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检索条件"任意字段=IEEE Visual Communications and Image Processing Conference"

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Semantic Preprocessor for image Compression for Machines

Semantic Preprocessor for Image Compression for Machines

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Mingyi Yang Luis Herranz Fei Yang Luka Murn Marc Gorriz Blanch Shuai Wan Fuzheng Yang Marta Mrak School of Telecommunications Engineering Xidian University Xi’an China Computer Vision Center Barcelona Spain Universitat Autònoma de Barcelona Barcelona Spain BBC Research & Development London UK School of Electronics and Information Northwestern Polytechnical University Xi’an China

visual content is being increasingly transmitted and consumed by machines rather than humans to perform automated content analysis tasks. In this paper, we propose an image preprocessor that optimizes the input image for machine consumption prior to encoding by an off-the-shelf codec designed for human consumption. To achieve a better trade-off between the accuracy of the machine analysis task and bitrate, we propose leveraging pre-extracted semantic information to improve the preprocessor’s ability to accurately identify and filter out task-irrelevant information. Furthermore, we propose a two-part loss function to optimize the preprocessor, consisted of a rate-task performance loss and a semantic distillation loss, which helps the reconstructed image obtain more information that contributes to the accuracy of the task. Experiments show that the proposed preprocessor can save up to 48.83% bitrate compared with the method without the preprocessor, and save up to 36.24% bitrate compared to existing preprocessors for machine vision.

关键词： visualization image coding Machine vision Semantics Bit rate Signal processing Information filters

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A Novel Cross-Component Context Model for End-to-End Wavelet image Coding

A Novel Cross-Component Context Model for End-to-End Wavelet...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Anna Meyer André Kaup Multimedia Communications and Signal Processing Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

In contrast to traditional compression techniques performing linear transforms, the latent space of popular compressive autoencoders is obtained from a learned nonlinear mapping and hard to interpret. In this paper, we explore a promising alternative approach for neural compression, with an autoencoder whose latent space represents a nonlinear wavelet decomposition. Previous work has shown that neural wavelet image coding can outperform HEVC. However, the approach codes color components independently, thereby ignoring inter-component dependencies. Hence, we propose a novel crosscomponent context model (CCM). With CCM, the entropy model for the chroma latent space can be conditioned on previously coded components exploiting correlations in the learned wavelet space. The proposed CCM outperforms the baseline model with average Bjøntegaard delta rate savings of 2.6 % and 1.6 % for the Kodak and Tecnick image sets. Also, our method is competitive with VVC and learning-based methods.

关键词： Wavelet transforms Video coding Training Learning systems image coding Correlation Video compression

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Modulation format identification of optical communication system based on multi-scale inception network

Modulation format identification of optical communication sy...

引用

2021 Asia communications and Photonics conference, ACP 2021

作者： Liang, Meng Li, Yongle School of Electronic Engineering Xi'an University of Posts and Telecommunications Xi'an710121 China

We propose a new convolutional neural network based on multi-scale inception, combined with constellation image processing, to improve the accuracy of optical network modulation format identification and reduce the tr... 详细信息

ISBN: (纸本)9781957171005

关键词： image enhancement

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Anomaly Detection in Hyperspectral images with High Dimensional Model Representation

Anomaly Detection in Hyperspectral Images with High Dimensio...

引用

ieee Signal processing and communications Applications (SIU)

作者： Derya Bodur Evrim Korkmaz Özay Burcu Tunga Yazılım Mühendisliði Matematik Mühendisliði Bahçeşehir Üniversitesi İstanbul Teknik Üniversitesi İstanbul Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Hyperspectral imaging (HSI) is an important imaging technology that enables the high sensitivity and accuracy analysis of wide areas through its multispectral band structure. Anomaly detection in hyperspectral images can be defined as the detection of pixels that do not belong to the background and whose spectral properties are unknown. In this study, a method based on High Dimensional Model Representation (HDMR) is proposed for detecting anomalies in hyperspectral images. The effectiveness of the proposed method has been tested on a sample image and compared with commonly used anomaly detection methods. The HDMR-based anomaly detection algorithm has shown to be more effective in suppressing the background compared to other methods by making anomaly pixels more visible.

关键词： Band structures Sensitivity Accuracy Signal processing algorithms Imaging Signal processing Anomaly detection

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The Effect of Surface Temperature on Infrared Reflections in a Thermal image

The Effect of Surface Temperature on Infrared Reflections in...

引用

ieee Signal processing and communications Applications (SIU)

作者： Duru Bektaş Melike Girgin Tayfun Akgül Elektronik ve Haberleşme Mühendisligi Bölümü İstanbul Teknik Üniversitesi İstanbul Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Thermal imaging is an important technique for various fields. While processing thermal images, infrared reflections seen on the camera may cause problems. Most important reason why is that thermal reflections occur differently than visible light reflections. This study investigated infrared reflections on objects with different emissivity values and the infrared radiation reflected from the same object at varying temperatures, utilizing a thermal camera. The findings reinforced that objects with higher emissivity values reflected infrared radiation more efficiently. It was also observed that infrared reflection diminished as an object cooled and increased with rising temperature. These outcomes highlight the critical importance of considering an object’s emissivity value, its temperature, and the environmental conditions meticulously when determining its true temperature with a thermal camera.

关键词： Signal processing Cameras Reflection Surface treatment

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Enhancing image Matching Between Digital Twin and Real-World Through Cross-Domain Geo-Localization Methods

Enhancing Image Matching Between Digital Twin and Real-World...

引用

Metaverse Computing, Networking and Applications (MetaCom), ieee International conference on

作者： Soorim Yang Jaeho Kim Underwater Surveillance Technology Research and Development LIG Nex1 Seongnam Republic of Korea Department of Information and Communications Engineering Sejong University Seoul Republic of Korea

ISBN: (数字)9798331515997

ISBN: (纸本)9798331516000

Digital twins, virtual replicas of real-world entities, have garnered significant attention for their ability to synchronize with reality and facilitate simulations. As digital twins generate datasets akin to those from their real-world counterparts, the emergence of diverse image datasets has fueled the demand for advanced image-based matching techniques. However, inherent disparities exist between the representations of digital twin datasets and their real-world counterparts, posing challenges for traditional image-based matching methods. In this paper, we explore and experimentally analyze the performance of existing cross-domain image geo-localization methods between digital twins and the real world. We applied visual feature matching and semantic feature matching techniques to datasets collected from the digital twin and real-world. Our experiments demonstrate that both methods achieve excellent performance, with the semantic feature matching technique showing superior results. This suggests that using semantic information may yield better performance in matching between digital twin and real-world domains, which have different appearances. The evaluation results confirm the potential of using semantic feature matching approaches for cross-domain matching between digital twins and the real world.

关键词： visualization Metaverse image matching Semantics Digital twins Synchronization

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Classification-Based Dynamic Network for Efficient Super-Resolution

Classification-Based Dynamic Network for Efficient Super-Res...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Qi Wang Weiwei Fang Meng Wang Yusong Cheng Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China

Deep neural networks (DNNs) based approaches have achieved superior performance in single image super-resolution (SR). To obtain better visual quality, DNNs for SR are generally designed with massive computation overhead. To accelerate network inference under resource constraints, we propose a classification-based dynamic network for efficient super-resolution (CDNSR), which combines the classification and SR networks in a unified framework. Specifically, CDNSR decomposes a large image into a number of image-patches, and uses a classification network to categorize them into different classes based on the restoration difficulty. Each class of image-patches will be handled by the SR network that corresponds to the difficulty of this class. In particular, we design a new loss to trade off between the computational overhead and the reconstruction quality. Besides, we apply contrastive learning based knowledge distillation to guarantee the performance of SR networks and the quality of reconstructed images. Extensive experiments show that CDNSR significantly outperforms the other SR networks and backbones on image quality and computational overhead.

关键词： Knowledge engineering image quality visualization Superresolution Neural networks Dynamic scheduling image restoration

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Cloud Detection System for UAV Sense and Avoid: Discussion of Suitable Algorithms

Cloud Detection System for UAV Sense and Avoid: Discussion o...

引用

ieee Aerospace conference (AeroConf)

作者： Dudek, Adrian Stutz, Peter Univ Bundeswehr Munich Inst Flight Syst Neubiberg Germany

ISBN: (纸本)9781728174365

This paper presents an approach for image-based detection of clouds on-board unmanned aerial vehicles (UAVs), taking into account changing environmental conditions and different sensor types. Therefore, different image processing methods for cloud detection are introduced. In order to separate manned and unmanned aircraft in airspaces where air traffic is expected to operate according to visual flight rules (VFR), the visibility of unmanned platforms must be ensured. Besides reduced visibility, clouds can additionally indicate an increased risk of icing and turbulence. Thus, entry into clouds must be prevented and the required minimum distances to clouds have to be complied with. In order to initiate appropriate evasive maneuvers, the cloud detection component as part of a Sense and Avoid system must deliver robust results. Our previous investigations on cloud detection algorithms have shown that the quality of the results depends very much on environmental conditions like lighting conditions, visibility, cloud type or ground texture. The requirement to deliver reliable detection results under as many weather scenarios as possible is therefore difficult to fulfill. This goal shall be achieved by using different sensor configurations and a performance-based selection of cloud detection algorithms. Apart from introducing this concept of adaptive and context-sensitive cloud detection, this paper gives an overview of complementing cloud detection methods that can be used for this purpose.

关键词： Cloud computing visualization image processing conferences Lighting Unmanned aerial vehicles Reliability

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Analyzing the Effect of Psychovisual Thresholding of DCT Coefficients for Improved Robustness of a Blind Watermarking Technique

Analyzing the Effect of Psychovisual Thresholding of DCT Coe...

引用

Delhi Section conference (DELCON), ieee

作者： Iffat Rehman Ansari Nasir N. Hurrah Mohd. Ayyub Khan Ekram Khan Dept. of Electronics Engineering Zakir Husain College of Engineering and Technology Aligarh Muslim University Aligarh India Electrical Engineering Section University Polytechnic Aligarh Muslim University Aligarh India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

This paper presents a blind and robust watermarking technique that exploits the advantage of the inter-block correlation property of the DCT coefficients and psychovisual thresholding of the DCT coefficients to obtain high robustness and better imperceptibility. The two coefficients of succeeding blocks at different positions based on the psychovisual threshold are chosen after applying (8×8) block-wise DCT to the original cover image, and then the mean between them is computed. To embed a watermark bit, the values of two selected coefficients are modified to bring the mean to the predetermined zone. Simulation results confirm that the proposed watermarking scheme is blind and robust and provides good visual quality watermarked images. Compared to the state-of-the-art methods, our method is more imperceptible as it raises the PSNR upto 1.83 dB and is also more robust as the robustness against various image processing and geometric attacks and JPEG image compression attacks is improved upto 7% and 12%, respectively.

关键词： visualization Thresholding (Imaging) image coding Simulation Transform coding Watermarking Receivers Robustness Encryption Discrete cosine transforms

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image Inpainting with Semantic-Aware Transformer

Image Inpainting with Semantic-Aware Transformer

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Shiyu Chen Wenxin Yu Qi Wang Jun Gong Peng Chen Southwest University of Science and Technology Southwest Automation Research Institute Chengdu Hongchengyun Technology Co. Ltd

image inpainting has made huge strides benefiting from the advantages of convolutional neural networks (CNNs) in understanding high-level semantics. Recently, some studies have applied transformers to the visual field to solve the problem that the convolution kernel cannot attend to longdistance information. However, unlike other vision tasks, there is much interference from damaged information in image inpainting tasks. We propose a new Semantic-Aware Transformer, which in addition to including a self-attention block like previous vision transformers, also has a block for learning semantics from QSVM. Specifically, to provide more valid information, we design a Quantized Semantic Vector Memory (QSVM) that encodes and saves semantic features in images as quantized vectors in latent space. Experiments on different datasets demonstrate the effectiveness and superiority of our method compared with the existing state-of-the-art.

关键词： visualization Convolution Semantics Interference Transformers Acoustics Convolutional neural networks

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