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检索条件"任意字段=2014 IEEE Visual Communications and Image Processing Conference, VCIP 2014"

共 2408 条记录，以下是91-100 订阅

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SPCNet: A Panoramic image depth estimation method based on spherical convolution

SPCNet: A Panoramic image depth estimation method based on s...

引用

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

作者： He, Si Liu, Yu Wang, Yumei Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China Res Ctr Networks & Commun Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781728185514

As an emerging media format, virtual reality (VR) has attracted the attention of researchers. 6-DoF VR can reconstruct the surrounding environment with the help of the depth information of the scene, so as to provide users with immersive experience. However, due to the lack of depth information in panoramic image, it is still a challenge to convert panorama to 6-DOF VR. In this paper, we propose a new depth estimation method SPCNet based on spherical convolution to solve the problem of depth information restoration of panoramic image. Particularly, spherical convolution is introduced to improve depth estimation accuracy by reducing distortion, which is attributed to Equi-Rectangular Projection (ERP). The experimental results show that many indicators of SPCNet are better than other advanced networks. For example, RMSE is 0.419 lower than UResNet. Moreover, the threshold accuracy of depth estimation has also been improved.

关键词： Depth estimation Spherical convolution Panoramic image Virtual reality

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Nearly Reversible image-to-image Translation Using Joint Inter-Frame Coding and Embedding

Nearly Reversible Image-to-Image Translation Using Joint Int...

引用

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

作者： Cao, Xinzhu Yao, Yuanzhi Yu, Nenghai Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230027 Peoples R China

ISBN: (纸本)9781728185514

image-to-image translation tasks which have been widely investigated with generative adversarial networks (GAN) aim to map an image from the source domain to the target domain. The translated image can be inversely mapped to the reconstructed source image. However, existing GAN-based schemes lack the ability to accomplish reversible translation. To remedy this drawback, a nearly reversible image-to-image translation scheme where the reconstructed source image is approximately distortion-free compared with the corresponding source image is proposed in this paper. The proposed scheme jointly considers inter-frame coding and embedding. Firstly, we organize the GAN-generated reconstructed source image and the source image into a pseudo video. Furthermore, the bitstream obtained by inter-frame coding is reversibly embedded in the translated image for nearly lossless source image reconstruction. Extensive experimental results and analysis demonstrate that the proposed scheme can achieve a high level of performance in image quality and security.

关键词： image-to-image translation generative adversarial network inter-frame coding reversible data embedding

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Analyzing Time Complexity of Practical Learned image Compression Models

Analyzing Time Complexity of Practical Learned Image Compres...

引用

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

作者： Pan, Xiaohan Guo, Zongyu Chen, Zhibo Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei Peoples R China

ISBN: (纸本)9781728185514

We have witnessed the rapid development of learned image compression (LIC). The latest LIC models have outperformed almost all traditional image compression standards in terms of rate-distortion (RD) performance. However, the time complexity of LIC model is still underdiscovered, limiting the practical applications in industry. Even with the acceleration of GPU, LIC models still struggle with long coding time, especially on the decoder side. In this paper, we analyze and test a few prevailing and representative LIC models, and compare their complexity with traditional codecs including H.265/HEVC intra and H.266/VVC intra. We provide a comprehensive analysis on every module in the LIC models, and investigate how bitrate changes affect coding time. We observe that the time complexity bottleneck mainly exists in entropy coding and context modelling. Although this paper pay more attention to experimental statistics, our analysis reveals some insights for further acceleration of LIC model, such as model modification for parallel computing, model pruning and a more parallel context model.

关键词： Time Complexity Analysis image Compression Parallel Computing Neural Networks

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Implementation of Fast Free-Viewpoint Video Rendering on Graphics processing Units

Implementation of Fast Free-Viewpoint Video Rendering on Gra...

引用

ieee visual communications and image processing conference

作者： Attard, Nicholas Debono, Carl J. Univ Malta Dept Commun & Comp Engn Msida Malta

ISBN: (纸本)9781479961399

This paper presents the development of a fast Free-Viewpoint Video (FVV) rendering algorithm that exploits the parallelism offered by General Purpose Graphics processing Units (GPGPUs). The system generates virtual views through the use of Depth image-Based Rendering (DIBR) algorithms, implemented using NVidia r Compute Unified Device Architecture (CUDA). A novel reference image brightness adjustment algorithm that exploits the correspondences between matching pixels in the reference images to avoid drastic brightness switching while navigating in between views is also discussed. The developed solution ensures that data transfers are kept at a minimum, thus improving the overall rendering speed. Objective and subjective test results show that, for typical free-view scenarios, the proposed algorithm can be successfully deployed in real-time FVV systems, providing a good Quality of Experience (QoE).

关键词： Depth image-based rendering free-view video general-purpose graphics processing units

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End-to-end image Compression with Swin-Transformer

End-to-end Image Compression with Swin-Transformer

引用

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

作者： Wang, Meng Zhang, Kai Zhang, Li Li, Yue Li, Junru Wang, Yue Wang, Shiqi City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Bytedance Inc San Diego CA 92122 USA Beijing Bytedance Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9781665475921

In this paper, we propose an end-to-end image compression framework, which cooperates with the swin-transformer modules to capture the localized and non-localized similarities in image compression. In particular, the swin-transformer modules are deployed in the analysis and synthesis stages, interleaving with convolution layers. The transformer layers are expected to perceive more flexible receptive fields, such that the spatially localized and non-localized redundancies could be more effectively eliminated. The proposed method reveals the excellent capability of signal conjunction and prediction, leading to the improvement of the rate and distortion performance. Experimental results show that the proposed method is superior to the existing methods on both natural scene and screen content images, where 22.46% BD-Rate savings are achieved when compared with the BPG. Over 30% BD-Rate gains could be observed with screen content images when compared with the classical hyper-prior end-to-end coding method.

关键词： image compression end-to-end compression transformer convolution

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Foveation-Based image Quality Assessment

Foveation-Based Image Quality Assessment

引用

ieee visual communications and image processing conference

作者： Tsai, Wen-Jiin Liu, Yi-Shih Natl Chiao Tung Univ Dept Comp Sci Hsinchu 30050 Taiwan

ISBN: (纸本)9781479961399

Since human vision has much greater resolutions at the center of our visual field than elsewhere, different criteria of quality assessment should be applied on the image areas with different visual resolutions. This paper proposed a foveation-based image quality assessment method which adopted different sizes of windows in quality assessment for a single image. visual salience models which estimate visual attention regions are used to determine the foveation center and foveation resolution models are used to guide the selection of window sizes for the areas over spatial extent of the image. Finally, the quality scores obtained from different window sizes are pooled together to get a single value for the image. The proposed method has been applied to IQA metrics, SSIM, PSNR, and UQI. The result shows that both Spearman and Kendall correlation coefficients can be improved significantly by our foveation-based method.

关键词： image quality assessment foveation human visual system visual salience model

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Deep Learning-Based Blind image Super-Resolution using Iterative Networks

Deep Learning-Based Blind Image Super-Resolution using Itera...

引用

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

作者： Yaar, Asfand Ates, Hasan F. Gunturk, Bahadir K. Istanbul Medipol Univ Sch Engn & Nat Sci Istanbul Turkey

ISBN: (纸本)9781728185514

Deep learning-based single image super-resolution (SR) consistently shows superior performance compared to the traditional SR methods. However, most of these methods assume that the blur kernel used to generate the low-resolution (LR) image is known and fixed (e.g. bicubic). Since blur kernels involved in real-life scenarios are complex and unknown, performance of these SR methods is greatly reduced for real blurry images. Reconstruction of high-resolution (HR) images from randomly blurred and noisy LR images remains a challenging task. Typical blind SR approaches involve two sequential stages: i) kernel estimation;ii) SR image reconstruction based on estimated kernel. However, due to the ill-posed nature of this problem, an iterative refinement could be beneficial for both kernel and SR image estimate. With this observation, in this paper, we propose an image SR method based on deep learning with iterative kernel estimation and image reconstruction. Simulation results show that the proposed method outperforms state-of-the-art in blind image SR and produces visually superior results as well.

关键词： Super-resolution Kernel estimation Blind Iterative Deep learning

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Quality of Experience Evaluation for Streaming Video Using CGNN

Quality of Experience Evaluation for Streaming Video Using C...

引用

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

作者： Zhou, Zhiming Dong, Yu Song, Li Xie, Rong Li, Lin Zhou, Bing Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China MIGU Co Ltd Beijing Peoples R China

ISBN: (纸本)9781728180687

One of the principal contradictions these days in the field of v ideo is l ying b etween t he b ooming d emand for evaluating the streaming video quality and the low precision of the Quality of Experience prediction results. In this paper, we propose Convolutional Neural Network and Gate Recurrent Unit (CGNN)-QoE, a deep learning QoE model, that can predict overall and continuous scores of video streaming services accurately in real time. We further implement state-of-the-art models on the basis of their works and compare with our method on six public available datasets. In all considered scenarios, the CGNN-QoE outperforms existing methods.

关键词： Quality of Experience (QoE) adaptive video streaming Gate Recurrent Unit

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EXTENDING CCSDS 123.0-B-1 FOR LOSSLESS 4D image COMPRESSION

EXTENDING CCSDS 123.0-B-1 FOR LOSSLESS 4D IMAGE COMPRESSION

引用

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

作者： Zhang, Panpan Wang, Xiuheng Gao, Tiande Feng, Zhenfu Chen, Jie Northwestern Polytech Univ Ctr Intelligent Acoust & Immers Commun Sch Marine Sci & Technol Xian Shaanxi Peoples R China Xian Univ Posts & Telecommun Sch Elect Engn Xian Shaanxi Peoples R China

ISBN: (纸本)9781728180687

A 4-dimensional (4D) image can be viewed as a stack of volumetric images over channels of observation depth or temporal frames. This data contains rich information at the cost of high demands for storage and transmission resources due to its large volume. In this paper, we present a lossless 4D image compression algorithm by extending CCSDS-123.0-B-1 standard. Instead of separately compressing the volumetric image at each channel of 4D images, the proposed algorithm efficiently exploits redundancy across the fourth dimension of data. Experiments conducted on two types of 4D images demonstrate the effectiveness of the proposed lossless compression method.

关键词： 4D images lossless compression CCSDS-123.0-B-1 channel redundancy

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Entropy-based Deep Product Quantization for visual Search and Deep Feature Compression

Entropy-based Deep Product Quantization for Visual Search an...

引用

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

作者： Niu, Benben Wei, Ziwei He, Yun Tsinghua Univ Dept Elect Engn Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781728185514

With the emergence of various machine-to-machine and machine-to-human tasks with deep learning, the amount of deep feature data is increasing. Deep product quantization is widely applied in deep feature retrieval tasks and has achieved good accuracy. However, it does not focus on the compression target primarily, and its output is a fixed-length quantization index, which is not suitable for subsequent compression. In this paper, we propose an entropy-based deep product quantization algorithm for deep feature compression. Firstly, it introduces entropy into hard and soft quantization strategies, which can adapt to the codebook optimization and codeword determination operations in the training and testing processes respectively. Secondly, the loss functions related to entropy are designed to adjust the distribution of quantization index, so that it can accommodate to the subsequent entropy coding module. Experimental results carried on retrieval tasks show that the proposed method can be generally combined with deep product quantization and its extended schemes, and can achieve a better compression performance under near lossless condition.

关键词： deep feature deep product quantization entropy image retrieval semi-supervised

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