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Coarse-to-fine Transformer For Lossless 3D Medical image Compression

Coarse-to-fine Transformer For Lossless 3D Medical Image Com...

引用

IEEE visual communications and image processing (VCIP)

作者： Xiaoxuan Yang Guo Lu Donghui Feng Zhengxue Cheng Guosheng Yu Li Song Shanghai Jiao Tong University Shanghai China Alibaba Group T-Head Semiconductor Co. Ltd Hangzhou China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

The rapid advancements in medical imaging have led to a growing demand for high-performance lossless compression of large 3D medical image datasets. Unlike natural images, medical images typically feature three-dimensional structures, and high bit-depth, necessitating specialized compression techniques. Based on a decoder-only transformer, we propose a learnable dual-decoder model for lossless compression of 3D medical images. Our approach packs voxels into patches, which are processed by a patch-level decoder to extract the patch feature. The voxels, along with the patch feature, are subsequently fed into a voxel-level decoder to model each voxel. This coarse-to-fine modeling strategy reduces the computational time for each voxel and enables long-range modeling dependencies. Experimental results demonstrate that our proposed model achieves state-of-the-art compression performance, with an approximately 15% improvement in compression performance over the traditional JP3D benchmark on various datasets.

关键词： Learning systems Solid modeling image coding Three-dimensional displays visual communication Computational modeling Transformers Feature extraction Decoding Biomedical imaging

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Rate-Controllable and Target-Dependent JPEG-Based image Compression Using Feature Modulation

Rate-Controllable and Target-Dependent JPEG-Based Image Comp...

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2023 IEEE International conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Jeong, Seongmoon Jeon, Kang Eun Ko, Jong Hwan Sungkyunkwan University Department of Artificial Intelligence Suwon-si Korea Republic of Convergence Research Institute Sungkyunkwan University Suwon-si Korea Republic of Sungkyunkwan University Department of Electrical and Computer Engineering Suwon-si Korea Republic of

ISBN: (纸本)9798350313154

While conventional image compression techniques are optimized for human visual perception, the rise of machine learning techniques has led to the emergence of image compression methods tailored for machine vision tasks. Although a few recent studies explored target-dependent reconfiguration of lightweight codecs such as JPEG, these approaches are limited to specific trained bitrates only. Moreover, existing deep learning-based compression frameworks entail a high computational cost, making them impractical for real-time compression on devices with limited resources. In this paper, we present a novel JPEG compression framework that can adaptively generate an optimal quantization table (QT) depending on both the target bitrate and the target metric (quality or accuracy). To provide fine controllability over a wide range of bitrates, we employ a feature modulation technique to a QT generator and bitrate predictor, which are trained by a novel training method called bitrate range partitioning. Our simulation results show that the proposed framework enhances the performance of standard JPEG by up to 2dB in PSNR and 10% in accuracy at the same bitrate, while incurring minimal computational overhead compared to JPEG. © 2023 IEEE.

关键词： image compression

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Scenedoor: An Environmental Backdoor Attack for Face Recognition

Scenedoor: An Environmental Backdoor Attack for Face Recogni...

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IEEE visual communications and image processing (VCIP)

作者： Ruofei Wang Ziyuan Luo Haoliang Li Renjie Wan Department of Computer Science Hong Kong Baptist University Department of Electrical Engineering City University of Hong Kong

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Face recognition is often used for biometric validation, which has become a significant technique in our society. Due to its sensitive applications, security vulnerabilities posed by backdoor attacks have attracted considerable focus. Current backdoor attack methods use digital perturbations or physical objects as triggers, while these additional requirements make existing backdoor attacks less viable in real-world applications. To address this issue, we propose a novel backdoor attack method named Scene Backdoor (Scenedoor), which injects a 3D scene as the trigger that effectively simplifies the backdoor activation. Any person who appears in this scene will be attacked as the attacker-desired identity. Specifically, we reconstruct a 3D scene from several 2D images and then blend the facial part extracted from the input sample with the reconstructed scene to generate the poisoned image. Extensive experiments are conducted on CelenDF (v2), CelebA-HQ, and PinsFace datasets, demonstrating that Scenedoor overtakes five state-of-the-art methods in terms of effectiveness, stealthiness, and robustness.

关键词： Three-dimensional displays image recognition visual communication Face recognition Perturbation methods Robustness Application security image reconstruction

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Robust image Watermarking Based on Texture Guidance and Dynamic Multi-Spectrum Attention

Robust Image Watermarking Based on Texture Guidance and Dyna...

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International conference on Wireless communications and Signal processing (WCSP)

作者： Fengting Yang Weikang Xue Honglong Cao Jianling Hu School of Electronic and Information Engineering Soochow University Suzhou China Wuxi University Wuxi China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

Tracking image sources and verifying copyright information is crucial in digital media communication. Digital image watermarking technology, widely used for copyright protection and source tracking, faces challenges in balancing imperceptibility and robustness under a certain embedded capacity. To address this problem, this paper proposes a robust image watermarking framework based on a deep invertible network, named DRIN. Based on the frequency-domain characteristics of robust steganography, we parallelly introduce a texture guidance module (GLCM) and a dynamic fine-grained DCT representation-based channel attention module (DFscaNet) into the network. These modules guide the model to effectively locate the texture features where high-frequency energy is concentrated in the cover image, improving the quality of the watermarked image and enhancing the robustness of the watermark. Experiments have demonstrated that the proposed method has superior performance, higher visual quality, and stronger robustness compared to the state-of-the-art methods.

关键词： Wireless communication visualization Steganography Digital images Noise Watermarking Media Gray-scale Robustness Discrete cosine transforms

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Towards Validating Face Editing Ability in Generative Models

Towards Validating Face Editing Ability in Generative Models

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IEEE visual communications and image processing (VCIP)

作者： Dai-Jie Wu Pin-Yen Chiu Chih-Yu Wang Jun-Cheng Chen Academia Sinica Research Center for Information Technology Innovation

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Face editing has recently blossomed into a highly active and significant domain, impacting numerous applications from entertainment to security. Despite its rapid growth, the field still faces challenges in establishing a universally accepted and robust evaluation mechanism that can comprehensively assess the performances of face editing techniques and their underlying generative models. Our paper introduces a well-defined evaluation protocol that seamlessly combines systematic experimental methodologies with thorough subjective evaluations. This collaborative approach ensures a more in-depth and unbiased examination of face editing techniques. Based on our extensive studies, we observe that traditional metrics, notably the Fréchet Inception Distance (FID), serve well in measuring perceptual attributes of edited images. However, they might fall short in covering all aspects of a face editing method’s capabilities. To bridge this gap, we have incorporated additional metrics that assess disentanglement and editing effectiveness, leading to the creation of a holistic assessment framework that promises a more comprehensive evaluation upon face editing capability of different deep generative models.

关键词： Measurement Bridges Systematics Protocols visual communication image processing Entertainment industry Collaboration Security Faces

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Federated Contrastive Domain Adaptation for Category-inconsistent Object Detection

Federated Contrastive Domain Adaptation for Category-inconsi...

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IEEE visual communications and image processing (VCIP)

作者： Wei-Yu Chen Peggy Joy Lu Vincent Shin-Mu Tseng Department of Computer Science National Center for High-Performance Computing (NCHC) National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science National Chung Cheng University Chiayi Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

To obtain diverse scenarios for collaboratively training a more generalized object detector, the multi-source domain adaptive object detection has been proposed. However, such scenario faces challenges related to data privacy, domain discrepancy and category inconsistency, thus we propose a framework called FedCoin: Federated Contrastive domain-adaptation for category-inconsistent object detection. On the client sides, a novel dynamic model contrastive strategy is proposed to reduce excessively domain-specific features from local models. On the server side, we design a two-stage teacher-student architecture to tackle the challenge of backbone aggregation and inconsistent categories integration. Our method outperforms SOTA methods across different domain adaptation tasks, with an average precision increase of 6% on various datasets, demonstrating its superiority over existing methods for category-inconsistent and privacy-preserving scenarios. The source code is available online: https://***/ccuvislab/FedCoin

关键词： Training Adaptation models Data privacy visual communication Source coding image processing Object detection Detectors Servers Faces

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Faster and Finer Pose Estimation for Object Pool in a Single RGB image

Faster and Finer Pose Estimation for Object Pool in a Single...

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IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Aing, Lee Lie, Wen-Nung Chiang, Jui-Chiu Natl Chung Cheng Univ CCU Dept Elect Engn Chiayi Taiwan Natl Chung Cheng Univ CCU Ctr Innovat Res Aging Soc CIRAS Chiayi Taiwan Natl Chung Cheng Univ CCU Adv Inst Mfg High Tech Innovat AIM HI Chiayi Taiwan

ISBN: (纸本)9781728185514

Predicting/estimating the 6DoF pose parameters for multi-instance objects accurately in a fast manner is an important issue in robotic and computer vision. Even though some bottom-up methods have been proposed to be able to estimate multiple instance poses simultaneously, their accuracy cannot be considered as good enough when compared to other state-of-the-art top-down methods. Their processing speed still cannot respond to practical applications. In this paper, we present a faster and finer bottom-up approach of deep convolutional neural network to estimate poses of the object pool even multiple instances of the same object category present high occlusion/overlapping. Several techniques such as prediction of semantic segmentation map, multiple keypoint vector field, and 3D coordinate map, and diagonal graph clustering are proposed and combined to achieve the purpose. Experimental results and ablation studies show that the proposed system can achieve comparable accuracy at a speed of 24.7 frames per second for up to 7 objects by evaluation on the well-known Occlusion LINEMOD dataset.

关键词： 6DoF object pose estimation bottom-up approach

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visual Analysis of Linked Musicological Data with the musiXplora 15th

Visual Analysis of Linked Musicological Data with the musiXp...

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15th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 15th International conference on Computer Vision Theory and Applications (VISAPP)

作者： Khulusi, Richard Focht, Josef Jaenicke, Stefan Univ Leipzig Image & Signal Proc Grp Leipzig Germany Univ Leipzig Mus Instrument Museum Leipzig Germany Univ Southern Denmark Dept Math & Comp Sci Odense Denmark

ISBN: (纸本)9783030948931;9783030948924

While digitizing data is the first major step for many digital humanities projects, the visual analysis is of high value for humanists, as it brings a wide range of possibilities to work with data. While rather traditional analysis often concentrates on standalone or sets of information (close reading), global inspections of linked data are also requested by today's researchers and made possible through digital processing. Hence, distance reading approaches are more and more found in humanities projects. Next to such approaches allowing new research questions of quantitative analysis, linking previously separate information on a data level is another way of providing humanists with access to further, previously not reachable, global inspections of faceted datasets. As a domain with both, faceted data and a rather low level of digitization, musicology is a prime example of how the digital humanities may improve and support the daily workflows of humanists. Despite the generally low level of digitization, multiple projects already build a basis to help in digitizing the field. As an example, the musiXplora project collected a vast amount of musicological data throughout the last 16 years and now offers both, a detailed biography of persons, places, objects, events, media, institutions and terms and also the linkage between these kinds of entities to help in giving a user a comprehensible overview in the traditionally fragmented field of musicology. Supported by a set of visualizations, the website of the project allows for visual analysis on close reading and distant reading levels. This does not only help researchers in their daily workflows but also offers users with a more casual nature an interesting view inside the domain of musicology.

关键词： Computer science visualization visual analysis Digital humanities Musicology

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Signaling of object masks with the assistance of the object mask information SEI message

Signaling of object masks with the assistance of the object ...

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IEEE visual communications and image processing (VCIP)

作者： Jie Chen Zixiang Zhang Yan Ye Shurun Wang Damo Academy Alibaba Group Beijing China Damo Academy Alibaba Group Sunnyvale USA Damo Academy Alibaba Group Beijing USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

To reduce the decoder burden of performing the video analysis tasks and increase the accuracy of the task results, an effective solution is to perform the video analysis task in the encoder side and send the results to the decoder. In this paper, the object masks which are obtained by invoking a video analysis model in the encoder are represented by object mask pictures and encoded as auxiliary pictures. To enable this idea, a new auxiliary picture type was specified and a new supplemental enhancement information (SEI) message, the object mask information (OMI) SEI message, is introduced. The experiments are conducted by using versatile video coding test model (VTM) layered coding, and according to the experimental results, it is concluded that coding the object mask pictures as auxiliary pictures is feasible and efficient. And thus, the proposed OMI SEI message was adopted to working draft of versatile supplemental enhancement information version 4 and included in the technical report of optimization of encoders and receiving systems for machine analysis of coded video content in January 2024.

关键词： Video coding image coding Accuracy visual communication ISO Standards Writing Software Decoding IEC Standards Optimization

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Research on Some Key Technologies of Deep Learning in the Field of Computer Vision

Research on Some Key Technologies of Deep Learning in the Fi...

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Innovation in Technology (INOCON), IEEE International conference for

作者： Yirong Mao Rui Mao Yunnan Communications Vocational and Technical College China

The role of computer vision technology in the field of artificial intelligence development is very important, but there is a problem of poor application effect of key technologies. Traditional neural network algorithms cannot solve the problems of image classification and inaccurate image detection in computer visual perception tasks. In the tide of artificial intelligence, the combination of computer vision and deep learning has become an important force to promote technological progress. By imitating the processing mechanism of images and videos by human brain, deep learning has not only made a breakthrough in understanding complex visual information, but also demonstrated its incomparable ability in many fields.

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