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检索条件"任意字段=2021 International Conference on Visual Communications and Image Processing, VCIP 2021"

共 3164 条记录，以下是41-50 订阅

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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 international conference on visual communications and image processing (vcip) - visual communications in the Era of AI and Limited Resources

作者： Bhowmik, Deepayan Elawady, Mohamed Nogueira, Keiller Univ Stirling Div Comp Sci & Math Stirling FK9 4LA Scotland

ISBN: (纸本)9781728185514

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： Media forensics security learning based image coding JPEG AI DNN watermarking source identification

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An Error Self-learning Semi-supervised Method for No-reference image Quality Assessment

An Error Self-learning Semi-supervised Method for No-referen...

引用

IEEE international conference on visual communications and image processing (vcip) - visual communications in the Era of AI and Limited Resources

作者： Feng, Yingjie Li, Sumei Hao, Sihan Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728185514

In recent years, deep learning has achieved significant progress in many respects. However, unlike other research fields with millions of labeled data such as image recognition, only several thousand labeled images are available in image quality assessment (IQA) field for deep learning, which heavily hinders the development and application for IQA. To tackle this problem, in this paper, we proposed an error self-learning semi-supervised method for no-reference (NR) IQA (ESSIQA), which is based on deep learning. We employed an advanced full reference (FR) IQA method to expand databases and supervise the training of network. In addition, the network outputs of expanding images were used as proxy labels replacing errors between subjective scores and objective scores to achieve error self-learning. Two weights of error back propagation were designed to reduce the impact of inaccurate outputs. The experimental results show that the proposed method yielded comparative effect.

关键词： error self-learning semi-supervised no-reference image quality assessment

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Binocular visual Mechanism Guided No-Reference Stereoscopic image Quality Assessment Considering Spatial Saliency

Binocular Visual Mechanism Guided No-Reference Stereoscopic ...

引用

IEEE international conference on visual communications and image processing (vcip) - visual communications in the Era of AI and Limited Resources

作者： Feng, Jinhui Li, Sumei Chang, Yongli Tianjin Univ Tianjin Int Engn Inst Tianjin Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728185514

In recent years, with the popularization of 3D technology, stereoscopic image quality assessment (SIQA) has attracted extensive attention. In this paper, we propose a two-stage binocular fusion network for SIQA, which takes binocular fusion, binocular rivalry and binocular suppression into account to imitate the complex binocular visual mechanism in the human brain. Besides, to extract spatial saliency features of the left view, the right view, and the fusion view, saliency generating layers (SGLs) are applied in the network. The SGL apply multi-scale dilated convolution to emphasize essential spatial information of the input features. Experimental results on four public stereoscopic image databases demonstrate that the proposed method outperforms the state-of-the-art SIQA methods on both symmetrical and asymmetrical distortion stereoscopic images.

关键词： stereoscopic image quality assessment binocular visual mechanism spatial saliency convolutional neural network

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MPEG Immersive Video tools for Light Field Head Mounted Displays

MPEG Immersive Video tools for Light Field Head Mounted Disp...

引用

IEEE international conference on visual communications and image processing (vcip) - visual communications in the Era of AI and Limited Resources

作者： Bonatto, Daniele Hirt, Gregoire Kvasov, Alexander Fachada, Sarah Lafruit, Gauthier Univ Libre Bruxelles Labs Image Synth & Anal Brussels Belgium CREAL Lausanne Switzerland Fonds Rech Sci FNRS Brussels Belgium

ISBN: (纸本)9781728185514

Light field displays project hundreds of microparallax views for users to perceive 3D without wearing glasses. It results in gigantic bandwidth requirements if all views would be transmitted, even using conventional video compression per view. MPEG Immersive Video (MIV) follows a smarter strategy by transmitting only key images and some metadata to synthesize all the missing views. We developed (and will demonstrate) a real-time Depth image Based Rendering software that follows this approach for synthesizing all light field micro-parallax views from a couple of RGBD input views.

关键词： Demo View Synthesis DIBR Holography

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SMRD: A Local Feature Descriptor for Multi-modal image Registration

SMRD: A Local Feature Descriptor for Multi-modal Image Regis...

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IEEE international conference on visual communications and image processing (vcip) - visual communications in the Era of AI and Limited Resources

作者： Xie, Jiayu Jin, Xin Cao, Hongkun Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Key Lab Broadband Network & Multimedia Shenzhen Peoples R China

ISBN: (纸本)9781728185514

image registration among multimodality has received increasing attention in the scope of computer vision and computational photography nowadays. However, the nonlinear intensity variations prohibit the accurate feature points matching between modal-different image pairs. Thus, a robust image descriptor for multi-modal image registration is proposed, named shearlet-based modality robust descriptor(SMRD). The anisotropic feature of edge and texture information in multi-scale is encoded to describe the region around a point of interest based on discrete shearlet transform. We conducted the experiments to verify the proposed SMRD compared with several state-of-the-art multi-modal/multispectral descriptors on four different multi-modal datasets. The experimental results showed that our SMRD achieves superior performance than other methods in terms of precision, recall and F1-score.

关键词： Multi-modal image registration feature descriptor discrete shearlet transform

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A Video Dataset for Learning-based visual Data Compression and Analysis

A Video Dataset for Learning-based Visual Data Compression a...

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IEEE international conference on visual communications and image processing (vcip) - visual communications in the Era of AI and Limited Resources

作者： Xu, Xiaozhong Liu, Shan Li, Zeqiang Tencent Media Lab Palo Alto CA 94306 USA

ISBN: (纸本)9781728185514

Learning-based visual data compression and analysis have attracted great interest from both academia and industry recently. More training as well as testing datasets, especially good quality video datasets are highly desirable for related research and standardization activities. A UHD video dataset, referred to as Tencent Video Dataset (TVD), is established to serve various purposes such as training neural network-based coding tools and testing machine vision tasks including object detection and segmentation. This dataset contains 86 video sequences with a variety of content coverage. Each video sequence consists of 65 frames at 4K (3840x2160) spatial resolution. In this paper, the details of this dataset, as well as its performance when compressed by VVC and HEVC video codecs, are introduced.

关键词： 4K Dataset Video Compression Machine Learning Video Coding for Machines Object Detection Object Segmentation

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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 international conference on visual communications and image processing (vcip) - visual communications in the Era of AI and Limited Resources

作者： Zhou, Mingyue Li, Sumei Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728185514

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： stereoscopic image quality assessment (SIQA) human visual system (HVS) feedback mechanism deformable convolution

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Underwater image Enhancement with Multi-Scale Residual Attention Network

Underwater Image Enhancement with Multi-Scale Residual Atten...

引用

IEEE international conference on visual communications and image processing (vcip) - visual communications in the Era of AI and Limited Resources

作者： Ueki, Yosuke Ikehara, Masaaki Keio Univ EEE Dept Yokohama Kanagawa 2238522 Japan

ISBN: (纸本)9781728185514

Underwater images suffer from low contrast, color distortion and visibility degradation due to the light scattering and attenuation. Over the past few years, the importance of underwater image enhancement has increased because of ocean engineering and underwater robotics. Existing underwater image enhancement methods are based on various assumptions. However, it is almost impossible to define appropriate assumptions for underwater images due to the diversity of underwater images. Therefore, they are only effective for specific types of underwater images. Recently, underwater image enhancement algorisms using CNNs and GANS have been proposed, but they are not as advanced as other image processing methods due to the lack of suitable training data sets and the complexity of the issues. To solve the problems, we propose a novel underwater image enhancement method which combines the residual feature attention block and novel combination of multi-scale and multi-patch structure. Multi-patch network extracts local features to adjust to various underwater images which are often Non-homogeneous. In addition, our network includes multi-scale network which is often effective for image restoration. Experimental results show that our proposed method outperforms the conventional method for various types of images.

关键词： image processing image enhancement image restoration underwater image attention

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CROSS-COMPONENT SAMPLE OFFSET FOR image AND VIDEO CODING

CROSS-COMPONENT SAMPLE OFFSET FOR IMAGE AND VIDEO CODING

引用

IEEE international conference on visual communications and image processing (vcip) - visual communications in the Era of AI and Limited Resources

作者： Du, Yixin Zhao, Xin Liu, Shan Tencent Amer 2747 Pk Blvd Palo Alto CA 94306 USA

ISBN: (纸本)9781728185514

Existing cross-component video coding technologies have shown great potential on improving coding efficiency. The fundamental insight of cross-component coding technology is respecting the statistical correlations among different color components. In this paper, a Cross-Component Sample Offset (CCSO) approach for image and video coding is proposed inspired by the observation that, luma component tends to contain more texture, while chroma component is relatively smoother. The key component of CCSO is a nonlinear offset mapping mechanism implemented as a look-up-table (LUT). The input of the mapping is the co-located reconstructed samples of luma component, and the output is offset values applied on chroma component. The proposed method has been implemented on top of a recent version of libaom. Experimental results show that the proposed approach brings 1.16% Random Access (RA) BD-rate saving on top of AV1 with marginal encoding/decoding time increase.

关键词： Cross-component prediction video coding AV1 loop filtering

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No-Reference Stereoscopic image Quality Assessment Based on The visual Pathway of Human visual System

No-Reference Stereoscopic Image Quality Assessment Based on ...

引用

IEEE international conference on visual communications and image processing (vcip) - visual communications in the Era of AI and Limited Resources

作者： Meng, Fan Li, Sumei Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728185514

With the development of stereoscopic imaging technology, stereoscopic image quality assessment (SIQA) has gradually been more and more important, and how to design a method in line with human visual perception is full of challenges due to the complex relationship between binocular views. In this article, firstly, convolutional neural network (CNN) based on the visual pathway of human visual system (HVS) is built, which simulates different parts of visual pathway such as the optic chiasm, lateral geniculate nucleus (LGN), and visual cortex. Secondly, the two pathways of our method simulate the 'what' and 'where' visual pathway respectively, which are endowed with different feature extraction capabilities. Finally, we find a different application way for 3D-convolution, employing it fuse the information from left and right view, rather than just extracting temporal features in video. The experimental results show that our proposed method is more in line with subjective score and has good generalization.

关键词： Stereoscopic image quality assessment (SIQA) Convolutional neural network (CNN) the human visual system (HVS) 3D-convolution (C3D)

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