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Fidelity-preserving Learning-Based image Compression: Loss Function and Subjective Evaluation Methodology

Fidelity-preserving Learning-Based Image Compression: Loss F...

引用

IEEE visual communications and image processing (VCIP)

作者： Shima Mohammadi Yaojun Wu João Ascenso Instituto Superior Técnico Instituto de Telecomunicações Bytedance

Learning-based image compression methods have emerged as state-of-the-art, showcasing higher performance compared to conventional compression solutions. These data-driven approaches aim to learn the parameters of a neural network model through iterative training on large amounts of data. The optimization process typically involves minimizing the distortion between the decoded and the original ground truth images. This paper focuses on perceptual optimization of learning-based image compression solutions and proposes: i) novel loss function to be used during training and ii) novel subjective test methodology that aims to evaluate the decoded image fidelity. According to experimental results from the subjective test taken with the new methodology, the optimization procedure can enhance image quality for low-rates while offering no advantage for high-rates.

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visual Data Enciphering via DNA Encoding, S-Box, and Tent Mapping 5

Visual Data Enciphering via DNA Encoding, S-Box, and Tent Ma...

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5th IEEE International conference on image processing Applications and Systems (IPAS)

作者： Gabr, Mohamed Younis, Hana Ibrahim, Marwa Alajmy, Sara Alexan, Wassim German Univ Cairo Fac MET Cairo Egypt German Univ Cairo Fac IET Cairo Egypt

ISBN: (纸本)9781665462198

The ever-evolving nature of the Internet and wireless communications, as well as the production of huge amounts of multimedia every day has created a dire need for their security. In this paper, an image encryption technique that is based on 3 stages is proposed. The first stage makes use of DNA encoding. The second stage proposed and utilizes a novel S-box that is based on the Mersenne Twister and a linear descent algorithm. The third stage employs the Tent chaotic map. The computed performance evaluation metrics exhibit a high level of achieved security.

关键词： Chaotic maps cryptography image encryption Mersenne Twister S-box Tent map

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Deep Learning: A Detailed Analysis of Various image Augmentation Techniques 1

Deep Learning: A Detailed Analysis of Various Image Augmenta...

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1st IEEE International conference on Networking and communications, ICNWC 2023

作者： Swathi, S. Rajalakshmi, M. Senniappan, Vijayalakshmi Sri Sairam Institute of Technology Department of Computer Science and Engineering Chennai India Coimbatore Institute of Technology Department of Information Technology Coimbatore India Park College of Engineering Tech Department of Information Technology Coimbatore India

ISBN: (纸本)9798350336009

Deep learning has been performing reasonably well in computer vision tasks that call for a high volume of photos, although gathering images is often expensive and challenging. Different picture augmentation techniques have been put forth as practical and efficient solutions to this problem Understanding current algorithms is critical when developing new processes or determining the best approaches for a certain task. With deep learning, some of the data pre-processing that is typically required for machine learning is avoided. Unstructured text and visual data can be handled by these algorithms, which can also automate feature extraction and lessen the need for human experts. With a brand-new taxonomy of usable data, we undertake a complete survey of picture augmentation for deep learning in this work. We discuss the difficulties in computer vision tasks and vicinity distribution to give you a fundamental understanding of why we want picture augmentation. Based on the study, we think that our survey provides a clearer knowledge that may be used to select the best techniques or create original algorithms for real-world uses. © 2023 IEEE.

关键词： Computer vision

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Improved Wild Horse Optimizer with Deep Learning Model for Skin Lesion Detection and Classification on Dermoscopic images 23th

Improved Wild Horse Optimizer with Deep Learning Model for S...

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23rd International conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Maranco, M. Tyagi, Amit Kumar Sivakumar, M. Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu 603203 India Department of Fashion Technology National Institute of Fashion Technology New Delhi India

ISBN: (纸本)9783031648120

Melanoma is a deadly kind of skin cancer which can spread to other parts of the body. Therefore, it is necessary to identify melanoma at the beginning level. visual examinationat the time of medical examination of skin lesion is a challenging process as there exist high resemblance among the lesions. Besides, dermoscopy is a non-invasive imaging tool which enables visualizationof the skin surface by light magnifying device and immersion fluid. Traditional image processing models such as histogram thresholding, clustering, or active contours are employed to segment skin lesions. This motivates the study of how to best employ AI, and deep learning (DL) algorithms in particular, to identify skin lesions in dermoscopic pictures. By using these images, current research refines the existing wild horse optimizer (IWHO) by using a deep learning driven skin lesion detection and classification (IWHODL-SLDC) *** proposed model utilizes optimal Tsallis entropy based image segmentation. Besides, Squeeze Netapproach can be employed to derive feature vectors. Also, IWHO with deep wavelet neural network (DWNN) technique is used to classify the dermoscopic images. Numerous tests are carried out to guarantee the enhancement of the IWHODL-SLDC method on the reference dataset. It was found via experimentation that the IWHODL-SLDC model outperformed the other approaches in a wide range of ways. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： image classification

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Privacy Protection for image Sharing Using Reversible Adversarial Examples

Privacy Protection for Image Sharing Using Reversible Advers...

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IEEE International conference on communications (ICC)

作者： Ping Yin Wei Chen Jiaxi Zheng Yiting Zhang Lifa Wu School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Online image sharing on social media platforms faces information leakage due to deep learning-aided privacy attacks. To avoid these attacks, this paper proposes a privacy protection mechanism for image sharing without changing the visual effect, which is based on reversible adversarial examples. Specifically, social media platform users can change the class activation feature to convert the original image into an adversarial image before sharing. When users want to restore the adversarial image to the original image, they can use an improved generative adversarial network model to restore it. The experimental results prove that the conversion model in this paper can effectively prevent privacy attacks from analyzing and stealing users' private information while having no visual impact. At the same time, the proposed restoration model can restore the adversarial examples with high accuracy.

关键词： Training Privacy image recognition Social networking (online) Perturbation methods visual effects Information leakage

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MVBind: Self-Supervised Music Recommendation for Videos via Embedding Space Binding

MVBind: Self-Supervised Music Recommendation for Videos via ...

引用

IEEE visual communications and image processing (VCIP)

作者： Jiajie Teng Huiyu Duan Yucheng Zhu Sijing Wu Guangtao Zhai Shanghai Jiao Tong University China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Recent years have witnessed the rapid development of short videos, which usually contain both visual and audio modalities. Background music is important to the short videos, which can significantly influence the emotions of the viewers. However, at present, the background music of short videos is generally chosen by the video producer, and there is a lack of automatic music recommendation methods for short videos. This paper introduces MVBind, an innovative Music-Video embedding space Binding model for cross-modal retrieval. MVBind operates as a self-supervised approach, acquiring inherent knowledge of intermodal relationships directly from data, without the need of manual annotations. Additionally, to compensate the lack of a corresponding musical-visual pair dataset for short videos, we construct a dataset, SVM-10K (Short Video with Music-10K), which mainly consists of meticulously selected short videos. On this dataset, MVBind manifests significantly improved performance compared to other baseline methods. The database and code are available at: https://***/IntMeGroup/MVBind.

关键词： visualization Codes Databases visual communication Annotations image processing Complexity theory Recommender systems Videos Cross modal retrieval

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IoU-CLIP: IoU-Aware Language-image Model Tuning for Open Vocabulary Object Detection

IoU-CLIP: IoU-Aware Language-Image Model Tuning for Open Voc...

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IEEE visual communications and image processing (VCIP)

作者： Mingzhou He Qingbo Wu King Ngi Ngan Yiming Xiao Fanman Meng Heqian Qiu Hongliang Li School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Open vocabulary object detection (OVD), which detects novel categories through detectors trained on base categories, has achieved remarkable advancement attributable to large-scale vision-language models, such as CLIP. The prior OVD works mainly focused on improving the classification accuracy of proposals, ignoring the ability of localization for novel categories. In this work, we propose IoU-aware language-image model tuning (IoU-CLIP) for open vocabulary object detection. Specifically, we construct a region image dataset with different IoU and adopt IoU values as labels to fine-tune the CLIP model to learn IoU-aware and class-agnostic semantic prompts and visual embeddings. The fine-tuned IoU-CLIP can predict IoU scores for proposals, which interact with classification scores. Meanwhile, IoU-aware and class-agnostic visual embeddings are utilized for box regression to enhance the generalization of the localization capability. We evaluate our method on the COCO and LVIS OVD benchmarks, outperforming the baseline (RegionCLIP) by 5.5% AP 50 and 5.8% AP on novel categories, respectively, achieving state-of-the-art performance.

关键词： Location awareness Vocabulary visualization visual communication image processing Semantics Object detection Detectors Proposals Tuning

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Learned Lossless Coding for Ultra-high-speed Spike Streams via Intensity Remapping

Learned Lossless Coding for Ultra-high-speed Spike Streams v...

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IEEE visual communications and image processing (VCIP)

作者： Fanke Dong Chuanmin Jia Wangxuan Institute of Computer Technology Peking University Beijing China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

As a novel bio-inspired imaging device, the spike camera shows remarkable potential in capturing ultra-high-speed motion scenes by simulating the mechanism of the retinal fovea. It achieves a temporal resolution of tens of thousands of Hz through spike emission. However, this capability presents significant challenges in terms of large-scale data storage and transmission, along with stringent fidelity requirements for spike data, thus posing a formidable obstacle to the lossless compression of continuous spike streams. In this paper, we introduce an effective image representation method for spikes, along with an intensity remapping technique to mitigate noise effects in spike streams. Building on this, we propose a learned lossless spike data compression model. To our knowledge, it is the first learning-based model for lossless spike stream compression. Experimental results demonstrate that our method can realize state-of-the-art performance for spike data lossless compression.

关键词： image coding image resolution visual communication Noise Memory image representation Retina Propagation losses Data models Streams

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Learning Multi-dimensional Parallax Prior for Stereo image Super-Resolution 28th

Learning Multi-dimensional Parallax Prior for Stereo Image S...

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28th International conference on Neural Information processing

作者： Li, Changyu Zhang, Dongyang Jiang, Chunlin Xie, Ning Shao, Jie Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Sichuan Artificial Intelligence Res Inst Yibin 644000 Peoples R China

ISBN: (纸本)9783030923099;9783030923105

Recent years have witnessed great success in image super-resolution (SR). However, how to effectively exploit stereo information for the SR purpose is still challenging. This paper focuses on proposing a general solution to stereo image SR. We propose a novel module named Parallax Multi-Dimensional Attention (PMDA) that could not only be seamlessly integrated into most of existing SISR networks but also explore cross-view information from stereo images. Specifically, a pair of stereo images are fed into two identical SISR networks. The extracted middle features are transferred into PMDA to capture the inherent correlation within stereo image pairs. Finally, the internal-view and cross-view information is mixed by SISR network to generate the final output. We also introduce Self Multi-Dimensional Attention (SMDA) to effectively improve the feature representation capacity of a single image. Based on PMDA and SMDA, we design a stereo image SR model named Progressive Attention Stereo SR (PASR), which illustrates the flexibility of PMDA and performance-boosting guided by PMDA and SMDA. Extensive experiments show its superiority in the aspects of visual quality and quantitative comparison.

关键词： image super-resolution Deep learning Stereo image

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Attention to Emotions: Body Emotion Recognition In-the-Wild Using Self-attention Transformer Network 18th

Attention to Emotions: Body Emotion Recognition In-the-Wild ...

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18th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 18th International conference on Computer Graphics Theory and Applications

作者： Paiva, Pedro V. V. Ramos, Josue J. G. Gavrilova, Marina Carvalho, Marco A. G. Univ Estadual Campinas Sch Technol Limeira Brazil Renato Archer IT Ctr Cyber Phys Syst Div Campinas Brazil Univ Calgary Dept Comp Sci Calgary AB Canada

ISBN: (纸本)9783031667428;9783031667435

Body movements are an essential part of non-verbal communication as they help to express and interpret human emotions. The potential of Body Emotion Recognition (BER) is immense, as it can provide insights into user preferences, automate real-time exchanges and enable machines to respond to human emotions. BER finds applications in customer service, healthcare, entertainment, emotion-aware robots, and other areas. While face expression-based techniques are extensively researched, detecting emotions from body movements in the realworld presents several challenges, including variations in body posture, occlusions, and background. Recent research has established the efficacy of transformer deep-learning models beyond the language domain to solve video and image-related problems. A key component of transformers is the self-attention mechanism, which captures relationships among features across different spatial locations, allowing contextual information extraction. In this study, we aim to understand the role of body movements in emotion expression and to explore the use of transformer networks for body emotion recognition. Our method proposes a novel linear projection function of the visual transformer, which enables the transformation of 2D joint coordinates into a conventional matrix representation. Using an original method of contextual information learning, the developed approach enables a more accurate recognition of emotions by establishing unique correlations between individual's body motions over time. Our results demonstrated that the self-attention mechanism was able to achieve high accuracy in predicting emotions from body movements, surpassing the performance of other recent deep-learning methods. In addition, the impact of dataset size and frame rate on classification performance is analyzed.

关键词： Body emotion recognition Affective computing Video and image processing Gait analysis Attention-based design

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