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Human pose based video compression via forward-referencing using deep learning

Human pose based video compression via forward-referencing u...

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IEEE visual communications and image processing (VCIP)

作者： S M Ataul Karim Rajin Manzur Murshed Manoranjan Paul Shyh Wei Teng Jiangang Ma Federation University Australia Deakin University Australia Charles Sturt University Australia

ISBN: (纸本)9781665475938

To exploit high temporal correlations in video frames of the same scene, the current frame is predicted from the already-encoded reference frames using block-based motion estimation and compensation techniques. While this approach can efficiently exploit the translation motion of the moving objects, it is susceptible to other types of affine motion and object occlusion/deocclusion. Recently, deep learning has been used to model the high-level structure of human pose in specific actions from short videos and then generate virtual frames in future time by predicting the pose using a generative adversarial network (GAN). Therefore, modelling the high-level structure of human pose is able to exploit semantic correlation by predicting human actions and determining its trajectory. Video surveillance applications will benefit as stored “big” surveillance data can be compressed by estimating human pose trajectories and generating future frames through semantic correlation. This paper explores a new way of video coding by modelling human pose from the already-encoded frames and using the generated frame at the current time as an additional forward-referencing frame. It is expected that the proposed approach can overcome the limitations of the traditional backward-referencing frames by predicting the blocks containing the moving objects with lower residuals. Our experimental results show that the proposed approach can achieve on average up to 2.83 dB PSNR gain and 25.93% bitrate savings for high motion video sequences compared to standard video coding.

关键词： Video coding Deep learning Correlation image coding visual communication Semantics Predictive models

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An Integrated Model for Generating 3D Point Clouds and Segmentation from 2D images

An Integrated Model for Generating 3D Point Clouds and Segme...

引用

International conference on Imaging, Signal processing and Communication (ICISPC)

作者： Huan-Yu Chen Chuen-Horng Lin Yan-Yu Lin Yung-Kuan Chan Department of Computer Science and Information Engineering National Taichung University of Science and Technology No. 129 Sec.3 Sanmin Rd Taichung Taiwan R.O.C Department of Management Information Systems National Chung Hsing University 145 Xingda Rd.South Dist Taichung Taiwan R.O.C

ISBN: (数字)9798350367157

ISBN: (纸本)9798350367164

This study presents a cost-effective approach to constructing simulation systems by integrating 2D visual processing with neural network technology for simultaneous 3D point cloud generation and part segmentation, thereby reducing time and costs. We propose multiple task-specific models using 2D RGB images, incorporating attention mechanisms, feature encoding, decoding layers, and innovative loss functions. The optimal models are merged to form an integrated system, evaluated using public and self-built datasets. Our experiments demonstrate that the integrated model effectively reduces training time and Floating Point Operations (FLOP) values while maintaining high accuracy in Critical Hole Distance (CHD), Earth Mover’s Distance (EMD), and Intersection over Union (IOU) metrics. This advancement enhances applications in military training, aerospace technology, and disaster response simulation, facilitating faster 3D object construction.

关键词： Training Point cloud compression image segmentation Solid modeling Adaptation models visualization Three-dimensional displays

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Lightweight Color image Demosaicking with Multi-Core Feature Extraction

Lightweight Color Image Demosaicking with Multi-Core Feature...

引用

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

作者： Tan, Yufei Chang, Kan Li, Hengxin Tang, Zhenhua Qin, Tuanfa Guangxi Univ Sch Comp & Elect Informat Nanning Peoples R China Guangxi Univ Guangxi Key Lab Multimedia Commun & Network Techn Nanning Peoples R China

ISBN: (纸本)9781728180687

Convolutional neural network (CNN)-based color image demosaicking methods have achieved great success recently. However, in many applications where the computation resource is highly limited, it is not practical to deploy large-scale networks. This paper proposes a lightweight CNN for color image demosaicking. Firstly, to effectively extract shallow features, a multi-core feature extraction module, which takes the Bayer sampling positions into consideration, is proposed. Secondly, by taking advantage of inter-channel correlation, an attention-aware fusion module is presented to efficiently r econstruct t he full color image. Moreover, a feature enhancement module, which contains several cascading attention-aware enhancement blocks, is designed to further refine t he i nitial r econstructed i mage. To demonstrate the effectiveness of the proposed network, several state-of-the-art demosaicking methods are compared. Experimental results show that with the smallest number of parameters, the proposed network outperforms the other compared methods in terms of both objective and subjective qualities.

关键词： convolutional neural network image demosaicking feature extraction channel attention residual learning

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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 ...

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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 ...

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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...

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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 ...

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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

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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...

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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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