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Multi-scale Parallax Attention for Stereo image Super-Resolution

Multi-scale Parallax Attention for Stereo Image Super-Resolu...

引用

IEEE visual communications and image processing (VCIP)

作者： Zilin Zhao Sumei Li Zhen Gao School of Electrical and Information Engineering Tianjin University Tianjin China

Stereo image super-resolution (SR) has achieved great progress in recent years. However, the existing methods are unable to obtain rich cross-view information at a low computational cost. In addition, these methods treat each pixel equally when fusing the cross-view information with the intra-view information, resulting in non-robustness of the fused information. In this work, we propose a multi-scale parallax attention stereo super-resolution network (MPASSRnet) to address these problems. Firstly, we design a multi-scale parallax attention module (MSPAM), which computes the similarity between stereo images on multiple scale images based on the introduction of pixel-to-patch matching, thus acquiring multi-scale cross-view information at a low computational cost. Second, we propose a pixel attention fusion module (PAFM), which introduces spatial attention (SA) by calculating the correlation between cross-view information and intra-view information, and combines channel attention (CA) to make the fused information more robust. Finally, extensive experiments show that our method achieves state-of-the-art performance on the benchmark datasets.

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Variable Bitrate Models For Learned image Compression with Multi-gain units and Weighted Probability Assignment

Variable Bitrate Models For Learned Image Compression with M...

引用

IEEE visual communications and image processing (VCIP)

作者： Ran Wang Wen Jiang Heming Sun Jiro Katto Department of Computer Science and Communication Engineering Waseda University Tokyo Japan Faculty of Engineering Yokohama National University Japan Department of Computer Science and Communication Engineering Waseda Research Institute for Science and Engineering Waseda University Tokyo Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

With the advancement of deep learning techniques, learned image compression (LIC) has surpassed traditional compression methods. However, these methods typically require training separate models to achieve optimal rate-distortion performance, leading to increased time and resource consumption. To tackle this challenge, we propose leveraging multi-gain and inverse multi-gain unit pairs to enable variable rate adaptation within a single model. Nevertheless, experiments have shown that rate-distortion performance may degrade at certain bitrates. Therefore, we introduce weighted probability assignment, where different selection probabilities are assigned during training based on lambda values, to increase the model’s training frequency under specific bitrate conditions. To validate our approach, extensive experiments were conducted on Transformer-based and CNN-based models. The experimental results validate the efficiency of our proposed method.

关键词： Training Degradation Deep learning Adaptation models image coding visual communication Bit rate Redundancy Rate-distortion Transformers

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Feature Compression With 3D Sparse Convolution

Feature Compression With 3D Sparse Convolution

引用

IEEE visual communications and image processing (VCIP)

作者： Yifan Ma Changsheng Gao Qiaoxi Chen Li Li Dong Liu Xiaoyan Sun MOE Key Laboratory of Brain-Inspired Intelligent Perception and Cognition University of Science and Technology of China Hefei China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Feature compression is an important branch of video coding for machines (VCM). While existing methods draw inspiration from image compression, they have not fully utilized the unique characteristics of features. In this paper, we investigate feature characteristics in two key aspects: dimensionality and sparsity. Our analysis reveals that the low spatial dimensionality and high channel dimensionality of features make traditional 2D convolution-based methods, which usually downsample along spatial dimensions while increasing channels, unsuitable for feature compression. To address this, we propose compressing features using 3D convolution. Additionally, considering the sparsity characteristic, we propose applying sparse convolution to reduce model complexity. To thoroughly investigate the proposed 3D sparse convolution-based method, we verify it with various network structures and input features. Experimental results demonstrate the superiority of our proposed method over traditional 2D convolution-based approaches, highlighting its potential for effective feature compression.

关键词： Convolutional codes Video coding Solid modeling image segmentation image coding Three-dimensional displays Convolution visual communication Object detection Complexity theory

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An Experimental Study of a Landmine Detection Robot

An Experimental Study of a Landmine Detection Robot

引用

communications and Computer Science (InCCCS), International conference on

作者： Subhradip Mukherjee Eshwari M. Thilagaraj Hardik Gururaj Manish Rajpurohit Electronics and communication Department MVJ College of Engineering Bangalore India

ISBN: (数字)9798350358858

ISBN: (纸本)9798350358865

A landmine detection robot's objective is to cover as much ground as it can display landmines and map the remaining region with millimeter accuracy on a visual map. The land mine detecting robot prototype model presented in this paper is user-friendly, reasonably powerful, reasonably accurate, and has a visual interface for mapping landmines, adjusting PIDs, and aligning cameras. Emphasis is placed on controlling the differential drive robot in manual, semi-auto, and auto modes. The robot's precise location is ascertained by image processing, which also gives its dead reckoning servo control live reckoning feedback. The tranquil type of sensor used to find landmines is called a beat metal detector. The user has simple yet effective control over the robot connects to the graphical user interface on the remote terminal PC. The system's overall goal is to give the user access to something strong, economical, and simultaneously understandable to the typical user.

关键词： visualization Landmine detection Accuracy image processing Robot vision systems Prototypes Metals

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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...

引用

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...

引用

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

引用

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...

引用

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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