Journal of Visual Communication and Image Representation最新文献_第8页

Aesthetic image cropping meets VLP: Enhancing good while reducing bad 美学图像裁剪与 VLP 相结合：扬长避短

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2024-10-18 DOI: 10.1016/j.jvcir.2024.104316

Quan Yuan, Leida Li, Pengfei Chen

{"title":"Aesthetic image cropping meets VLP: Enhancing good while reducing bad","authors":"Quan Yuan, Leida Li, Pengfei Chen","doi":"10.1016/j.jvcir.2024.104316","DOIUrl":"10.1016/j.jvcir.2024.104316","url":null,"abstract":"<div><div>Aesthetic Image Cropping (AIC) enhances the visual appeal of an image by adjusting its composition and aesthetic elements. People make these adjustments based on these elements, aiming to enhance appealing aspects while minimizing detrimental factors. Motivated by these observations, we propose a novel approach called CLIPCropping, which simulates the human decision-making process in AIC. CLIPCropping leverages Contrastive Language–Image Pre-training (CLIP) to align visual perception with textual description. It consists of three branches: composition embedding, aesthetic embedding, and image cropping. The composition embedding branch learns principles based on Composition Knowledge Embedding (CKE), while the aesthetic embedding branch learns principles based on Aesthetic Knowledge Embedding (AKE). The image cropping branch evaluates the quality of candidate crops by aggregating knowledge from CKE and AKE; an MLP produces the best result. Extensive experiments on three benchmark datasets — GAICD-1236, GAICD-3336, and FCDB — show that CLIPCropping outperforms state-of-the-art methods and provides insightful interpretations.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"105 ","pages":"Article 104316"},"PeriodicalIF":2.6,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552675","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

U-TPE: A universal approximate thumbnail-preserving encryption method for lossless recovery U-TPE：用于无损恢复的通用近似缩略图保留加密方法

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2024-10-01 DOI: 10.1016/j.jvcir.2024.104318

Haiju Fan , Shaowei Shi , Ming Li

{"title":"U-TPE: A universal approximate thumbnail-preserving encryption method for lossless recovery","authors":"Haiju Fan , Shaowei Shi , Ming Li","doi":"10.1016/j.jvcir.2024.104318","DOIUrl":"10.1016/j.jvcir.2024.104318","url":null,"abstract":"<div><div>Due to the limited local storage space, more and more people are accustomed to uploading images to the cloud, which has aroused concerns about privacy leaks. The traditional solution is to encrypt the images directly. However, in this way, users cannot easily browse the images stored in the cloud. Obviously, the traditional method has lost the visual usability of cloud images. To solve this problem, the Thumbnail-Preserving Encryption (TPE) method is proposed. Although approximate-TPE is more efficient than ideal TPE, it cannot restore the original image without damage and cannot encrypt some images with texture features. Inspired by the above, we propose a universal approximate thumbnail-preserving encryption method with lossless recovery. This method divides the image into equal-sized chunks, each of which is further divided into an embedding area and an adjustment area. The pixels of the embedding area are recorded by prediction. Then, the auxiliary information necessary to restore the image is encrypted and hidden in the embedding area of the encrypted image. Finally, the pixel values of the adjustment area in each block are adjusted so that the average value is close to the original block. Experimental results show that the proposed method can not only restore images losslessly but also process images with different texture features, achieving good generality. On the BOWS2 dataset, all images can be encrypted by adjusting the block size. In addition, it can resist third-party face recognition and comparison, achieving satisfactory results in balancing privacy and visual usability.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"104 ","pages":"Article 104318"},"PeriodicalIF":2.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534733","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

Deep video steganography using temporal-attention-based frame selection and spatial sparse adversarial attack 利用基于时间注意力的帧选择和空间稀疏对抗攻击的深度视频隐写术

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2024-10-01 DOI: 10.1016/j.jvcir.2024.104311

Beijing Chen , Yuting Hong , Yuxin Nie

{"title":"Deep video steganography using temporal-attention-based frame selection and spatial sparse adversarial attack","authors":"Beijing Chen , Yuting Hong , Yuxin Nie","doi":"10.1016/j.jvcir.2024.104311","DOIUrl":"10.1016/j.jvcir.2024.104311","url":null,"abstract":"<div><div>With the development of deep learning-based steganalysis, video steganography is facing with great challenges. To address the insufficient security against steganalysis of existing deep video steganography, given that the video has both spatial and temporal dimensions, this paper proposes a deep video steganography method using temporal frame selection and spatial sparse adversarial attack. In temporal dimension, a stego frame selection module based on temporal attention is designed to calculate the weight of each frame and selects frames with high weights for message and sparse perturbation embedding. In spatial dimension, sparse adversarial perturbations are performed in the selected frames to improve the ability of resisting steganalysis. Moreover, to control the adversarial perturbations’ sparsity flexibly, an intra-frame dynamic sparsity threshold mechanism is designed by using percentile. Experimental results demonstrate that the proposed method effectively enhances the visual quality and security against steganalysis of video steganography and has controllable sparsity of adversarial perturbations.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"104 ","pages":"Article 104311"},"PeriodicalIF":2.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424089","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

A robust coverless image-synthesized video steganography based on asymmetric structure 基于非对称结构的鲁棒无掩码图像合成视频隐写术

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2024-10-01 DOI: 10.1016/j.jvcir.2024.104303

Yueshuang Jiao , Zhenzhen Zhang , Zhenzhen Li , Zichen Li , Xiaolong Li , Jiaoyun Liu

{"title":"A robust coverless image-synthesized video steganography based on asymmetric structure","authors":"Yueshuang Jiao , Zhenzhen Zhang , Zhenzhen Li , Zichen Li , Xiaolong Li , Jiaoyun Liu","doi":"10.1016/j.jvcir.2024.104303","DOIUrl":"10.1016/j.jvcir.2024.104303","url":null,"abstract":"<div><div>Due to the ability of hiding secret information without modifying image content, coverless image stegonagraphy has gained higher level of security and become a research hot spot. However, in existing methods, the issue of image order disruption during network transmission is overlooked. In this paper, the image-synthesized video carrier is proposed for the first time. The selected images which represent secret information are synthesized to a video in order, thus the image order will not be disrupted during transmission and the effective capacity is greatly increased. Additionally, an asymmetric structure is designed to improve the robustness, in which only the receiver utilizes a robust image retrieval algorithm to restore secret information. Specifically, certain images are randomly selected from a public image database to create multiple coverless image datasets (MCIDs), with each image in a CID mapped to hash sequence. Images are indexed based on secret segments and synthesized into videos. After that, the synthesized videos are sent to the receiver. The receiver decodes the video into frames, identifies the corresponding CID of each frame, retrieves original image, and restores the secret information with the same mapping rule. Experimental results indicate that the proposed method outperforms existing methods in terms of capacity, robustness, and security.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"104 ","pages":"Article 104303"},"PeriodicalIF":2.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433307","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

Optimized deep learning enabled lecture audio video summarization 支持深度学习的讲座音频视频摘要优化

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2024-10-01 DOI: 10.1016/j.jvcir.2024.104309

Preet Chandan Kaur , Dr. Leena Ragha

引用次数: 0

A Transformer-based invertible neural network for robust image watermarking 基于变压器的可逆神经网络用于鲁棒图像水印技术

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2024-10-01 DOI: 10.1016/j.jvcir.2024.104317

Zhouyan He , Renzhi Hu , Jun Wu , Ting Luo , Haiyong Xu

{"title":"A Transformer-based invertible neural network for robust image watermarking","authors":"Zhouyan He , Renzhi Hu , Jun Wu , Ting Luo , Haiyong Xu","doi":"10.1016/j.jvcir.2024.104317","DOIUrl":"10.1016/j.jvcir.2024.104317","url":null,"abstract":"<div><div>For the existing encoder-noise-decoder (END) based watermarking models, since the coupling between the encoder and the decoder is weak, the encoder generally embeds certain redundant features into the cover image to enable the decoder to extract watermark completely, which will affect watermarking invisibility. To address this problem, this paper proposes a Transformer-based invertible neural network (INN) for robust image watermarking (IWFormer). In order to effectively reduce redundant features, the INN framework is utilized for the watermark embedding and extracting processes, so that the encoded features are highly consistent with the features required for decoding. For enhancing watermarking robustness, an affine Transformer module is designed by mining the global correlation of the cover image. In addition, considering that the human visual system is sensitive to low-frequency variations, the wavelet low-frequency sub-band loss is deployed to guide watermark to be embedded in middle- and high-frequency components, thus further increasing the quality of the encoded images. Experimental results demonstrate that compared with the existing state-of-the-art watermarking models, the proposed IWFormer owns remarkable advantages in terms of both watermarking invisibility and robustness.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"104 ","pages":"Article 104317"},"PeriodicalIF":2.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446136","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

A robust watermarking approach for medical image authentication using dual image and quorum function 使用双图像和法定人数函数的医疗图像认证鲁棒水印方法

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2024-10-01 DOI: 10.1016/j.jvcir.2024.104299

Ashis Dey , Partha Chowdhuri , Pabitra Pal , Utpal Nandi

引用次数: 0

HPIDN: A Hierarchical prior-guided iterative denoising network with global–local fusion for enhancing low-dose CT images HPIDN：分层先验引导迭代去噪网络与全局-局部融合，用于增强低剂量 CT 图像

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2024-10-01 DOI: 10.1016/j.jvcir.2024.104297

Xiuya Shi , Yi Yang , Hao Liu , Litai Ma , Zhibo Zhao , Chao Ren

{"title":"HPIDN: A Hierarchical prior-guided iterative denoising network with global–local fusion for enhancing low-dose CT images","authors":"Xiuya Shi , Yi Yang , Hao Liu , Litai Ma , Zhibo Zhao , Chao Ren","doi":"10.1016/j.jvcir.2024.104297","DOIUrl":"10.1016/j.jvcir.2024.104297","url":null,"abstract":"<div><div>Low-dose computed tomography (LDCT) is an emerging medical diagnostic tool that reduces radiation exposure but suffers from noise retention. Current CNN-based LDCT denoising algorithms struggle to capture comprehensive global representations, impacting diagnostic accuracy. To address this, we propose a novel Hierarchical Prior-guided Iterative Denoising Network (HPIDN) for LDCT images, consisting of two main modules: the Dynamic Feature Extraction and Fusion Module (DFEFM) and the Feature-domain Iterative Denoising Module (FIDM). DFEFM dynamically captures a comprehensive representation, encompassing detailed local features in intra-relationships and complex global features in inter-relationships. It effectively guides the multi-stage iterative denoising process. FIDM hierarchically fuses the prior with image features from DFEFM by using the dual-domain attention fusion sub-network (DAFSN), enhancing denoising robustness and adaptability. This yields higher-quality images with reduced noise artifacts. Extensive experiments on the Mayo and ELCAP Datasets demonstrate the superior performance of our method quantitatively and qualitatively, improving diagnostic accuracy of lung diseases.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"104 ","pages":"Article 104297"},"PeriodicalIF":2.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424087","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

Lossless medical ultrasound image compression based on frequency domain decomposition 基于频域分解的无损医学超声图像压缩技术

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2024-10-01 DOI: 10.1016/j.jvcir.2024.104306

Yaqi Zhao, Yue Li

引用次数: 0

Local and global mixture network for image inpainting 用于图像着色的局部和全局混合网络

IF 2.6 4区计算机科学

Journal of Visual Communication and Image Representation Pub Date : 2024-10-01 DOI: 10.1016/j.jvcir.2024.104312

Seunggyun Woo , Keunsoo Ko , Chang-Su Kim

{"title":"Local and global mixture network for image inpainting","authors":"Seunggyun Woo , Keunsoo Ko , Chang-Su Kim","doi":"10.1016/j.jvcir.2024.104312","DOIUrl":"10.1016/j.jvcir.2024.104312","url":null,"abstract":"<div><div>In general, CNN-based inpainting can recover local patterns effectively using convolutional filters, but it may not exploit global correlation fully. On the other hand, transformer-based inpainting can fill in large holes faithfully based on global correlation, rather than local one. In this paper, we propose a novel image inpainting algorithm, called local and global mixture (LGM), to take advantage of the strengths of both approaches and compensate for their weaknesses. The LGM network comprises the local inpainting network (LIN) and the global inpainting network (GIN) in parallel, which are based on convolutional layers and transformer blocks, respectively, and exchange their intermediate results with each other. Furthermore, we develop an error propagation model with a continuous error mask, updated in LIN but used in both LIN and GIN to provide more reliable inpainting results. Extensive experiments demonstrate that the proposed LGM algorithm provides excellent inpainting performance, which indicates the efficacy of the parallel combination of LIN and GIN and the effectiveness of the error propagation model.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"104 ","pages":"Article 104312"},"PeriodicalIF":2.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534823","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