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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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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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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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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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Subjective and Objective Quality Assessment of the SoftCast Video Transmission Scheme

Subjective and Objective Quality Assessment of the SoftCast ...

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ieee international conference on visual communications and image processing (vcip)

作者： Trioux, Anthony Valenzise, Giuseppe Cagnazzo, Marco Kieffer, Michel Coudoux, Francois-Xavier Corlay, Patrick Gharbi, Mohamed Univ Lille Univ Polytech Hauts de France CNRS YNCREADOAEUMR IEMN 8520Cent Lille F-59313 Valenciennes France Inst Polytech Paris Telecom ParisTech LTCI Paris France Univ Paris Saclay CNRS CentraleSupelec L2S F-91192 Gif Sur Yvette France

ISBN: (纸本)9781728180687

SoftCast-based linear video coding and transmission (LVCT) schemes have been proposed as a promising alternative to traditional video coding and transmission schemes in wireless environments. Currently, the performance of LVCT schemes is evaluated by means of traditional objective scores such as PSNR or SSIM. Nevertheless, since the compression is performed in a very different way from traditional coding schemes such as HEVC, visual artifacts are also quite different and deserve to be subjectively assessed. In this paper, we propose a subjective quality assessment of SoftCast, pioneer and standard of the LVCT schemes. This study aims to better understand the trade-offs between the LVCT parameters that can be tuned to improve the quality. These parameters, including different GoP-sizes, Compression Ratios (CR) and Channel Signal-to-Noise Ratio (CSNR), are used to generate a dataset of 85 videos. A Double Stimulus Impairment Scale (DSIS) test is performed on the received videos to assess the perceived quality. Results show that the key characteristic of SoftCast, the linear relation between CSNR and PSNR, is also observed with the Mean-Opinion Scores (MOS), except at high CSNR where the quality saturates. In addition, Bjpntegaard model is used to quantify the trade-offs between CR, GoP-size and CSNR, depending on the intended application. Finally, the performance of objective metrics compared to the obtained MOS is evaluated. Results show that Multi-Scale SSIM (MS-SSIM), SSIM and Video Multimethod Assessment Fusion (VMAF) metrics offer the best correlation with the MOS values.

关键词： SoftCast Linear Video Coding Joint Source-Channel Coding Subjective Quality Assessment visual Artifacts

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Improved PSP-Net Segmentation Network for Automatic Detection of Neovascularization in Color Fundus images

Improved PSP-Net Segmentation Network for Automatic Detectio...

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ieee international conference on visual communications and image processing (vcip)

作者： Liu, Qiuming Wang, Shen Dai, Yulan Zhang, Jintao Wang, Yichen Zhou, Ruoxuan Jiangxi Univ Sci & Tech Sch Software Engn Ganzhou Jiangxi Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China Jiangxi Normal Univ Sch Comp Info Engn Nanchang Jiangxi Peoples R China Nanchang Key Lab Virtual Digital Factory & Cultur Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781665475921

Proliferative Diabetic Retinopathy (PDR) is a serious retinal disease threatening diabetic patients. Intense retinal neovascularization in the retinal image is the most important clinical symptom of PDR, leading to visual distortion if not controlled. Accurate and timely detection of neovascularization from retinal images allows patients to receive adequate treatment to avoid further vision loss. In this work, we propose a retinal neovascularization automatic segmentation model based on improved Pyramid Scene Parsing Network (PSP-Net). To improve the accuracy of the model, we introduce the proposed channel attention module into the model. The network is evaluated with color fundus images from practice. Evaluation results show the network is superior to FCN, SegNet, U-Net and PSP-Net in accuracy and sensitivity. The model could achieve accuracy, sensitivity, specificity, precision and Jaccard similarity score of 0.9832, 0.9265, 0.9897, 0.9116 and 0.8501, respectively. This paper proves through plenty of experimental results that the network model is able to improve the accuracy of segmentation, relieve the workload of doctors, and is worthy of further clinical promotion.

关键词： fundus images image segmentation diabetic retinopath neovascularization deep learning

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An Efficient Content-aware Downsampling-based Video Compression Framework

An Efficient Content-aware Downsampling-based Video Compress...

引用

ieee international conference on visual communications and image processing (vcip)

作者： Jiang, Hao Chen, Li Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China

ISBN: (纸本)9781665475921

Recently, deep learning-based video compression algorithms have achieved competitive performance in Bjontegaard delta (BD) rate, especially those adopting super-resolution networks as post-processing modules in downsampling-based video compression (DBC) frameworks. However, limited by the non-differentiable characteristics of traditional codecs, DBC frameworks mainly focus on improving the performance of super-resolution modules while ignoring optimizing downscaling modules. It is crucial to improve video compression performance without introducing additional modifications to the decoder client in practical application scenarios. We propose a context-aware processing network (CPN) compatible with standard codecs with no computational burden introduced to the client, which preserves the critical information and essential structures during downscaling. The proposed CPN works as a precoder cascaded by standard codecs to improve the compression performance on the server before encoding and transmission. Besides, a surrogate codec is employed to simulate the degradation process of the standard codecs and backpropagate the gradient to optimize the CPN. Experimental results show that the proposed method outperforms latest pre-processing networks and achieves considerable performance compared with the latest DBC frameworks.

关键词： compact representation video precoding deep learning

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A Learning-based Approach for Martian image Compression

A Learning-based Approach for Martian Image Compression

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ieee international conference on visual communications and image processing (vcip)

作者： Ding, Qing Xu, Mai Li, Shengxi Deng, Xin Shen, Qiu Zou, Xin Beihang Univ Beijing Peoples R China Nanjing Univ Nanjing Peoples R China Beijing Inst Spacecraft Syst Engn Beijing Peoples R China

ISBN: (纸本)9781665475921

For the scientific exploration and research on Mars, it is an indispensable step to transmit high-quality Martian images from distant Mars to Earth. image compression is the key technique given the extremely limited Mars-Earth bandwidth. Recently, deep learning has demonstrated remarkable performance in natural image compression, which provides a possibility for efficient Martian image compression. However, deep learning usually requires large training data. In this paper, we establish the first large-scale high-resolution Martian image compression (MIC) dataset. Through analyzing this dataset, we observe an important non-local self-similarity prior for Marian images. Benefiting from this prior, we propose a deep Martian image compression network with the non-local block to explore both local and non-local dependencies among Martian image patches. Experimental results verify the effectiveness of the proposed network in Martian image compression, which outperforms both the deep learning based compression methods and HEVC codec.

关键词： image compression Martian image 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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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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