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Fine-grained Single-layer Tiling for Viewport-Adaptive 360-degree Video Streaming

Fine-grained Single-layer Tiling for Viewport-Adaptive 360-d...

引用

IEEE visual communications and image processing (VCIP)

作者： Jong-Beom Jeong Jun-Hyeong Park Soonbin Lee Eun-Seok Ryu Department of Computer Science Education Sungkyunkwan University (SKKU) Seoul Republic of Korea Department of Immersive Media Engineering Sungkyunkwan University (SKKU) Seoul Republic of Korea Multimedia Communications Group Fraunhofer Heinrich-Hertz-Institute (HHI) Berlin Germany

Tile-based streaming is widely adopted for viewport-adaptive 360-degree video streaming due to its potential for bitrate reduction. However, for legacy devices equipped with a single decoder, managing multiple tile bitstreams poses a challenge, particularly with regards to synchronizing multiple decoders. This paper introduces Butterfly360, a fine-grained single-layer tiling method that advances two main ideas: (i) an optimal tiling scheme decision method for single-layer merging of tiles using various tile sizes, and (ii) a simple rate adaptation method for viewport tiles. Experimental results demonstrate that the proposed method offers advantages in terms of bitrate savings and reduction in decoding runtime. Furthermore, the proposed method facilitates the use of either multiple tile bitstreams or a single-layer merged bitstream.

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Multimodal Fake News Detection on Fakeddit Dataset Using Transformer-Based Architectures 4th

Multimodal Fake News Detection on Fakeddit Dataset Using Tra...

引用

4th International conference on Machine Learning, image processing, Network Security and Data Sciences (MIND)

作者： Kalra, Sakshi Kumar, Chitneedi Hemanth Sai Sharma, Yashvardhan Chauhan, Gajendra Singh BITS Pilani Dept CSIS Pilani Campus Pilani 333031 Rajasthan India BITS Pilani Dept HSS Pilani Campus Pilani 333031 Rajasthan India

ISBN: (纸本)9783031243660;9783031243677

Real-time information is transforming due to technological advancements and widespread internet access. In our increasingly digital culture, fake news and misinformation are more common in journalism, news reporting, social media, and other online information consumption platforms. The spread of misinformation can have harmful impacts or even control public events by using multimedia content to deceive readers and gain dissemination. The question here is how to spot fake news about recently occurring events and it is one of the special difficulties in Fake News Detection (FND) on social networking sites. Recent study has considerably increased our ability to identify fake news, because of less emphasis on utilizing the relationship between the textual and visual information in news samples. It is possible to spot fake news by giving importance to similarity among textual and visual features. In this paper, we study the task of identifying fake news using the Fakeddit dataset, which is a collection of full-length articles and related images. We propose a multimodal approach that makes use of transfer learning to gather semantic and contextual data, develop stronger hidden representations between the words in news samples and the images, and tries to improve the accuracy of FND task. We carefully evaluate the performance of our model on the Fakeddit dataset. The results demonstrate that the proposed model learns more accurate textual features and outperforms the most current textual results on that dataset.

关键词： Natural Language processing (NLP) Fake News Detection (FND) Transfer learning Deep learning Social media analytics

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Privacy Preserving Facial image processing Method Using Variational Autoencoder 3rd

Privacy Preserving Facial Image Processing Method Using Vari...

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3rd International conference on Big Data and Security, ICBDS 2021

作者： Qiu, Yuying Niu, Zhiyi Tian, Qikun Song, Biao Nanjing China Nanjing Institute of Technology Nanjing China University of Oulu Oulu Finland

ISBN: (纸本)9789811908514

Over the recent years, a massive number of face images are widely used in each field of Internet, which makes the privacy leakage problem due to the abuse of facial images become more serious. The existing simple face de-identification methods like occlusion or blurring seriously damage the useful information and structure of image, which leads to a poor visual effect and is of little significant in practical application. In this work, we propose a novel privacy preserving facial image processing method. The proposed method removes the identity information of face images by four different measures first, and then fuse these de-identified images through a multi-input generative model based on Variational Autoencoder (VAE) to reconstruct the useful information of image. The proposed method aims to generate images that is close to the real face but does not contain the privacy information in the original human face image, which avoids the disclosure of privacy. We demonstrate the information reconstruction capabilities of our method with contrast experiments on the CelebA datasets. Experimental results emphasize the superiority of our method in terms of maintaining useful information and visual effects over traditional face de-identification methods. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Damage detection

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Improving Transferability Reversible Adversarial Examples Based on Flipping Transformation 9th

Improving Transferability Reversible Adversarial Examples Ba...

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9th International conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Fang, Youqing Jia, Jingwen Yang, Yuhai Lyu, Wanli Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (纸本)9789819959679

Adding subtle perturbations to an image can cause the classification model to misclassify, and such images are called adversarial examples. Adversarial examples threaten the safe use of deep neural networks, but when combined with reversible data hiding (RDH) technology, they can protect images from being correctly identified by unauthorized models and recover the image lossless under authorized models. Based on this, the reversible adversarial example (RAE) is rising. However, existing RAE technology focuses on feasibility, attack success rate and image quality, but ignores transferability and time complexity. In this paper, we optimize the data hiding structure and combine data augmentation technology, which flips the input image in probability to avoid overfitting phenomenon on the dataset. On the premise of maintaining a high success rate of white-box attacks and the image’s visual quality, the proposed method improves the transferability of reversible adversarial examples by approximately 16% and reduces the computational cost by approximately 43% compared to the state-of-the-art method. In addition, the appropriate flip probability can be selected for different application scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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A Near Lossless Learned image Coding Network Quantization Approach for Cross-Platform Inference

A Near Lossless Learned Image Coding Network Quantization Ap...

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IEEE visual communications and image processing (VCIP)

作者： Xinyu Hang Chuanmin Jia Siwei Ma Wen Gao National Engineering Research Center on Visual Technology School of Computer Science Peking University China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China Wangxuan Institute of Computer Technology Peking University China

This paper presents an innovative pipeline design for the quantization of neural image codecs, ensuring robust and consistent deployment across various platform inferences. Our approach addresses a significant challenge prevalent in neural image compression networks: the lack of quantization methods for entropy probability hyper-priors, such as Gaussian mixture models (GMMs), in the existing literature. Our method serves as a solution to prevent decoding errors stemming from float-point discrepancies between the encoder and decoder, especially when executed on different platforms. By implementing a quantization scheme based on look-up tables (LUTs), we achieve precise quantization of GMM probability models with minimal performance degradation, approaching near losslessness. Moreover, our contribution offers a comprehensive quantization pipeline that facilitates dependable data transmission and harmonious synchronization of arithmetic coding algorithms between the encoder and decoder components. Empirical evidence highlights the effectiveness of our method, demonstrating seamless codec matching on diverse platforms and substantiating its capacity to enhance the integrity and security of compressed data. This work enhances the practical applicability of neural compression techniques across real-world scenarios.

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AsymLLIC: Asymmetric Lightweight Learned image Compression

AsymLLIC: Asymmetric Lightweight Learned Image Compression

引用

IEEE visual communications and image processing (VCIP)

作者： Shen Wang Zhengxue Cheng Donghui Feng Guo Lu Li Song Wenjun Zhang Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Learned image compression (LIC) methods often employ symmetrical encoder and decoder architectures, evitably increasing decoding time. However, practical scenarios demand an asymmetric design, where the decoder requires low complexity to cater to diverse low-end devices, while the encoder can accommodate higher complexity to improve coding performance. In this paper, we propose an asymmetric lightweight learned image compression (AsymLLIC) architecture with a novel training scheme, enabling the gradual substitution of complex decoding modules with simpler ones. Building upon this approach, we conduct a comprehensive comparison of different decoder network structures to strike a better trade-off between complexity and compression performance. Experiment results validate the efficiency of our proposed method, which not only achieves comparable performance to VVC but also offers a lightweight decoder with only 51.47 GMACs computation and 19.65M parameters. Furthermore, this design methodology can be easily applied to any LIC models, enabling the practical deployment of LIC techniques.

关键词： Training Performance evaluation image coding visual communication Design methodology Computational modeling Computer architecture Decoding Complexity theory Computational efficiency

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Learned image Compression With Variable Neural Network Architecture

Learned Image Compression With Variable Neural Network Archi...

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IEEE visual communications and image processing (VCIP)

作者： Ziyi Zeng Lu Yu Zhejiang University Hangzhou China

With the development of neural networks, the coding efficiency of learned image compression methods gradually exceeds that of traditional image codecs that are carefully designed and optimized by experts. However, the deep image compression model with an invariable network structure and fixed network parameters can only cover a limited range of bit rates. Using the same network structure for different bit rate ranges leads to sub-optimal coding efficiency. Meanwhile, the more complex network structure and the increase of network parameters cause higher model complexity, which makes it difficult for network training and practical application. In this paper, a new perspective is proposed to solve the above challenges. The idea of variable network architecture is introduced to the field of learned image compression. We devise two strategies with variable network architecture, which improve the coding performance of the learned image compression model from the coding efficiency and complexity respectively. Compared to the image compression method of fixed network structure, our first approach attains an average BDPSNR of 0.771dB and an average BDBR of −14.876%. The second attains an average BDPSNR of 0.320dB and an average BDBR of −5.471%. These results demonstrate the effectiveness of variable network structure in improving coding efficiency and controlling model complexity.

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visual Question Answering

Visual Question Answering

引用

International conference on Computing, Networking and communications (ICNC)

作者： Ahmed Nada Min Chen Computing and Software Systems University of Washington Bothell Bothell USA

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

visual question answering (VQA) is an artificial intelligence (AI) and computer vision (CV) comprehensive task to answer questions about the visual content of an image, such as “what color is the bus?” or “how many people are in the photo?” VQA has shown great potential and importance in various domains, ranging from medical imaging applications, autonomous driving, to virtual assistants and search engines. This study develops a framework to tackle VQA research challenges by adopting and extending recent breakthroughs in attention techniques, natural language processing, and image classification models. In addition, different from other previous work that uses static question embedding, we investigate how alternative dynamic embedding models enhance the effectiveness of VQA task. The work is evaluated using the latest developed VQA v2 dataset with a 9% improvement over the results obtained with static word embedding. We also deployed the model as a cloud based VQA system to facilitate VQA tasks in real-life applications.

关键词： visualization Computer vision image coding Computational modeling Virtual assistants Optical character recognition Search engines

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A Practical Approach to Depth-Aware Augmentation for Neural Radiance Fields

A Practical Approach to Depth-Aware Augmentation for Neural ...

引用

IEEE visual communications and image processing (VCIP)

作者： Hamed Razavi Khosroshahi Jaime Sancho Daniele Bonatto Sarah Fachada Gun Bang Gauthier Lafruit Eduardo Juarez Mehrdad Teratani Laboratory of Image Synthesis and Analysis Universite Libre de Bruxelles Brussels Belgium Research Center on Software Technologies and Multimedia Systems Universidad Politecnica de Madrid Madrid Spain Electronics Telecommunication Research Institute Daejeon Korea

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Neural Radiance Fields (NeRF) have demonstrated exceptional performance in generating novel views of scenes by learning implicit volumetric representations from calibrated RGB images, without depth information. A major limitation is the need for large training datasets in neural network-based view synthesis frameworks. The challenge of effective data augmentation for view synthesis remains unresolved. NeRF models require extensive scene coverage from multiple views to accurately estimate radiance and density. Insufficient coverage reduces the model’s ability to interpolate or extrapolate unseen parts of the scene effectively. In this paper, we propose a novel pipeline to address this data augmentation issue using depth map information. We use depth image-based rendering (DIBR) to overcome the lack of enough views for training NeRF. Experimental results indicate that our approach enhances the quality of rendered images using the NeRF framework, achieving an average peak signal-to-noise ratio (PSNR) increase of 7.2 dB, with a maximum improvement of 12 dB.

关键词： Training Measurement PSNR visual communication image processing Pipelines Neural radiance field Data augmentation Rendering (computer graphics) Software

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Microscopic Fluorescence In Situ Hybridization (FISH) image Synthesis with Generative Adversarial Networks 29

Microscopic Fluorescence In Situ Hybridization (FISH) Image ...

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29th IEEE conference on Signal processing and communications Applications (SIU)

作者： Dursun, Gizem Ozkaya, Ufuk Suleyman Demirel Univ Elekt Elekt Muhendisligi Isparta Turkey

ISBN: (纸本)9781665436496

One of the most important problems in biomedical image analysis is the low amount of data and the cost of accessing to the marked data by researchers. In order to provide a solution to this problem, microscopic fluorescence in situ hybridization (FISH) images are synthesized with generative adversarial network in this paper. The generative adversarial network is trained to synthesize FISH images from mask images. The trained model was implemented on 150 test images and the performance of the model both was presented with visual results and evaluated quantitatively by calculating the performance metrics. By evaluating the synthesized FISH images in terms of image quality and structural features, it is observed that they can be used to provide a solution to the problem of the lack of data.

关键词： generative adversarial networks image synthesis fluorescence in situ hybridization

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