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4th International conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th International conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249778

The proceedings contain 113 papers. The special focus in this conference is on Applied Technologies. The topics include: Automatic Evaluation of Physiotherapy Activities Using Deep Learning Techniques;multivariable Control Approach Applied to the Embryo Incubation Process of Gallus Gallus Domesticus;discovering visual Deficiencies in Pilots Candidates Using Data Mining;an Electronic Equipment for Measuring Color Difference Between Tissues Based on Digital image processing and Neural Networks;artificial Intelligence-Based Banana Ripeness Detection;U-Net vs. TransUNet: Performance Comparison in Medical image Segmentation;access Control Through Mask Detection and Estimation of People Capacity in Covered Premises;customer Segmentation in Food Retail Sector: An Approach from Customer Behavior and Product Association Rules;Bibliometric Analysis of Web of Science Database STEM Fields in Engineering and Mathematics. Ecuador’s Case Study;smart Antenna Array for Optimal Electromagnetic Energy Capture;image processing Method to Estimate Water Quality Parameter;use of the Student Engagement as a Strategy to Optimize Online Education, Applying a Supervised Machine Learning Model Using Facial Recognition;crime Data Analysis Using Machine Learning Models;Gene Therapy as a Solution to Genetic Diseases Through DNA Manipulation;Vulnerability of CAPTCHA Systems Using Bots with Computer Vision Abilities;military Leadership in the Ecuadorian Army;analysis of Key Variables for Ecuadorian Defense Industry Development;the Prioritization of External Security as a Means of Guaranteeing Multidimensional Security and Economic Growth;Comparative Study of Deep Learning Algorithms in the Detection of Phishing Attacks Based on HTML and Text Obtained from Web Pages;ground Robot for Search and Rescue Management;virtual Training Module for the Production of Rubber Adhesives Through the Production of Cyclopentenol.

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4th International conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th International conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249709

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4th International conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th International conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249846

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Subjective Quality Assessment of HDR Stereoscopic Omnidirectional Videos

Subjective Quality Assessment of HDR Stereoscopic Omnidirect...

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International conference on Communication Technology (ICCT)

作者： Manman Liu Jihao You Ruitao Chen Ziwei Ye Gangyi Jiang Faculty of Information Science and Engineering Ningbo University Ningbo China

High dynamic range stereoscopic omnidirectional video (HSOV) can bring wide field of view and high contrast visual experience to viewers. However, the generation, transmission and display of HSOV will inevitably cause distortions in the video. Therefore, it is important to quantify the impact of the processing of coding and tone mapping on the quality of HSOV. To this end, the subjective quality of HSOV after HEVC coding and tone mapping is evaluated in this paper. Firstly, an HSOV database namely NBU-HSOVD is constructed, which consists of 450 distorted HSOVs. Then, 34 subjects are invited to evaluate the quality of the distorted videos with absolute category rating method so that the MOS value of each video can be provided. Finally, the performance of six existing objective image or video quality assessment methods are tested on NBU-HSOVD. The constructed NBU-HSOVD database can provide reference and basis for the research of objective quality assessment of HSOV in the future.

关键词： visualization image analysis Databases Stereo image processing Distortion Encoding communications technology

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Parallelized Context Modeling for Faster image Coding

Parallelized Context Modeling for Faster Image Coding

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IEEE visual communications and image processing (VCIP)

作者： A. Burakhan Koyuncu Kai Cui Atanas Boev Eckehard Steinbach Chair of Media Technology Technical University of Munich Munich Germany Audiovideo Technology Lab Huawei Munich Research Center Munich Germany

ISBN: (纸本)9781728173221

Learning-based image compression has reached the performance of classical methods such as BPG. One common approach is to use an autoencoder network to map the pixel information to a latent space and then approximate the symbol probabilities in that space with a context model. During inference, the learned context model provides symbol probabilities, which are used by the entropy encoder to obtain the bitstream. Currently, the most effective context models use autoregression, but autoregression results in a very high decoding complexity due to the serialized data processing. In this work, we propose a method to parallelize the autoregressive process used for image compression. In our experiments, we achieve a decoding speed that is over 8 times faster than the standard autoregressive context model almost without compression performance reduction.

关键词： image coding visual communication Information sharing Network architecture Data processing Entropy Data models

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GPU-Intensive Fast Entropy Coding Framework for Neural image Compression

GPU-Intensive Fast Entropy Coding Framework for Neural Image...

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IEEE visual communications and image processing (VCIP)

作者： Hiroaki Akutsu Takahiro Naruko Akifumi Suzuki Research & Development Group Hitachi Ltd. Japan

ISBN: (纸本)9781728173221

Neural compression has benefited from technological advances such as convolutional neural networks (CNNs) to achieve advanced bitrates, especially in image compression. In neural image compression, an encoder and a decoder can run in parallel on a GPU, so the speed is relatively fast. However, the conventional entropy coding for neural image compression requires serialized iterations in which the probability distribution is estimated by multi-layer CNNs and entropy coding is processed on a CPU. Therefore, the total compression and decompression speed is slow. We propose a fast, practical, GPU-intensive entropy coding framework that consistently executes entropy coding on a GPU through highly parallelized tensor operations, as well as an encoder, decoder, and entropy estimator with an improved network architecture. We experimentally evaluated the speed and rate-distortion performance of the proposed framework and found that we could significantly increase the speed while maintaining the bitrate advantage of neural image compression.

关键词： Multiplexing image coding Tensors visual communication Bit rate Redundancy Graphics processing units

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Perceptual Evaluation of Pre-processing for Video Transcoding

Perceptual Evaluation of Pre-processing for Video Transcodin...

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IEEE visual communications and image processing (VCIP)

作者： Shiyu Huang Ziyuan Luo Jiahua Xu Wei Zhou Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781728173221

Recently, the pre-processed video transcoding has attracted wide attention and has been increasingly used in practical applications for improving the perceptual experience and saving transmission resources. However, very few works have been conducted to evaluate the performance of pre-processing methods. In this paper, we select the source (SRC) videos and various pre-processing approaches to construct the first Pre-processed and Transcoded Video Database (PTVD). Then, we conduct the subjective experiment, showing that compared with the video sent to the codec directly at the same bitrate, the appropriate pre-processing methods indeed improve the perceptual quality. Finally, existing image/video quality metrics are evaluated on our database. The results indicate that the performance of the existing image/video quality assessment (IQA/VQA) approaches remain to be improved. We will make our database publicly available soon.

关键词： Measurement Codecs Databases visual communication image processing Buildings Bit rate

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Stereoscopic Video Quality Assessment with Multi-level Binocular Fusion Network Considering Disparity and Multi-scale Information

Stereoscopic Video Quality Assessment with Multi-level Binoc...

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IEEE visual communications and image processing (VCIP)

作者： Yingjie Feng Sumei Li School of Electrical and Information Engineering Tianjin University TianJin China

ISBN: (纸本)9781728173221

Stereoscopic video quality assessment (SVQA) is of great importance to promote the development of the stereoscopic video industry. In this paper, we propose a three-branch multi-level binocular fusion convolutional neural network (MBFNet) which is highly consistent with human visual perception. Our network mainly includes three innovative structures. Firstly, we construct a multi-scale cross-dimension attention module (MSCAM) on the left and right branches to capture more critical semantic information. Then, we design a multi-level binocular fusion unit (MBFU) to fuse the features from left and right branches adaptively. Besides, a disparity compensation branch (DCB) containing an enhancement unit (EU) is added to provide disparity feature. The experimental results show that the proposed method is superior to other existing SVQA methods with state-of-the-art performance.

关键词： Industries visual communication Fuses Stereo image processing image processing Semantics Quality assessment

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Fast and Context-Aware Framework for Space-Time Video Super-Resolution

Fast and Context-Aware Framework for Space-Time Video Super-...

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IEEE visual communications and image processing (VCIP)

作者： Xueheng Zhang Li Chen Li Song Zhiyong Gao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781728173221

Increasing the spatial resolution and frame rate of a video simultaneously has attracted attention in recent years. The current one-stage space-time video super-resolution (STVSR) methods are difficult to deal with large motion and complex scenes, and are time-consuming and memory intensive. We propose an efficient STVSR framework, which can correctly handle complicated scenes such as occlusion and large motion and generate results with clearer texture. In REDS dataset, our method outperforms all existing one-stage methods. Our method is lightweight and can generate 720p frames at 16fps on a NVIDIA GTX 1080 Ti GPU.

关键词： visual communication Superresolution Graphics processing units Spatial resolution image reconstruction

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Revisiting Flipping Strategy for Learning-based Stereo Depth Estimation

Revisiting Flipping Strategy for Learning-based Stereo Depth...

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IEEE visual communications and image processing (VCIP)

作者： Yue Li Yueyi Zhang Zhiwei Xiong University of Science and Technology of China Hefei China

ISBN: (纸本)9781728173221

Deep neural networks (DNNs) have been widely used for stereo depth estimation, which achieve great success in performance. In this paper, we introduce a novel flipping strategy for DNN on the stereo depth estimation task. Specifically, based on a common DNN for stereo matching, we apply the flipping operation for both input stereo images, which are further fed to the original DNN. A flipping loss function is proposed to jointly train the network with the initial loss. We apply our strategy to many representative networks in both supervised and self-supervised manners. Extensive experimental results demonstrate that our proposed strategy improves the performance of these networks.

关键词： Training Deep learning Costs visual communication image processing Neural networks Estimation

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