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检索条件"任意字段=2017 IEEE Visual Communications and Image Processing, VCIP 2017"

共 113 条记录，以下是41-50 订阅

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Rate Control Model for High Dynamic Range Video

Rate Control Model for High Dynamic Range Video

引用

ieee visual communications and image processing (vcip)

作者： Bai, Lixun Song, Li Xie, Rong Zhang, Liang Luo, Zhengyi Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Cooperat Medianet Innovat Ctr Shanghai Peoples R China Shanghai Univ Elect Power Sch Elect & Informat Engn Shanghai Peoples R China

ISBN: (纸本)9781538604625

This paper describes a luminance based rate control (RC) model for high dynamic range (HDR) video. A novel mathematical relationship between luminance and bit allocation of a coding tree unit (CTU) is presented. By adjusting the existing RC algorithm through the proposed model, a better balance between dark and bright areas can be achieved and -4.4% gains can be obtained in terms of average BD-Rate (tPSNR-XZY). Moreover, subjective assessment also shows that, compared with the existing RC model, the proposed method can convey a wider range of perceptible shadow and highlight more details.

关键词： High efficiency video coding (HEVC) high dynamic range (HDR) standard dynamic range (SDR) rate control luminance

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Supervised Hashing with Extreme Learning Machine

Supervised Hashing with Extreme Learning Machine

引用

ieee visual communications and image processing (vcip)

作者： Yuan, Tongtong Deng, Weihong Hu, Jiani Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China

ISBN: (纸本)9781538604625

Supervised hashing methods, which aim to generate semantic similarity-preserving binary codes, have been proposed to improve the performance of large-scale image retrieval. However, learning binary codes remains an NP-hard problem due to the binary constraints and complex computation. Existing hashing methods have never explored the potentiality of the label information, leading to a limited performance. To address these problems, we propose a simple supervised hashing method based on extreme learning machine (ELM). And we generate the supervised information in ELM by target code learning instead of using the traditional label code to fit the retrieval problem. With this modified label code, our method can produce high-quality binary codes and obtain high retrieval precision. Comprehensive experiments have shown our superiority to other state-of-the-art methods.

关键词： image retrieval Supervised hashing Extreme Learning Machine Target code learning Hadamard code

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Saliency Change Based Reduced Reference image Quality Assessment

Saliency Change Based Reduced Reference Image Quality Assess...

引用

ieee visual communications and image processing (vcip)

作者： Wu, Jinjian Liu, Yongxu Shi, Guangming Lin, Weisi Xidian Univ Sch Elect Engn Xian Shaanxi Peoples R China Nanyang Technol Univ Sch Comp Engn Singapore Singapore

ISBN: (纸本)9781538604625

The image quality assessment (IQA) technique, which aims to perform coherently with subjective perception, is useful in quality-orientated image processing systems. In this paper, we suggest to take the saliency change into account for reduced reference (RR) IQA model. Generally, a saliency region will attract more attention, and our human vision is more sensitive to quality degradation on such region. Inspired by this, saliency values are firstly used to highlight these sensitive regions, and a local saliency weighted histogram (LSWH) based on visual orientation pattern is generated for visual feature extraction. Next, strong distortion may change the saliency from the reference to the distorted images. Thus, the saliency of each visual orientation pattern is measured, and a global saliency based histogram (GSBH) is created. Finally, by combining the LSWH and GSBH, a novel IQA model for reduced reference is introduced. Experimental results on five publicly available databases demonstrate that the proposed model uses only several values (9 values) as reference information, and performs consistently with subjective perception.

关键词： Reduced-Reference (RR) image Quality Assessment (IQA) visual Saliency visual Content Extraction

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Gram Matrix Based Representation for image Retrieval

Gram Matrix Based Representation for Image Retrieval

引用

ieee visual communications and image processing (vcip)

作者： Zhao, Shanwei Zhao, Zhicheng Su, Fei Beijing Univ Posts & Telecommun Sch Commun & Informat Engn Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China

ISBN: (纸本)9781538604625

In the field of image retrieval, most of image representations based on convolutional neural network (CNN) are first-order forms, i.e., the pooling or encoding methods are adopted on feature maps directly to produce compact image representations, while the high-order representations, such as the dependencies between different channels in the same layer are often neglected. In this paper, a novel image representation and retrieval algorithm based on Gram matrix is proposed. Specifically, based on Gram matrix of convolutional layers, second-order features are firstly constructed by considering the relationships between different channels of feature maps. Afterwards, two weighted schemes, that is, equal channel weighting and sparsity-sensitive channel weighting are presented respectively to aggregate them into the final representation. The extensive experiments on four public image datasets are conducted, and the promising results demonstrate the effectiveness of the proposed algorithm.

关键词： image retrieval Gram matrix Convolutional Neural Networks aggregate representation deep learning

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A Novel image Compression Framework at Edges

A Novel Image Compression Framework at Edges

引用

ieee visual communications and image processing (vcip)

作者： Ye, Long Liu, Qianhan Zhong, Wei Zhang, Qin Commun Univ China Key Lab Media Audio & Video Minist Educ Beijing Peoples R China

ISBN: (纸本)9781538604625

For the new developed area of edge computing, the traditional image coding schemes always have poor adaptability on the requirements of high-compression and low-delay coding, due to the fact that they do not consider the correlations between encoding image and external images. Aiming at this problem, we propose a novel image coding framework based on the content similarity analysis. Unlike the current state-of-the-art systems, our method compresses all the images stored in the edge computing terminal as a whole. The images are represented by their pHash values and classified into many groups, then each group is compressed in a quasi inter-prediction coding way. With our coding scheme, the redundancies among the similar images could be removed to achieve more coding gain. Experimental results demonstrate the superiorities of our method in the edge computing environment compared with current static image coding solutions.

关键词： image Compression Edge Computing image Classification Quasi Inter-Prediction Coding pHash

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Wireless image and Video Soft Transmission via Perception-Inspired Power Distortion Optimization

Wireless Image and Video Soft Transmission via Perception-In...

引用

ieee visual communications and image processing (vcip)

作者： Zhao, Jing Xiong, Ruiqin Luo, Chong Wu, Feng Gao, Wen Peking Univ Inst Digital Media Beijing 100871 Peoples R China Microsoft Res Asia Beijing 100080 Peoples R China Univ Sci & Technol China Hefei 230026 Anhui Peoples R China

ISBN: (纸本)9781538604625

Recently, a scheme called SoftCast has shown great potential for wireless image/video communication in the scenarios where the channel quality may fluctuate drastically and unpredictably. The transmission is lossy in nature, with its transmission power allocated among coefficients unequally to minimize the distortion. One problem is that its performance is optimized using mean square errors (MSE) as the quality metric, which is known for not matching the perception of human eyes. Inspired by the researches in image quality assessment, this paper proposes a power allocation and optimization scheme that minimizes the perceptual distortion of reconstruction image. In particular, we establish a perception model to evaluate the perceptual importance of different transform coefficients, based on the structure similarity (SSIM) image quality metric. Experimental results show that the proposed scheme can improve the perceptual performance of the original SoftCast scheme.

关键词： Wireless visual communication SoftCast perception model power allocation optimization

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Modeling Peripheral Vision Impact on Perceptual Quality of Immersive images

Modeling Peripheral Vision Impact on Perceptual Quality of I...

引用

ieee visual communications and image processing (vcip)

作者： Guo, Peiyao Shen, Qiu Huang, Mingkai Zhou, Rongbing Cao, Xun Ma, Zhan Nanjing Univ Sch Elect Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538604625

Conventional images/videos are often rendered within the central area of human visual system (HVS) with uniform quality. Recent virtual reality (VR) device with head mounted display (HMD) extends the field of view (FoV) significantly to include both central and peripheral areas. It exhibits the unequal acuity of the quality sensation because of the non-uniform distribution of photoreceptors in our retina. Hence, we propose to study the impact of image qualities (with respect to the quantization stepsize q or spatial resolution s) in peripheral vision and conclude self-adaptive analytical models that have shown quite impressive accuracy through independent cross validations. These models can further be applied to assign different quality weights at different regions, so as to significantly reduce the transmission data size but without subjective quality loss.

关键词： Peripheral vision image quality quantization stepsize spatial resolution

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Internal Generative Mechanism Inspired Reduced Reference image Quality Assessment with Entropy of Primitive

Internal Generative Mechanism Inspired Reduced Reference Ima...

引用

ieee visual communications and image processing (vcip)

作者： Wang, Shanshe Wang, Shiqi Gu, Ke Guo, Xiaoqiang Ma, Siwei Gao, Wen Peking Univ Inst Digital Media Beijing Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Beijing Univ Technol Beijing Peoples R China Acad Broadcasting Sci SAPPRFT Beijing Peoples R China

ISBN: (纸本)9781538604625

In this paper, we propose a novel reduced-reference (RR) image quality assessment (IQA) algorithm based on the internal generative mechanism, which suggests that the human visual system (HVS) can actively predict the primary visual information and avoid the uncertainty. Specifically, the explanation of the visual scene is formulated as the process of sparse representation. In particular, the entropy of primitive accounts for the primary visual information and the discrepancy between the image signal and its best sparse description is regarded as the uncertainty in perception. As such, the combined feature that can summarize the primary visual information and uncertainty in sparse domain is required to be transmitted in the RR-IQA framework. Comparative studies of the proposed reduced reference metric is conduced on both single and multiple distortion databases, and experimental results demonstrate that the proposed metric can achieve high correlation with the human perception by only sending ignorable additional information.

关键词： Reduced-reference image quality assessment sparse representation internal generative mechanism entropy-of-primitive

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Real-Time Deep image Super-Resolution via Global Context Aggregation and Local Queue Jumping

Real-Time Deep Image Super-Resolution via Global Context Agg...

引用

ieee visual communications and image processing (vcip)

作者： Hu, Yueyu Liu, Jiaying Yang, Wenhan Deng, Shihong Zhang, Luyao Guo, Zongming Peking Univ Inst Comp Sci & Technol Beijing 100871 Peoples R China

ISBN: (纸本)9781538604625

Deep learning-based image super-resolution has provided very impressive reconstruction quality. However, their running time still sets barriers for real-time applications. In this paper, we propose a Global context aggregation and Local queue jumping Network (GLNet) which provides the more effective image SR given a certain number of model parameters. In our GLNet, we reconsider the model design of the real-time image SR paradigm. Then, we construct a deep network with fewer channels but a deeper structure to effectively aggregate the global context. The dilated convolutions are used as parts of basic units of our GLNet, which further enlarges the receptive field. Besides, an additional local queue jumping path is employed to connect the first-layer feature map and the last-layer feature map to better model the local signal structure. Extensive experiments demonstrate the superiority of our GLNet which offers new state-of-the-art performance considering both reconstruction quality and time consumption.

关键词： Real-Time image Super-Resolution Global Context Aggregation Local Queue Jumping Deep Learning

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CNN-Based Text image Super-Resolution Tailored for OCR

CNN-Based Text Image Super-Resolution Tailored for OCR

引用

ieee visual communications and image processing (vcip)

作者： Zhang, Haochen Liu, Dong Xiong, Zhiwei Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei 230027 Anhui Peoples R China

ISBN: (纸本)9781538604625

Since low-resolution images may hamper the performance of optical character recognition (OCR), text image super-resolution (SR) has become an increasingly important problem in computer vision. Convolutional neural network (CNN) has been proposed for generic image SR as well as text image SR, but the previous works concern more on the objective quality (e.g. PSNR) rather than the OCR performance. In this paper, we propose a new loss function when training CNN for text image SR to facilitate OCR, and conduct model combination to further improve the performance. Also, we propose a simple yet effective image padding method to refine the image boundaries during SR. Experimental results show that we achieve an OCR accuracy of 78.10% on the ICDAR 2015 TextSR dataset, which is comparable with that of using the original high-resolution images (78.80%), and also exceeds the state-of-the-arts.

关键词： Convolutional neural network Loss function Model combination Optical character recognition Super-resolution

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