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共 25554 条记录，以下是381-390 订阅

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Perceptual Evaluation of Pre-processing for Video Transcoding

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

引用

ieee International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Huang, Shiyu Luo, Ziyuan Xu, Jiahua Zhou, Wei Chen, Zhibo Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei Peoples R China

ISBN: (纸本)9781728185514

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： Video quality assessment Subjective evaluation Database Pre-processing for video transcoding

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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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Towards Universal GAN image Detection

Towards Universal GAN Image Detection

引用

ieee International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Cozzolino, Davide Gragnaniello, Diego Poggi, Giovanni Verdoliva, Luisa Univ Federico II Naples Naples Italy

ISBN: (纸本)9781728185514

The ever higher quality and wide diffusion of fake images have spawn a quest for reliable forensic tools. Many GAN image detectors have been proposed, recently. In real world scenarios, however, most of them show limited robustness and generalization ability. Moreover, they often rely on side information not available at test time, that is, they are not universal. We investigate these problems and propose a new GAN image detector based on a limited sub-sampling architecture and a suitable contrastive learning paradigm. Experiments carried out in challenging conditions prove the proposed method to be a first step towards universal GAN image detection, ensuring also good robustness to common image impairments, and good generalization to unseen architectures.

关键词： Fake detection

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Inter-Observer visual Congruency in Video-Viewing

Inter-Observer Visual Congruency in Video-Viewing

引用

ieee International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Yue, Jiaomin Lu, Qiang Zhu, Dandan Min, Xiongkuo Zhang, Xiao-Ping Zhai, Guangtao Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON Canada

ISBN: (纸本)9781728185514

There are individual differences in human visual attention between observers when viewing the same scene. Inter-observer visual congruency (IOVC) describes the dispersion between different people's visual attention areas when they observe the same stimulus. Research on the IOVC of video is interesting but lacking. In this paper, we first introduce the measurement to calculate the IOVC of video. And an eye-tracking experiment is conducted in a realistic movie-watching environment to establish a movie scene dataset. Then we propose a method to predict the IOVC of video, which employs a dual-channel network to extract and integrate content and optical flow features. The effectiveness of the proposed prediction model is validated on our dataset. And the correlation between inter-observer congruency and video emotion is analyzed.

关键词： Inter-observer visual congruency visual attention neural network movie analysis

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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-...

引用

ieee International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Zhang, Xueheng Chen, Li Song, Li Gao, Zhiyong Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China

ISBN: (纸本)9781728185514

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： space-time video super-resolution frame interpolation deep learning

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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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No-reference Stereoscopic image Quality Assessment Based on Parallel Multi-scale Perception

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

引用

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

作者： Zhang, Ziyi Li, Sumei Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781665475921

With the rapid development of 3D technologies, effective no-reference stereoscopic image quality assessment (NR-SIQA) methods are in great demand. In this paper, we propose a parallel multi-scale feature extraction convolution neural network (CNN) model combined with novel binocular feature interaction consistent with human visual system (HVS). In order to simulate the characteristics of HVS sensing multi-scale information at the same time, parallel multi-scale feature extraction module (PMSFM) followed by compensation information is proposed. And modified convolutional block attention module (MCBAM) with less computational complexity is designed to generate visual attention maps for the multi-scale features extracted by the PMSFM. In addition, we employ cross-stacked strategy for multi-level binocular fusion maps and binocular disparity maps to simulate the hierarchical perception characteristics of HVS. Experimental results show that our method is superior to the state-of-the-art metrics and achieves an excellent performance.

关键词： no-reference stereoscopic image quality assessment (NR-SIQA) convolution neural network (CNN) human visual system (HVS)

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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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DesnowFormer: an effective transformer-based image desnowing network

DesnowFormer: an effective transformer-based image desnowing...

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

作者： Zhang, Ting Jiang, Nanfeng Lin, Junhong Lin, Jielian Zhao, Tiesong Fuzhou Univ Coll Phys & Informat Engn Fujian Key Lab Intelligent Proc & Wireless Transm Fuzhou Peoples R China

ISBN: (纸本)9781665475921

Single image desnowing is an important and challenge task for lots of computer vision applications, such as visual tracking and video surveillance. Although existing deep learning-based methods have achieved promising results, most of them rely on the local deep features and neglect global relationship information between the local regions. Therefore, inevitably leading to over-smooth or detail loss results. To solve this issue, we design a UNet-based end-to-end architecture for image desnowing. Specially, to better characterize global information and preserve image detail, we combine Window-based Self-Attention (WSA) transformer block with Residue Spatial Attention (RSA) to build basic unit of our network. Besides, to protect the structure of the image effectively, we also introduce a Residue Channel (RC) loss to guide high-quality image restoration. Extensive experimental results on both synthetic and real-world datasets demonstrate that the proposed model achieves new state-of-the-art results.

关键词： image Desnowing Transformer block Residual Spatial Attention

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Optimized MobileNetV2 Based on Model Pruning for image Classification

Optimized MobileNetV2 Based on Model Pruning for Image Class...

引用

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

作者： Xiao, Peng Pang, Yuliang Feng, Hao Hao, Yu Xian Univ Posts & Telecommunicat Ctr Image & Informat Proc (CIIP) Xian Peoples R China

ISBN: (纸本)9781665475921

Due to the large memory requirement and a large amount of computation, traditional deep learning networks cannot run on mobile devices as well as embedded devices. In this paper, we propose a new mobile architecture combining MobileNetV2 and pruning, which further decreases the Flops and number of parameters. The performance of MobileNetV2 has been widely demonstrated, and pruning operation can not only allow further model compression but also prevent overfitting. We have done ablation experiments at CIIP Tire Data for different pruning combinations. In addition, we introduced a global hyperparameter to effectively weigh the accuracy and precision. Experiments show that the accuracy of 98.3 % is maintained under the premise that the model size is only 804.5 KB, showing better performance than the baseline method.

关键词： model compression MobileNetV2 pruning

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