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Perceptual Evaluation of Pre-processing for Video Transcoding

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

引用

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...

引用

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...

引用

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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Deep Learning-Based Multiple Detection Techniques of Covid-19 Disease From Chest X-Ray images Using Advanced image processing Methods and Transfer Learning 1st

Deep Learning-Based Multiple Detection Techniques of Covid-1...

引用

1st Analytics Global conference, AGC 2023

作者： Hussain, Arif Basak, Rohini Mandal, Sourav Department of Informtion Technology Jadavpur University West Bengal Kolkata700098 India School of Computer Scinece and Engineering XIM University Odisha Bhubaneswar752050 India

ISBN: (纸本)9783031508141

The 2019 new coronavirus (COVID-19), which originally surfaced in the Chinese city of Wuhan in December 2019, expanded quickly, and caused a pandemic. It has created a terrible influence on daily lives, public health, and the global economy. To stop the pandemic from spreading further and to treat the affected individuals as soon as feasible, it is essential to identify positive cases as soon as possible. One of the primary issues in the present COVID-19 pandemic is the early detection and diagnosis of COVID-19, as well as the precise separation of non-COVID-19 cases at the lowest cost and in the early stages of the disease. This study compares the usage of the most recent Convolutional Neural Network (CNN), a deep learning methodology employing networks, with a transfer learning technique using VGG and ResNet. With their assistance, we created models that can recognize COVID-19 in X-Ray photos. We have applied techniques of image preprocessing using Contrast Limited Adaptive Histogram Equalization (CLAHE) and Adaptive Histogram Equalization (AHE). We built the model by applying CNN. We have also applied transfer learning techniques using visual Geometry Group (VGG16) and Residual Network (ResNet50) after image preprocessing using CLAHE and AHE. We have compared the accuracy of all models. The CLAHE technique along with ResNet50 works well as we achieve an accuracy of 0.99 It can be stated that ResNet50 with CLAHE played a very significant role in determining the covid-19 in X-ray images. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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Security and Forensics Exploration of Learning-based image Coding

Security and Forensics Exploration of Learning-based Image C...

引用

IEEE visual communications and image processing (VCIP)

作者： Deepayan Bhowmik Mohamed Elawady Keiller Nogueira Division of Computing Science & Mathematics University of Stirling Stirling UK

ISBN: (纸本)9781728173221

Advances in media compression indicate significant potential to drive future media coding standards, e.g., Joint Photographic Experts Group's learning-based image coding technologies (JPEG AI) and Joint Video Experts Team's (JVET) deep neural networks (DNN) based video coding. These codecs in fact represent a new type of media format. As a dire consequence, traditional media security and forensic techniques will no longer be of use. This paper proposes an initial study on the effectiveness of traditional watermarking on two state-of-the-art learning based image coding. Results indicate that traditional watermarking methods are no longer effective. We also examine the forensic trails of various DNN architectures in the learning based codecs by proposing a residual noise based source identification algorithm that achieved 79% accuracy.

关键词： Deep learning Video coding image coding Codecs visual communication Forensics Transform coding

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A 3D MRI Brain image Segmentation and Reconstruction System Based on Augmented Reality Technology 10th

A 3D MRI Brain Image Segmentation and Reconstruction System ...

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10th China Health Information processing conference, CHIP 2024

作者： Zhao, Wang Lu, Peixin Hu, LianTing Lu, Long School of Management Engineering Henan University of Engineering Zhengzhou China School of Safety Science and Emergency Management Wuhan University of Technology Wuhan China Guangdong Provincial People’s Hospital Guangzhou China School of Information Management Wuhan University Wuhan China The Center for Healthcare Big Data Research The Big Data Institute Wuhan University Wuhan China Institute of Pediatrics Guangzhou Women and Children’s Medical Center Guangzhou Medical University Guangzhou China School of Public Health Wuhan University Wuhan China

ISBN: (纸本)9789819637546

Background With the rapid development of information technology and the digitization of medical devices, various diseases require the use of medical imaging equipment for diagnosis. At present, various medical imaging diagnostic equipment such as CT and nuclear magnetic resonance can provide two-dimensional planar images of diseases. Doctors urgently need to accurately determine the spatial location, size, geometry, and spatial relationship with the surrounding tissue. Therefore, it is very important to use computer technology to segment 3D MRI images, determine the location of lesions, and then perform 3D reconstruction. Method At present, automatic recognition and marking of brain images are displayed in two dimensions. Therefore, it is necessary to use 3D visualization technology for reconstruction. In addition, it can be combined with virtual and real, and some additional information is superimposed on the brain image for integrated display. In addition, a combination of virtual and real needs to be superimposed, and some additional information is superimposed on the brain image for integrated display. The research focus of this paper includes two main parts: disease segmentation and 3D reconstruction visualization. Firstly, the disease segmentation method based on 3D MRI brain image files was designed, and then the feature extraction and 3D reconstruction functions were designed. Thereby forming a complete process of disease region segmentation and three-dimensional reconstruction. Results This study is based on a three-dimensional MRI brain image segmentation algorithm. The algorithm is advanced in technology, high in accuracy, and can effectively identify the location of the disease. Then, this study used the Unity tool to implement a three-dimensional reconstruction and visual display program for brain image disease segmentation. Therefore, the doctor can quickly and intuitively grasp the spatial information inside the brain and the information of the lesion

关键词： image segmentation

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Multicomponent Secondary Transform

Multicomponent Secondary Transform

引用

IEEE visual communications and image processing (VCIP)

作者： Madhu P Krishnan Xin Zhao Shan Liu Tencent America Palo Alto CA United States

ISBN: (纸本)9781728173221

The Alliance for Open Media has recently initiated coding tool exploration activities towards the next-generation video coding beyond AV1. In this regard, a frequency-domain coding tool, which is designed to leverage the cross-component correlation existing between collocated chroma blocks, is explored in this paper. The tool, henceforth known as multi-component secondary transform (MCST), is implemented as a low complexity secondary transform with primary transform coefficients of multiple color components as input. The proposed tool is implemented and tested on top of libaom. Experimental results show that, compared to libaom, the proposed method achieves an average 0.34% to 0.44% overall coding efficiency for All Intra (AI) coding configuration for a wide range of video content.

关键词： Video coding image coding Correlation image color analysis visual communication Frequency-domain analysis Transforms

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Deformable Convolution Based No-Reference Stereoscopic image Quality Assessment Considering visual Feedback Mechanism

Deformable Convolution Based No-Reference Stereoscopic Image...

引用

IEEE visual communications and image processing (VCIP)

作者： Mingyue Zhou Sumei Li School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (纸本)9781728173221

Simulation of human visual system (HVS) is very crucial for fitting human perception and improving assessment performance in stereoscopic image quality assessment (SIQA). In this paper, a no-reference SIQA method considering feedback mechanism and orientation selectivity of HVS is proposed. In HVS, feedback connections are indispensable during the process of human perception, which has not been studied in the existing SIQA models. Therefore, we design a new feedback module (FBM) to realize the guidance of the high-level region of visual cortex to the low-level region. In addition, given the orientation selectivity of primary visual cortex cells, a deformable feature extraction block is explored to simulate it, and the block can adaptively select the regions of interest. Meanwhile, retinal ganglion cells (RGCs) with different receptive fields have different sensitivities to objects of different sizes in the image. So a new multi receptive fields information extraction and fusion manner is realized in the network structure. Experimental results show that the proposed model is superior to the state-of-the-art no-reference SIQA methods and has excellent generalization ability.

关键词： image quality Deformable models visualization Adaptation models Sensitivity Convolution visual communication

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AnomalyHop: An SSL-based image Anomaly Localization Method

AnomalyHop: An SSL-based Image Anomaly Localization Method

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IEEE visual communications and image processing (VCIP)

作者： Kaitai Zhang Bin Wang Wei Wang Fahad Sohrab Moncef Gabbouj C.-C. Jay Kuo University of Southern California Los Angeles USA Tampere University Tampere Finland

ISBN: (纸本)9781728173221

An image anomaly localization method based on the successive subspace learning (SSL) framework, called Anomaly-Hop, is proposed in this work. AnomalyHop consists of three modules: 1) feature extraction via successive subspace learning (SSL), 2) normality feature distributions modeling via Gaussian models, and 3) anomaly map generation and fusion. Comparing with state-of-the-art image anomaly localization methods based on deep neural networks (DNNs), AnomalyHop is mathematically transparent, easy to train, and fast in its inference speed. Besides, its area under the ROC curve (ROC-AUC) performance on the MVTec AD dataset is 95.9%, which is among the best of several benchmarking methods.

关键词： Location awareness Deep learning visual communication Neural networks Benchmark testing Feature extraction Mathematical models

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