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HDR image Compression with Convolutional Autoencoder

HDR Image Compression with Convolutional Autoencoder

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Han, Fei Wang, Jin Xiong, Ruiqin Zhu, Qing Yin, Baocai Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Peking Univ Inst Digital Media Beijing Peoples R China

ISBN: (纸本)9781728180687

As one of the next- generation multimedia technology, high dynamic range (HDR) imaging technology has been widely applied. Due to its wider color range, HDR image brings greater compression and storage burden compared with traditional LDR image. To solve this problem, in this paper, a two-layer HDR image compression framework based on convolutional neural networks is proposed. The framework is composed of a base layer which provides backward compatibility with the standard JPEG, and an extension layer based on a convolutional variational autoencoder neural networks and a post-processing module. The autoencoder mainly includes a nonlinear transform encoder, a binarized quantizer and a nonlinear transform decoder. Compared with traditional codecs, the proposed CNN autoencoder is more flexible and can retain more image semantic information, which will improve the quality of decoded HDR image. Moreover, to reduce the compression artifacts and noise of reconstructed HDR image, a post-processing method based on group convolutional neural networks is designed. Experimental results show that our method outperforms JPEG XT profile A, B, C and other methods in terms of HDR-VDP-2 evaluation metric. Meanwhile, our scheme also provides backward compatibility with the standard JPEG.

关键词： Convolutional neural networks

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A Unified Single image De-raining Model via Region Adaptive Coupled Network

A Unified Single Image De-raining Model via Region Adaptive ...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Wu, Qingbo Chen, Li Ngan, King Ngi Li, Hongliang Meng, Fanman Xu, Linfeng Univ Elect Sci & Technol China Chengdu 611731 Peoples R China

ISBN: (纸本)9781728180687

Single image de-raining is quite challenging due to the diversity of rain types and inhomogeneous distributions of rainwater. By means of dedicated models and constraints, existing methods perform well for specific rain type. However, their generalization capability is highly limited as well. In this paper, we propose a unified de-raining model by selectively fusing the clean background of the input rain image and the well restored regions occluded by various rains. This is achieved by our region adaptive coupled network (RACN), whose two branches integrate the features of each other in different layers to jointly generate the spatial-variant weight and restored image respectively. On the one hand, the weight branch could lead the restoration branch to focus on the regions with higher contributions for de-raining. On the other hand, the restoration branch could guide the weight branch to keep off the regions with over-/under-filtering risks. Extensive experiments show that our method outperforms many state-of-the-art de-raining algorithms on diverse rain types including the rain streak, raindrop and rain-mist.

关键词： Single image de-raining region adaptive coupled network weighted fusion model

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PCGFormer: Lossy Point Cloud Geometry Compression via Local Self-Attention

PCGFormer: Lossy Point Cloud Geometry Compression via Local ...

引用

IEEE visual communications and image processing (VCIP)

作者： Gexin Liu Jianqiang Wang Dandan Ding Zhan Ma Hangzhou Normal University Hangzhou China Nanjing University Nanjing China

ISBN: (纸本)9781665475938

Although the multiscale sparse tensor using stacked convolutions has attained noticeable gains for lossy compression of point cloud geometry (PCG), its capability suffers because convolutions with fixed receptive field and fixed weights after training cannot aggregate sufficient information collection due to the extremely sparse and unevenly distributed nature of points. To best tackle the sparsity and adaptively exploit inter-point correlations, we apply local self-attention on $k$ nearest neighbors (kNN) that are instantaneously formed for each point, with which attention-based mechanism can effectively characterize and embed spatial information conditioned on the dynamic neighborhood. This kNN self-attention is implemented using the prevalent Transformer architecture and stacked with sparse convolutions to capture neighborhood information in a progres-sively re-sampling framework, referred to as the PCGFormer. Compared with the MPEG standard Geometry-based PCC (G-PCC) using the latest octree codec, the proposed PCGFormer provides more than 90% and 87% BD-rate (Bjøntegaard Delta Rate) reduction in average across three different object point cloud datasets for point-to-point (D1) and point-to-plane (D2) distortion measures. Compared with the state-of-the-art learning-based approach, the PCGFormer achieves 17.39% and 15.75% BD-rate gains on D1 and D2, respectively.

关键词： Point cloud compression Geometry Convolutional codes Training MPEG standards image coding Tensors

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Medical image Fusion Based on Weighted Least Square Optimization and Deep Learning Algorithm 9

Medical Image Fusion Based on Weighted Least Square Optimiza...

引用

9th International Japan-Africa conference on Electronics, communications, and Computations, JAC-ECC 2021

作者： Ghandour, C. El-Shafai, Walid El-Rabaie, S. Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh Saudi Arabia

ISBN: (纸本)9781665482929

Recently, medical image processing has become a hot area of research, especially with the rapid development in technology and instrumentation, and that's because of its effective role in the health sector. So that it becomes a very active research tool. This research introduces an image fusion algorithm that utilizes a deep learning model to produce only one medical fused image that includes all the traits from the medical source images. Firstly, the source images are separated into detailed content and base parts using the Gaussian and rolling guidance filters (RGF). Secondly, by the weighted averaging strategy, the base parts are fused. For the detail content, to quote traits of multi-layer which employ weighted average strategy to produce the fused detail content several candidates, the deep learning network is utilized. The max selection technique is employed to gain the last detailed content as soon as these candidates are gotten. Eventually, by uniting the fused detail and base layers, the fused image will be recreated. The experimental outcomes show that this algorithm can accomplish better results by comparing the other fusion methods in both thematic assessment and visual quality. © 2021 IEEE.

关键词： Learning algorithms

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EyeRis: visual image Recognition using Machine Learning for the visually-Impaired

EyeRis: Visual Image Recognition using Machine Learning for ...

引用

International conference on Electronics, Information and communications (ICEIC)

作者： Alliah May Eugenio Marian Jowie Patulot Lykha Jane Seguiro Angelica Nicole Tuazon Shekinah Lor Huyo-a Mideth Abisado Gabriel Avelino Sampedro Department of Computer Science Technological University of the Philippines Manila Philippines Philippine Coding Camp Research and Development Center Manila Philippines College of Computing and Information Technologies National University Manila Philippines Faculty of Information and Communication Studies University of the Philippines Open University Laguna Philippines

visually impaired people struggle daily and have difficulty recognizing and distinguishing objects around them. Thus, they mainly depend on supervision from other people to assist them. Since smartphones have become a necessity in this modern world, the researchers formulated a solution to help the visually impaired through a machine learning-based mobile application for object recognition. Nowadays, software applications can provide accurate findings in picture classification and processing processes thanks to machine learning techniques and algorithms. In this study, the researchers use a convolutional neural network (CNN)-based system on TensorFlow Lite to create a mobile version of a visual information system employing a machine learning strategy and deep learning framework. The main objectives of the smartphone application, EyeRis, is to recognize and categorize items in real-time and to separate photographs from the user-selected scenarios. Results were analyzed and contrasted based on the app’s obtained recognition accuracy data. It demonstrated the utility of CNN as a model for image recognition algorithms.

关键词： visualization image recognition Machine learning algorithms Software algorithms Neural networks Software Real-time systems

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Volumetric End-to-End Optimized Compression for Brain images

Volumetric End-to-End Optimized Compression for Brain Images

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Gao, Shuo Zhang, Yueyi Liu, Dong Xiong, Zhiwei Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781728180687

The amount of volumetric brain image increases rapidly, which requires a vast amount of resources for storage and transmission, so it's urgent to explore an efficient volumetric compression method. Recent years have witnessed the progress of deep learning-based approaches for two-dimensional (2D) natural image compression, but the field of learned volumetric image compression still remains unexplored. In this paper, we propose the first end-to-end learning framework for volumetric image compression by extending the advanced techniques of 2D image compression to volumetric images. Specifically, a convolutional autoencoder is used to compress 3D image cubes, and the non-local attention models are embedded in the convolutional autoencoder to jointly capture local and global correlations. Both hyperprior and autoregressive models are used to perform the conditional probability estimation in entropy coding. To reduce model complexity, we introduce a convolutional long short-term memory network for the autoregressive model based on channel-wise prediction. Experimental results on volumetric mouse brain images show that the proposed method outperforms JPEG2000-3D, HEVC and state-of-the-art 2D methods.

关键词： Volumetric image compression 3D convolutional neural network convolutional long short-term memory

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One Picture is Worth a Thousand Words: A New Wallet Recovery Process

One Picture is Worth a Thousand Words: A New Wallet Recovery...

引用

GLOBECOM 2022 - 2022 IEEE Global communications conference

作者： Hervé Chabanne Vincent Despiegel Linda Guiga IDEMIA and Télécom Paris IDEMIA

ISBN: (纸本)9781665435413

We introduce a new wallet recovery pro-cess. Our solution associates 1) visual passwords: a photograph ofa secretly picked object (Chabanne et aI., 2013) with 2) imageNet classifiers transforming images into binary vectors and, 3) obfuscated fuzzy matching (Galbraith and Zobernig, 2019) for the storage of visual passwords/retrieval of wallet seeds. Our experiments show that the replacement of long seed phrases by a photograph is possible.

关键词： visualization Databases image processing Face recognition Passwords Machine learning Museums

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A Hybrid Model for Natural Face De-Identiation with Adjustable Privacy

A Hybrid Model for Natural Face De-Identiation with Adjustab...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Wen, Yunqian Liu, Bo Xie, Rong Zhu, Yunhui Cao, Jingyi Song, Li Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Univ Technol Sydney Sch Comp Sci Sydney NSW Australia

ISBN: (纸本)9781728180687

As more and more personal photos are shared and tagged in social media, security and privacy protection are becoming an unprecedentedly focus of attention. Avoiding privacy risks such as unintended verification, b ecomes increasingly challenging. To enable people to enjoy uploading photos without having to consider these privacy concerns, it is crucial to study techniques that allow individuals to limit the identity information leaked in visual data. In this paper, we propose a novel hybrid model consists of two stages to generate visually pleasing deidentified f ace i mages a ccording t o a s ingle i nput. Meanwhile, we successfully preserve visual similarity with the original face to retain data usability. Our approach combines latest advances in GAN-based face generation with well-designed adjustable randomness. In our experiments we show visually pleasing deidentified output of our method while preserving a high similarity to the original image content. Moreover, our method adapts well to the verificator of unknown structure, which further improves the practical value in our real life.

关键词： privacy protection multi-level features fusion GAN-based face generation randomness

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An Efficient image Retrieval Model with Convolutional Neural Network based Text/image Identification for Copyright Violation Detection

An Efficient Image Retrieval Model with Convolutional Neural...

引用

IEEE Signal processing and communications Applications (SIU)

作者： Harun Ö zden Bü lent Tavlı M. Fatih Demirci &#x0130 leti&#x015F im Teknolojileri Direkt&#x00F6 rl&#x00FC &#x011F &#x00FC Anadolu Ajans&#x0131 Elektrik-Elektronik M&#x00FC hendisli&#x011F i TOBB Ekonomi ve Teknoloji &#x00DC niversitesi Bilgisayar M&#x00FC i B. Ankara Y&#x0131 ld&#x0131 r&#x0131 m Beyaz&#x0131 t &#x00DC

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450935

One of the most important problems faced by broadcasters is the unauthorized use of their images by third parties or organizations in a large-scale database, which contains hundreds of thousands of images. For this reason, it is important to perform an efficient and effective image retrieval, whose objective is to find the most similar images to a given test image. In addition, test images often contain text, and the presence of the text together with the visual part complicates the search process. In this paper, we present an image retrieval framework based on a bag of visual words, which has been shown to be effective in the literature. A convolutional neural network model is used to parse the text in the images. Experiments demonstrate the efficacy of this model in a large database.

关键词： visualization Databases image retrieval Organizations Signal processing Convolutional neural networks

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Use of Depth Completion and SLAM Algorithm to Build Dense Maps of Large Scenes 6

Use of Depth Completion and SLAM Algorithm to Build Dense Ma...

引用

6th International conference on Signal and image processing, ICSIP 2021

作者： Jia, Yongxing Li, Mingcan Xu, Fenghui Yu, Jing Army Engineering University of PLA College of Communications Engineering Nanjing China

ISBN: (纸本)9780738133737

Limited by the range of sensor, constructing a dense outdoor map of large scene with a depth camera is difficult. Since the state-of-the-art performance on data fusion for pixel-level distance estimation, this paper proposes a mapping system which employs depth image generated by an unsupervised Lidar-Stereo network to achieve accurate reconstruction. Firstly, in the ORBSLAM2 stereo mode, the system estimates the pose information to finish camera localization. and in another thread LidarStereoNet is used to generate a depth image within visual angle to describe the pixel-level distance. Then, for correcting the completed depth image to truth ground, a nonlinear transformation between the generation and the rendering is performed to restore the accurate distance relationship. Finally, the RGB image, pose information and depth rendering image are entered into the dense mapping module to get a large-scale point cloud model. After running on KITTI dataset, the experiment results show that the system can construct a pretty comprehensive scene model after only one trip, and using the numerical truncation and filtering methods to extract usable areas can make the mapping results more accurate. © 2021 IEEE.

关键词： Mapping

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