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Bit-Plane Specific Selective Histogram Equalization for image Enhancement and Representation 2nd

Bit-Plane Specific Selective Histogram Equalization for Imag...

引用

2nd International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2018

作者： Arvind Ratan, Ram Hansraj College University of Delhi Delhi India Scientific Analysis Group Defence Research and Development Organization Delhi India

ISBN: (纸本)9789811391804

images acquired in poor acquisition conditions which are unavoidable and cannot be ignored in many visual applications require enhancement to achieve high contrast for better representation and interpretation of image objects. For efficient image storage and communications, compression and preserving of image details is required apart from contrast enhancement. A popular histogram equalization approach leaves undesirable artefacts in enhanced images and increases its data size due to excessive contrast enhancement. In this paper, we present a bit-plane specific selective histogram equalization technique which narrows the range of enhanced image than original image and keeps image details intact without unnoticeable artefacts with reduced image data size. The qualitative and quantitative measures as visual perception, histogram, edge details, entropy, data storage size, mean intensity and mean square error obtained for different images demonstrate the effectiveness of the selective bit-plane specific histogram equalization over to other methods of histogram equalization. © 2019, Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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Deep quad path quality evaluator combined aesthetic estimation for Multi-exposure image fusion 34

Deep quad path quality evaluator combined aesthetic estimati...

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34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： Guan, Ruoxuan Lu, Wen He, Weiquan Bai, Furui Xidian University School of Electronic Engineering Xian710071 China

ISBN: (纸本)9781728137230

There are limitations in existing multi-exposure fused image quality assessment (MEF-IQA) approaches. The first is that the existing approaches are hard to estimate the consistency of luminance distribution in fused images, which can be easily destroyed in fusion process. The second is that the aesthetics estimation of fused images are neglected in existing approaches, which should be considered for the images produced by fusion algorithms to give people an aesthetic experience. In order to solve these problems, a MEF-IQA approach based on the deep network was proposed. First, we adapted a quad path network as luminance error quality evaluator (LE) to extract the features, which can capture the luminance distribution patterns, from fused images. Then, we trained an aesthetic evaluator (AE) to obtain the feature which can describe the aesthetic attributes of fused image. Finally, the two extracted features are combined for predicting the perceptual quality scores. Experimental result clearly shows that the proposed approach can quantify the image quality reliably, and it is highly correlated with subjective quality scores. © 2019 IEEE.

关键词： image fusion

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Deep Blind Video Quality Assessment for User Generated Videos

Deep Blind Video Quality Assessment for User Generated Video...

引用

IEEE visual communications and image processing (VCIP)

作者： Jiapeng Tang Yu Dong Rong Xie Xiao Gu Li Song Lin Li Bing Zhou Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China MIGU Co. Ltd Beijing China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

As short video industry grows up, quality assessment of user generated videos has become a hot issue. Existing no reference video quality assessment methods are not suitable for this type of application scenario since they are aimed at synthetic videos. In this paper, we propose a novel deep blind quality assessment model for user generated videos according to content variety and temporal memory effect. Content-aware features of frames are extracted through deep neural network, and a patch-based method is adopted to obtain frame quality score. Moreover, we propose a temporal memory-based pooling model considering temporal memory effect to predict video quality. Experimental results conducted on KoNViD-1k and LIVE-VQC databases demonstrate that the performance of our proposed method outperforms other state-of-the-art ones, and the comparative analysis proves the efficiency o f our temporal pooling model.

关键词： Feature extraction Quality assessment Video recording Streaming media Databases Adaptation models Kernel

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A point cloud compression framework via spherical projection

A point cloud compression framework via spherical projection

引用

IEEE visual communications and image processing (VCIP)

作者： Yingshen He Ge Li Yiting Shao Jing Wang Yueru Chen Shan Liu Peking University Shenzhen Graduate School Shenzhen China Peng Cheng Lboratory Shenzhen China Media Lab Tencent America Palo Alto USA

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

In this paper, we propose a sphere-projection-based framework for point cloud geometry and attribute lossless and lossy coding. The original point cloud is adaptively divided into blocks, and then we create a fitting sphere in each block for modeling the local geometry structure of the point cloud. Sphere coordination transform and spherical projection scheme are introduced to transfer a 3D point cloud to a set of the range images. A novel compact representation of generated range images based on Morton codes is proposed to separate the range images into occupancy images and attributes vectors for further compression. Experimental results demonstrate that for the LiDAR point clouds datasets in lossless compression, the proposed method offers better performance than geometry-based point cloud compression (G-PCC). For the object point clouds datasets in lossy compression, the proposed method has better rate-distortion (R-D) performance than Draco.

关键词： Three-dimensional displays image coding Geometry Laser radar Tools Transform coding Codecs

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A Theory of Occlusion for Improving Rendering Quality of Views

A Theory of Occlusion for Improving Rendering Quality of Vie...

引用

IEEE visual communications and image processing (VCIP)

作者： Yijun Zeng Weiyan Chen Mengqin Bai Yangdong Zeng Changjian Zhu School of Electron. Engineering Guangxi Normal Univ. Guilin China Guilin Medical University Guilin Guilin China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Occlusion lack compensation (OLC) is a multiplexing gain optimization data acquisition and novel views rendering strategy for light field rendering (LFR). While the achieved OLC is much higher than previously thought possible, the improvement comes at the cost of requiring more scene information. This can capture more detailed scene information, including geometric information, texture information and depth information, by learning and training methods. In this paper, we develop an occlusion compensation (OCC) model based on restricted boltzmann machine (RBM) to compensate for lack scene information caused by occlusion. We show that occlusion will cause the lack of captured scene information, which will lead to the decline of view rendering quality. The OCC model can estimate and compensate the lack information of occlusion edge by learning. We present experimental results to demonstrate the performance of OCC model with analog training, verify our theoretical analysis, and extend our conclusions on optimal rendering quality of light field.

关键词： Cameras Rendering (computer graphics) Bandwidth Mathematical model Two dimensional displays Three-dimensional displays Signal processing

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Prediction-Aware Quality Enhancement of VVC Using CNN

Prediction-Aware Quality Enhancement of VVC Using CNN

引用

IEEE visual communications and image processing (VCIP)

作者： Fatemeh Nasiri Wassim Hamidouche Luce Morin Nicolas Dhollande Gildas Cocherel IRT b<>com France CNRS IETR - UMR 6164 Univ Rennes INSA Rennes Rennes France AVIWEST France

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

The upcoming video coding standard, Versatile Video Coding (VVC), has shown great improvement compared to its predecessor, High Efficiency Video Coding (HEVC), in terms of bitrate saving. Despite its substantial performance, compressed videos might still suffer from quality degradation at low bitrates due to coding artifacts such as blockiness, blurriness and ringing. In this work, we exploit Convolutional Neural Networks (CNN) to enhance quality of VVC coded frames after decoding in order to reduce low bitrate artifacts. The main contribution of this work is the use of coding information from the compressed bitstream. More precisely, the prediction information of intra frames is used for training the network in addition to the reconstruction information. The proposed method is applied on both luminance and chrominance components of intra coded frames of VVC. Experiments on VVC Test Model (VTM) show that, both in low and high bitrates, the use of coding information can improve the BD-rate performance by about 1% and 6% for luma and chroma components, respectively.

关键词： Encoding Training Convolutional codes image reconstruction Distortion Streaming media Decoding

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Identifying and pruning redundant structures for deep neural networks 34

Identifying and pruning redundant structures for deep neural...

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34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： Gan, Wenyao Song, Li Chen, Li Xie, Rong Gu, Xiao Shanghai Jiao Tong University Institute of Image Communication and Network Engineering China Shanghai Institute for Advanced Communication and Data Science Shanghai200240 China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781728137230

Deep convolutional neural networks have achieved considerable success in the field of computer vision. However, it is difficult to deploy state-of-The-Art models on resource-constrained platforms due to their high storage, memory bandwidth, and computational costs. In this paper, we propose a structured pruning method which employs a three-step process to reduce the resource consumption of neural networks. First, we train an initial network on the training set and evaluate it on the validation set. Next, we introduce an iterative pruning and fine-Tuning algorithm to identify and prune redundant structures, which results in a pruned network with a compact architecture. Finally, we train the pruned network from scratch on both the training set and validation set to obtain the final accuracy on the test set. In the experiments, our pruning method significantly reduces the model size (by 87.2% on CIFAR-10), saves inference time (53.3% on CIFAR-10), and achieves better performance as compared to recent state-of-The-Art methods. © 2019 IEEE.

关键词： Deep neural networks

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Learning Convolution Feature Aggregation via Edge Attention Convolution Network for Person Re-Identification

Learning Convolution Feature Aggregation via Edge Attention ...

引用

IEEE visual communications and image processing (VCIP)

作者： Chaoqun Lin Ruopei Guo Mingkun Li Xianbiao Qi Chun-Guang Li School of Artificial Intelligence Beijing University of Posts and Telecommunications Ping An Group Inc.

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Person Re-Identification (Re-ID) is a challenging task of matching pedestrian images collected from nonoverlapping multiple camera views due to huge variations from pose changes, occlusions, varying illumination and clutter background. Recently, graph convolution network or graph neural network increasingly gains a lot of research attention in person Re-ID. However, the existing methods have not fully exploit the available features on the graph. In this paper, we propose an efficient and effective end-to-end trainable framework, termed Edge Attention Convolution Network (EACN), to perform convolution feature learning and attentive feature aggregation for person Re-ID, in which the learned convolution features on vertex and its edges are attentively aggregated on a dynamic graph. We conduct extensive experiments on two large benchmark datasets, Market-1501 and DukeMTMC. Experimental results validate the efficiency and effectiveness of our proposal.

关键词： Convolution image edge detection Feature extraction Measurement Entropy Smoothing methods Training

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Mono is Enough: Instance Segmentation from Single Annotated Sample

Mono is Enough: Instance Segmentation from Single Annotated ...

引用

IEEE visual communications and image processing (VCIP)

作者： Longrong Yang Hongliang Li Qingbo Wu Fanman Meng King Ngi Ngan University of Electronic Science and Technology of China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

With the help of various Deep Neural Networks, instance segmentation has achieved significant progress. How-ever, these successes are heavily reliant on large-scale manually annotated samples, which are extremely time-consuming and expensive. To address this issue, we propose a highly efficient anisotropic data augmentation method, which generates high quality training data from a single manually annotated sample. Instead of equivalently modifying foreground and background like traditional data augmentation methods, we focus on enriching the diversities of foreground appearance and positional relation between foreground and background, which are beneficial for the classification and localization sub-tasks respectively. All foreground instances of the source annotated sample undergo various rotation, brightness change, rescale, distortion and frequency-component mixup (FCM). Then, these modified instances are randomly embedded into background, which serve as new training samples. Experiments on Cityscapes dataset show that our method significantly outperforms traditional data augmentation methods.

关键词： image segmentation Distortion Brightness Data models Annotations Training data Task analysis

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Comparison of Deep Learning, Data Augmentation and Bag of-visual-Words for Classification of Imbalanced image Datasets 2nd

Comparison of Deep Learning, Data Augmentation and Bag of-Vi...

引用

2nd International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2018

作者： Saini, Manisha Susan, Seba Delhi Technological University Delhi India

ISBN: (纸本)9789811391804

image classification is a supervised machine learning task to classify images into different categories. As most real-world datasets are imbalanced in nature, instances are not equally distributed in all classes which often results in biased classification. So considering this objective, we are dealing with an imbalanced dataset. We have created a small size of an imbalanced dataset consisting of Ant and Plane images from Caltech-101 dataset. This paper illustrates the comparison between Deep learning, Data Augmentation and the Bag-of-visual Words (BOVW) for classification of imbalanced image datasets. According to the experimental results, it was found that deep learning results in higher accuracy in comparison to bag-of-visual-words (BOVW) and data augmentation. © 2019, Springer Nature Singapore Pte Ltd.

关键词： image classification

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