Journal of Electronic Imaging最新文献_第6页

Highly compressed image encryption algorithm via fractal and semi-tensor product compressed sensing 通过分形和半张量乘积压缩传感实现高压缩图像加密算法

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-07-01 DOI: 10.1117/1.jei.33.4.043026

Lin Fan, Meng Li

{"title":"Highly compressed image encryption algorithm via fractal and semi-tensor product compressed sensing","authors":"Lin Fan, Meng Li","doi":"10.1117/1.jei.33.4.043026","DOIUrl":"https://doi.org/10.1117/1.jei.33.4.043026","url":null,"abstract":"Storage space and security concerns on multimedia images have emerged as a global issue in recent years. Image encryption algorithm via compressed sensing (CS) is an effective method for data security and reducing storage space. However, the existing CS-based image encryption still faces problems, such as weak resistance to attacks and extensive data storage. We design a high-compression image encryption algorithm that combines fractal and semi-tensor product compressed sensing. First, a measurement matrix required for CS is generated using fractal blocks combined with the semi-tensor product method, which enhances security while reducing the size of the measurement matrix. Then, the measurements obtained from the sampling are used to define the product features of their mean and standard deviation. Exclusion criteria are set, and fractal codes are obtained through matched searching. Finally, the fractal code undergoes scrambling and diffusion, providing triple-layer protection and further improving the security of the secret image. In comparison to conventional methods, our proposed method has greatly improved the compression efficiency through compressed sampling and has the advantages of better concealment and enhanced robustness. Experiments show that we substantiate the effectiveness and superior performance of our method, all while upholding image quality and security.","PeriodicalId":54843,"journal":{"name":"Journal of Electronic Imaging","volume":"122 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Progressive reversible data hiding in encrypted images based on polynomial secret sharing and Chinese remainder theorem 基于多项式秘密共享和中国余数定理的加密图像中的渐进可逆数据隐藏

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-07-01 DOI: 10.1117/1.jei.33.4.043008

Chao Jiang, Minqing Zhang, Zongbao Jiang, Yongjun Kong, Fuqiang Di

{"title":"Progressive reversible data hiding in encrypted images based on polynomial secret sharing and Chinese remainder theorem","authors":"Chao Jiang, Minqing Zhang, Zongbao Jiang, Yongjun Kong, Fuqiang Di","doi":"10.1117/1.jei.33.4.043008","DOIUrl":"https://doi.org/10.1117/1.jei.33.4.043008","url":null,"abstract":"In the current distributed environment, reversible data hiding in encrypted images has the disadvantages of low security and nonprogressivity. To address this problem, a homomorphic embedding algorithm is proposed based on polynomial secret sharing (PSS) and Chinese remainder theorem. First, the image owner encrypts the carrier image in streaming encryption and sends it to the data hider. Then, the data hider utilizes PSS to split the carrier image into n shares. At the same time, extra secrets after SS are embedded into the carrier shares using homomorphism. After splitting by Chinese remainder theorem, every share of the embedded data is divided into some sub-shares and then distributed to the participants. The participants that satisfy the threshold condition provide part or all of the sub-shares according to the authority of the data extractor. If each participant provides all sub-shares, the secrets and carrier image can be reconstructed completely. If each participant provides part of the sub-shares, the secrets and carrier image can be reconstructed partly. The experimental results show that the proposed scheme has progressivity, high security, and a large embedding rate (ER). Meanwhile, the ER is not affected by the carrier image.","PeriodicalId":54843,"journal":{"name":"Journal of Electronic Imaging","volume":"40 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141568282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved self-supervised learning for disease identification in chest X-ray images 改进自我监督学习，识别胸部 X 光图像中的疾病

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-07-01 DOI: 10.1117/1.jei.33.4.043006

Yongjun Ma, Shi Dong, Yuchao Jiang

引用次数: 0

Deep degradation-aware up-sampling-based depth video coding 基于深度降级感知的上采样深度视频编码

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-07-01 DOI: 10.1117/1.jei.33.4.043009

Zhaoqing Pan, Yuqing Niu, Bo Peng, Ge Li, Sam Kwong, Jianjun Lei

{"title":"Deep degradation-aware up-sampling-based depth video coding","authors":"Zhaoqing Pan, Yuqing Niu, Bo Peng, Ge Li, Sam Kwong, Jianjun Lei","doi":"10.1117/1.jei.33.4.043009","DOIUrl":"https://doi.org/10.1117/1.jei.33.4.043009","url":null,"abstract":"The smooth regions in depth videos contain a significant proportion of homogeneous content, resulting in many spatial redundancies. To improve the coding efficiency of depth videos, this paper proposes a deep degradation-aware up-sampling-based depth video coding method. For reducing spatial redundancies effectively, the proposed method compresses the depth video at a low resolution, and restores the resolution by utilizing the learning-based up-sampling technology. To recover high-quality depth videos, a degradation-aware up-sampling network is proposed, which explores the degradation information of compression artifacts and sampling artifacts to restore the resolution. Specifically, the compression artifact removal module is used to obtain refined low-resolution depth frames by learning the representation of compression artifacts. Meanwhile, a jointly optimized learning strategy is designed to enhance the capability of recovering high-frequency details, which is beneficial for up-sampling. According to the experimental results, the proposed method achieves considerable performance in depth video coding compared with 3D-HEVC.","PeriodicalId":54843,"journal":{"name":"Journal of Electronic Imaging","volume":"28 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141568577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pose-guided node and trajectory construction transformer for occluded person re-identification 用于模糊人物再识别的姿态引导节点和轨迹构建转换器

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-07-01 DOI: 10.1117/1.jei.33.4.043021

Chentao Hu, Yanbing Chen, Lingyi Guo, Lingbing Tao, Zhixin Tie, Wei Ke

{"title":"Pose-guided node and trajectory construction transformer for occluded person re-identification","authors":"Chentao Hu, Yanbing Chen, Lingyi Guo, Lingbing Tao, Zhixin Tie, Wei Ke","doi":"10.1117/1.jei.33.4.043021","DOIUrl":"https://doi.org/10.1117/1.jei.33.4.043021","url":null,"abstract":"Occluded person re-identification (re-id) is a task in pedestrian retrieval where occluded person images are matched with holistic person images. Most methods leverage semantic cues from external models to align the availability of visible parts in the feature space. However, presenting visible parts while discarding occluded parts can lead to the loss of semantics in the occluded regions, and in severely crowded regions of occlusion, it will introduce inaccurate features that pollute the overall person features. Thus, constructing person features for occluded regions based on the features of its holistic parts has the potential to address the above issues. In this work, we propose a pose-guided node and trajectory construction transformer (PNTCT). The part feature extraction module extracts parts feature of the person and incorporates pose information to activate key visible local features. However, this is not sufficient to completely separate occluded regions. To further distinguish visible and occluded parts, the skeleton graph module adopts a graph topology to represent local features as graph nodes, enhancing the network’s sensitivity to local features by constructing a skeleton feature graph, which is further utilized to weaken the occlusion noise. The node and trajectory construction module (NTC) mines the relationships between skeleton nodes and aggregates the information of the person’s skeleton to construct a novel skeleton graph. The features of the occluded regions can be reconstructed via the features of the corresponding nodes in the novel skeleton graph. Extensive experiments and analyses confirm the effectiveness and superiority of our PNTCT method.","PeriodicalId":54843,"journal":{"name":"Journal of Electronic Imaging","volume":"45 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Three-dimensional human pose estimation based on contact pressure 基于接触压力的三维人体姿态估计

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-07-01 DOI: 10.1117/1.jei.33.4.043022

Ning Yin, Ke Wang, Nian Wang, Jun Tang, Wenxia Bao

{"title":"Three-dimensional human pose estimation based on contact pressure","authors":"Ning Yin, Ke Wang, Nian Wang, Jun Tang, Wenxia Bao","doi":"10.1117/1.jei.33.4.043022","DOIUrl":"https://doi.org/10.1117/1.jei.33.4.043022","url":null,"abstract":"Various daily behaviors usually exert pressure on the contact surface, such as lying, walking, and sitting. Obviously, the pressure data from the contact surface contain some important biological information for an individual. Recently, a computer vision task, i.e., pose estimation from contact pressure (PECP), has received more and more attention from researchers. Although several deep learning-based methods have been put forward in this field, they cannot achieve accurate prediction using the limited pressure information. To address this issue, we present a multi-task-based PECP model. Specifically, the autoencoder is introduced into our model for reconstructing input pressure data (i.e., the additional task), which can help our model generate high-quality features for the pressure data. Moreover, both the mean squared error and the spectral angle distance are adopted to construct the final loss function, whose aim is to eliminate the Euclidean distance and angle differences between the prediction and ground truth. Extensive experiments on the public dataset show that our method outperforms existing methods significantly in pose prediction from contact pressure.","PeriodicalId":54843,"journal":{"name":"Journal of Electronic Imaging","volume":"14 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SDANet: scale-deformation awareness network for crowd counting SDANet：用于人群计数的规模变形感知网络

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-07-01 DOI: 10.1117/1.jei.33.4.043002

Jianyong Wang, Xiangyu Guo, Qilei Li, Ahmed M. Abdelmoniem, Mingliang Gao

引用次数: 0

Lightweight human activity recognition system for resource constrained environments 用于资源有限环境的轻量级人类活动识别系统

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-07-01 DOI: 10.1117/1.jei.33.4.043025

Mihir Karandikar, Ankit Jain, Abhishek Srivastava

引用次数: 0

Research on ground-based cloud image classification combining local and global features 结合局部和全局特征的地面云图像分类研究

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-07-01 DOI: 10.1117/1.jei.33.4.043030

Xin Zhang, Wanting Zheng, Jianwei Zhang, Weibin Chen, Liangliang Chen

{"title":"Research on ground-based cloud image classification combining local and global features","authors":"Xin Zhang, Wanting Zheng, Jianwei Zhang, Weibin Chen, Liangliang Chen","doi":"10.1117/1.jei.33.4.043030","DOIUrl":"https://doi.org/10.1117/1.jei.33.4.043030","url":null,"abstract":"Clouds are an important factor in predicting future weather changes. Cloud image classification is one of the basic issues in the field of ground-based cloud meteorological observation. Deep CNN mainly focuses on the local receptive field, and the processing of global information may be relatively weak. In ground-based cloud image classification, if there is a complex background, it will help to better model the long-range dependence of the image if the relationship between different locations in the image can be globally captured. A ground-based cloud image classification method is proposed based on the fusion of local features and global features (LG_CloudNet). The ground-based cloud image classification method integrates the global feature extraction module (GF_M) and the local feature extraction module (LF_M), using the attention mechanism to weight and merge features, respectively. The LG_CloudNet model enables richer and comprehensive feature representation at lower computational complexity. In order to ensure the learning and generalization capabilities of the model during training, AdamW (Adam weight decay) is combined with learning rate warm-up and stochastic gradient descent with warm restarts methods to adjust the learning rate. The experimental results demonstrate that the proposed method achieves favorable ground-based cloud image classification outcomes and exhibits robust performance in classifying cloud images. In the datasets of GCD, CCSN, and ZNCL, the classification accuracy is 94.94%, 95.77%, and 98.87%, respectively.","PeriodicalId":54843,"journal":{"name":"Journal of Electronic Imaging","volume":"26 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

No-reference video quality assessment based on human visual perception 基于人类视觉感知的无参照视频质量评估

IF 1.1 4区计算机科学

Journal of Electronic Imaging Pub Date : 2024-07-01 DOI: 10.1117/1.jei.33.4.043029

Zhou Zhou, Guangqian Kong, Xun Duan, Huiyun Long

{"title":"No-reference video quality assessment based on human visual perception","authors":"Zhou Zhou, Guangqian Kong, Xun Duan, Huiyun Long","doi":"10.1117/1.jei.33.4.043029","DOIUrl":"https://doi.org/10.1117/1.jei.33.4.043029","url":null,"abstract":"Conducting video quality assessment (VQA) for user-generated content (UGC) videos and achieving consistency with subjective quality assessment are highly challenging tasks. We propose a no-reference video quality assessment (NR-VQA) method for UGC scenarios by considering characteristics of human visual perception. To distinguish between varying levels of human attention within different regions of a single frame, we devise a dual-branch network. This network extracts spatial features containing positional information of moving objects from frame-level images. In addition, we employ the temporal pyramid pooling module to effectively integrate temporal features of different scales, enabling the extraction of inter-frame temporal information. To mitigate the time-lag effect in the human visual system, we introduce the temporal pyramid attention module. This module evaluates the significance of individual video frames and simulates the varying attention levels exhibited by humans towards frames. We conducted experiments on the KoNViD-1k, LIVE-VQC, CVD2014, and YouTube-UGC databases. The experimental results demonstrate the superior performance of our proposed method compared to recent NR-VQA techniques in terms of both objective assessment and consistency with subjective assessment.","PeriodicalId":54843,"journal":{"name":"Journal of Electronic Imaging","volume":"16 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0