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Blind panoramic image quality assessment via the asymmetric mechanism of human brain 34

Blind panoramic image quality assessment via the asymmetric ...

引用

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

作者： Xia, Yumeng Wang, Yongfang Peng, Ye Shanghai University School of Communication and Information Engineering Shanghai200444 China

ISBN: (纸本)9781728137230

Most existing objective quality evaluation metrics for panoramic images are typically derived from peak signal to noise ratio (PSNR) or structural similarity (SSIM). However, they need pristine panoramic images and are not highly consistent with human perception. To address the problem, we propose a novel blind panoramic image quality assessment (PIQA) method to predict the visual quality of panoramic images based on asymmetric mechanism of human brain. In the proposed method, the high-frequency feature is extracted by panoramic-weighted local binary pattern and relative total variation is used to clip the high-frequency information in panoramic images, then panoramic-weighted statistic feature can represent the lowfrequency feature. Finally, support vector regression (SVR) is adopted to build a quality predictor from feature space to quality score space. The experimental results on the public subjective dataset demonstrate the superiority of our proposed metric compared with state-of-The-Art objective PIQA methods. © 2019 IEEE.

关键词： Local binary pattern

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Attention-Based image Captioning Using DenseNet Features 1

引用

26th International conference on Neural Information processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS)

作者： Hossain, Md Zakir Sohel, Ferdous Shiratuddin, Mohd Fairuz Laga, Hamid Bennamoun, Mohammed Murdoch Univ Perth WA 6155 Australia Univ Western Australia Perth WA 6009 Australia

ISBN: (数字)9783030368029

ISBN: (纸本)9783030368029;9783030368012

We present an attention-based image captioning method using DenseNet features. Conventional image captioning methods depend on visual information of the whole scene to generate image captions. Such a mechanism often fails to get the information of salient objects and cannot generate semantically correct captions. We consider an attention mechanism that can focus on relevant parts of the image to generate fine-grained description of that image. We use image features from DenseNet. We conduct our experiments on the MSCOCO dataset. Our proposed method achieved 53.6, 39.8, and 29.5 on BLEU-2, 3, and 4 metrics, respectively, which are superior to the state-of-the-art methods.

关键词： image captioning DenseNet Attention

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Learned image and video compression with deep neural networks

Learned image and video compression with deep neural network...

引用

IEEE visual communications and image processing (VCIP)

作者： Dong Xu Guo Lu Ren Yang Radu Timofte School of Electrical and Information Engineering The University of Sydney Australia

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

This tutorial aims at reviewing the recent progress in the deep learning based data compression, including image compression and video compression. In the past years, deep learning techniques have been successfully applied to a large number of computer vision and image processing tasks. However, for the data compression task, the traditional approaches (i.e., block based motion estimation and motion compensation, etc.) are still widely employed in the mainstream codecs. Considering the powerful representation capability, it is possible to improve the data compression performance by employing the advanced deep learning technologies. To this end, deep leaning based compression approaches have recently received significant attention from both academia and industry in the field of computer vision and image/video compression. In this tutorial, we will introduce the related deep learning techniques for image compression and video compression. Specifically, in this tutorial, we will first introduce the basic pipeline for the traditional codecs, such as JPEG, H.264 and HEVC. Then, we will discuss the common network architectures for visual data compression and analyse different learning based entropy models. Based on these techniques, we will describe several widely used end-to-end optimized frameworks for visual data compression. In summary, our tutorial will cover both the traditional data coding techniques and the popular learning based visual data compression algorithms, which will help the audiences with different backgrounds learn the recent progresses in this emerging research area.

关键词： image coding Data compression Computer vision Video compression Deep learning Tutorials conferences

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Benign and Malignant Dermatoscopy image Classification

Benign and Malignant Dermatoscopy Image Classification

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International conference on Advances in Electronics, Computers and communications (ICAECC)

作者： T. Christy Bobby Ramaiah University of Applied Sciences - Bangalore Bangalore India

ISBN: (数字)9781728191836

ISBN: (纸本)9781728180458

Skin cancer is one of the life threatening diseases and there is mostly no chance of remission from skin cancer if diagnosed in the last stage. The three major types of skin cancers are basal cell carcinoma, squamous cell carcinoma, and melanoma. Among these three, melanoma skin cancer is dangerous and acute in nature. Dermatologists use various techniques for diagnosing the malignant, in which the popular and reliable clinical method is dermatoscopy. The manual inference of the disease condition from the dermatoscopy images requires intensive knowledge and experience in the related field. Also, there will be an unavoidable degree of variability in image analysis occurs as long as the diagnostics procedure relies on human visual perception. Thus recently, image processing and machine learning algorithms have been applied for the accurate diagnoses of skin cancers from the dermatoscopic images. Thus, the goal of the proposed work is to automatically segment and classify the dermatoscopy skin lesion image with the help of image processing and machine learning algorithms. The proposed approach classifies the skin lesion image as benign or malignant melanoma with 90% accuracy, 91% sensitivity, 86% specificity, and 93% precision.

关键词： image segmentation Machine learning algorithms Sensitivity Melanoma Skin Lesions visual perception

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Lightweight Color image Demosaicking with Multi-Core Feature Extraction

Lightweight Color Image Demosaicking with Multi-Core Feature...

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IEEE visual communications and image processing (VCIP)

作者： Yufei Tan Kan Chang Hengxin Li Zhenhua Tang Tuanfa Qin School of Computer and Electronic Information Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Convolutional neural network (CNN)-based color image demosaicking methods have achieved great success recently. However, in many applications where the computation resource is highly limited, it is not practical to deploy large-scale networks. This paper proposes a lightweight CNN for color image demosaicking. Firstly, to effectively extract shallow features, a multi-core feature extraction module, which takes the Bayer sampling positions into consideration, is proposed. Secondly, by taking advantage of inter-channel correlation, an attention-aware fusion module is presented to efficiently reconstruct the full color image. Moreover, a feature enhancement module, which contains several cascading attention-aware enhancement blocks, is designed to further refine the initial reconstructed image. To demonstrate the effectiveness of the proposed network, several state-of-the-art demosaicking methods are compared. Experimental results show that with the smallest number of parameters, the proposed network outperforms the other compared methods in terms of both objective and subjective qualities.

关键词： Feature extraction image reconstruction Convolution Iron image color analysis Color Training

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Towards Quantized DCT Coefficients Restoration for Compressed images

Towards Quantized DCT Coefficients Restoration for Compresse...

引用

IEEE visual communications and image processing (VCIP)

作者： Tong Ouyang Zhenzhong Chen Shan Liu School of Remote Sensing and Information Engineering Wuhan University Wuhan China Tencent Media Lab Palo Alto CA USA

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

images and videos suffer from undesirable visual artifacts at high compression ratios, which is due to the use of the discrete cosine transform and scalar quantization in the compression. To restore the quantized coefficients via producing the quantization error, we propose a coefficients restoration convolutional neural network in the frequency domain (FD-CRNet). Taking advantage of residual learning, the proposed FD-CRNet efficiently exploits the related distribution of different frequency components. The squeeze-and-excitation block (SE block) is adopted to reduce the computational complexity and better restoration performance. Experimental results show the quantized coefficients are recovered near the lossless coefficients effectively, which outperforms the existed coefficients restoration methods. In addition, the performance of methods in the spatial domain is significantly improved by FD-CRNet with more authentic details and sharper edges when removing the compression artifacts.

关键词： Q-factor image coding Quantization (signal) Frequency-domain analysis image restoration Discrete cosine transforms image reconstruction

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Dual-Streams Global Guided Learning for High Dynamic Range image Reconstruction 34

Dual-Streams Global Guided Learning for High Dynamic Range I...

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

作者： Lian, Junjie Wang, Yongfang Wang, Chuang Shanghai University School of Communication and Information Engineering Shanghai200444 China

ISBN: (纸本)9781728137230

High dynamic range (HDR) images capture the luminance information of the real world and have more detailed information than low dynamic range (LDR) images. In this paper, we propose a dual-streams global guided end-To-end learning method to reconstruct HDR image from a single LDR input that combines both global information and local image features. In our framework, global features and local features are separately learned in dual-streams branches. In the reconstructed phase, we use a fusion layer to fuse them so that the global features can guide the local features to better reconstruct the HDR image. Furthermore, we design mixed loss function including multi-scale pixel-wise loss, color similarity loss and gradient loss to jointly train our network. Comparative experiments are carried out with other state-of-The-Art methods and our method achieves superior performance. © 2019 IEEE.

关键词： image reconstruction

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Introducing Latent Space Correlation to Conditional Autoencoders for Intra Prediction

Introducing Latent Space Correlation to Conditional Autoenco...

引用

IEEE visual communications and image processing (VCIP)

作者： Fabian Brand Jürgen Seiler André Kaup Multimedia Communications and Signal Processing Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Intra prediction has been an integral part of image and video coders for a long time. A predominant method is angular prediction that extends the reference area in a certain angle into the block. Recently many deep-learning-based methods have been proposed. Since intra prediction uses multiple modes this usually requires training a large number of networks. With a conditional autoencoder we are able to generate an arbitrary number of modes with only one network. In this paper we introduce a novel loss function enforcing a spatially correlated latent space and extend the network structure to the same end. Thereby we are able to propose a simple spatial mode prediction scheme using most-probable-mode lists. By replacing matrix-based intra prediction in VVC with our method, we obtain average rate savings of 0.84% with peak gains of 2.37%.

关键词： Training Correlation Encoding Neural networks Rate-distortion Quantization (signal) Optimization

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No-Reference Stereoscopic image Quality Assessment Based on Shuffle-Convolutional Neural Network 34

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

引用

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

作者： Li, Sumei Wang, Mingyi Hou, Chunping Tianjin University School of Electrical and Information Engineering Tianjin China

ISBN: (纸本)9781728137230

With the development of stereoscopic imaging technology, stereoscopic image quality assessment (SIQA) has been gaining great attention. In this work, to find a better SIQA method conforming to the perceptual characteristics of our brain, we propose a two-channel convolutional neural network (CNN) based on shuffle unit, which is called SCNN, for no-reference SIQA. The shuffle unit is used to mix up the features extracted from the left and right views to complete information communication between the two views. Different from other SIQA methods, the four shuffle units among proposed model achieve the multiple binocular fusions while processing the left and right views. Moreover, the Shuffle v2 block before the global pooling layer further improves the accuracy of SCNN. In addition, it is worth noting that we employ decorrelated batch normalization (DBN) to obtain the better generalization ability. Experimental results demonstrate that the proposed model outperforms the state-of-The-Art no-reference SIQA methods. © 2019 IEEE.

关键词： Convolution

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Pyramid Real image Denoising Network 34

Pyramid Real Image Denoising Network

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

作者： Zhao, Yiyun Jiang, Zhuqing Men, Aidong Ju, Guodong Beijing University of Posts and Telecommunications Beijing China GuangDong TUS-TuWei Technology Co. Ltd Guangzhou China

ISBN: (纸本)9781728137230

While deep Convolutional Neural Networks (CNNs) have shown extraordinary capability of modelling specific noise and denoising, they still perform poorly on real-world noisy images. The main reason is that the real-world noise is more sophisticated and diverse. To tackle the issue of blind denoising, in this paper, we propose a novel pyramid real image denoising network (PRIDNet), which contains three stages. First, the noise estimation stage uses channel attention mechanism to recalibrate the channel importance of input noise. Second, at the multi-scale denoising stage, pyramid pooling is utilized to extract multi-scale features. Third, the stage of feature fusion adopts a kernel selecting operation to adaptively fuse multi-scale features. Experiments on two datasets of real noisy photographs demonstrate that our approach can achieve competitive performance in comparison with state-of-The-Art denoisers in terms of both quantitative measure and visual perception quality. © 2019 IEEE.

关键词： image denoising

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