection of the input video frames based on camera calibration, (2) edge detection and edge-guided feature matching for video registration, and (3) seam optimization to eliminate distortions and ghosts in the composited panoramic videos. The experimental results and user studies demonstrate that our method is robust to videos that have small overlapped regions and produces more visually pleasing panoramic videos than state-of-the-art techniques. © 2019 IEEE.

关键词： Edge detection

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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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Article dgfnet: Dual gate fusion network for land cover classification in very high-resolution images

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Remote Sensing 2021年第18期13卷

作者： Guo, Yongjie Wang, Feng Xiang, Yuming You, Hongjian The Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art performance on land cover classification thanks to their outstanding nonlinear feature extraction abil-ity. DCNNs are usually designed as an encoder–decoder architecture for the land cover classification in very high-resolution (VHR) remote sensing images. The encoder captures semantic representation by stacking convolution layers and shrinking image spatial resolution, while the decoder restores the spatial information by an upsampling operation and combines it with different level features through a summation or skip connection. However, there is still a semantic gap between different-level features;a simple summation or skip connection will reduce the performance of land-cover classification. To overcome this problem, we propose a novel end-to-end network named Dual Gate Fusion Network (DGFNet) to restrain the impact of the semantic gap. In detail, the key of DGFNet consists of two main components: Feature Enhancement Module (FEM) and Dual Gate Fusion Module (DGFM). Firstly, the FEM combines local information with global contents and strengthens the feature representation in the encoder. Secondly, the DGFM is proposed to reduce the semantic gap between different level features, effectively fusing low-level spatial information and high-level semantic information in the decoder. Extensive experiments conducted on the LandCover dataset and the ISPRS Potsdam dataset proved the effectiveness of the proposed network. The DGFNet achieves state-of-art performance 88.87% MIoU on the LandCover dataset and 72.25% MIoU on the ISPRS Potsdam dataset. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Convolution

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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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Improving Compression Artifact Reduction via End-to-End Learning of Side information

Improving Compression Artifact Reduction via End-to-End Lear...

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IEEE Visual Communications and Image Processing (VCIP)

作者： Haichuan Ma 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

We propose to improve neural network-based compression artifact reduction by transmitting side information for the neural network. The side information consists of artifact descriptors that are obtained by analyzing the original and compressed images in the encoder. In the decoder, the received descriptors are used as additional input to a well-designed conditional post-processing neural network. To reduce the transmission overhead, the entire model is optimized under the rate-distortion constraint via end-to-end learning. Experimental results show that introducing the side information greatly improves the ability of the post-processing neural network, and improves the rate-distortion performance.

关键词： Image coding Neural networks Decoding Training Feature extraction Computational modeling Transform coding

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Deep Learning-Based Nonlinear Transform for HEVC Intra Coding

Deep Learning-Based Nonlinear Transform for HEVC Intra Codin...

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IEEE Visual Communications and Image Processing (VCIP)

作者： Kun Yang 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 the hybrid video coding framework, transform is adopted to exploit the dependency within the input signal. In this paper, we propose a deep learning-based nonlinear transform for intra coding. Specifically, we incorporate the directional information into the residual domain. Then, a convolutional neural network model is designed to achieve better decorrelation and energy compaction than the conventional discrete cosine transform. This work has two main contributions. First, we propose to use the intra prediction signal to reduce the directionality in the residual. Second, we present a novel loss function to characterize the efficiency of the transform during the training. To evaluate the compression performance of the proposed transform, we implement it into the High Efficiency Video Coding reference software. Experimental results demonstrate that the proposed method achieves up to 1.79% BD-rate reduction for natural videos.

关键词： Transforms Discrete cosine transforms Neural networks Transform coding Video coding Image coding Decoding

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M-LVC: Multiple Frames Prediction for Learned Video Compression

M-LVC: Multiple Frames Prediction for Learned Video Compress...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jianping Lin Dong Liu Houqiang Li 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)9781728171685

ISBN: (纸本)9781728171692

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, respectively. To compensate for the compression error of the auto-encoders, we further design a MV refinement network and a residual refinement network, taking use of the multiple reference frames as well. All the modules in our scheme are jointly optimized through a single rate-distortion loss function. 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.

关键词： Video compression Image coding Motion compensation Entropy Encoding Motion estimation Transforms

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