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Blind Panoramic image Quality Assessment via the Asymmetric Mechanism of Human Brain

Blind Panoramic Image Quality Assessment via the Asymmetric ...

引用

IEEE visual communications and image processing conference

作者： Yameng Xia Yongfang Wang Ye Peng School of Communication and Information Engineering Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China

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 low-frequency 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.

关键词： Feature extraction Nonlinear distortion image quality Databases Transform coding Brain modeling

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Keypoints and codewords selection for efficient bag-of-features representation

Keypoints and codewords selection for efficient bag-of-featu...

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Future of Information and Communication conference, FICC 2018

作者： Vinoharan, Veerapathirapillai Ramanan, Amirthalingam Computer Centre University of Jaffna Jaffna Sri Lanka Department of Computer Science University of Jaffna Jaffna Sri Lanka

ISBN: (纸本)9783030034016

Bag-of-features (BoF) representation is one of the most popular image representations that is used in visual object classification, owing to its simplicity and good performance. However, the BoF representation always faces the difficulty of curse of dimensionality that leads to huge computational cost and increased storage requirement. To create a discriminative and compact BoF representation, it is desired to eliminate ambiguous features before the construction of visual codebook and to select the informative codewords from the constructed codebook. In this paper, we propose a two-staged approach to create a discriminative and compact BoF representation for object recognition. In the first step, we eliminate ambiguous patch-based descriptors using an entropy-based filtering approach to retain high-quality descriptors. In the subsequent step, we select the informative codewords based on statistical measures. We have tested the proposed technique on Xerox7, UIUC texture, PASCAL VOC 2007 and Caltech101 benchmark datasets. Testing results show that more training features and/or a high-dimensional codebook do not contribute significantly to increase the performance of classification but it increases the overall model complexity and computational cost. The proposed preprocessing step of descriptor selection increases the discriminative power of a codebook, whereas the post-processing step of codeword selection maintains the codebook to be more compact. The proposed framework would help to optimise BoF representation to be efficient with steady performance. © Springer Nature Switzerland AG 2019.

关键词： image representation

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Fast image Deblurring Based on Dual-Exposure Prior

Fast Image Deblurring Based on Dual-Exposure Prior

引用

IEEE visual communications and image processing conference

作者： Xu Zhang Hai-Miao Hu Jialin Chen Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University Beijing China

image deblurring is an important task for practical applications. However, it remains a challenge due to the serious camera shake. In this paper, we propose a prior named Dual-Exposure Prior (DEP) according to the observation that image captured with a relatively short exposure time may achieve sharp edges. Based on the DEP, the blur kernels of the blurry images can be accurately estimated. An effective optimization between blur kernel estimation and intermediate image restoration is established by using the L_2-regularized DEP in the maximum a posterior framework. Each sub-problem of the optimization is convex, it can be solved in close-form with fast Fourier transforms acceleration. The experiments based on real and synthesized blurry images show that the proposed image deblurring algorithm can outperform the state-of-the-art methods in terms of subjective and objective quality. Moreover, the proposed algorithm can be implemented with significantly low computational complexity.

关键词： Kernel image restoration Estimation Gaussian noise Cameras Optimization Noise measurement

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RGB-thermal Imaging System Collaborated with Marker Tracking for Remote Breathing Rate Measurement

RGB-thermal Imaging System Collaborated with Marker Tracking...

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IEEE visual communications and image processing conference

作者： Lushuang Chen Ning Liu Menghan Hu Guangtao Zhai Institute of Image Communication and Information Processing Shanghai Jiao Tong University China

The pixel variation signal extracted from the nasal region of RGB-thermal images can be used to achieve breathing rate (BR) measurement. However, this method fails when the nasal region is not detected in complicated motion scenarios. In this paper, we develop an RGB-thermal imaging system collaborated with marker sticker to achieve unobtrusive and accurate BR measurement. Pixel variation signal of Regions of interest (ROI) is extracted from the thermal video and chest movement signal is extracted from the RGB video with the assistance of marker stickers. Subsequently, a custom-made time-domain signal processing approach is developed for determining BR. We further propose a method of splicing computation to measure the BR after separate processing of signal segments. We construct an RGB-thermal video dataset with different head and body movements to evaluate the effectiveness of the proposed algorithm. After linear regression analysis, the determination coefficient (R~2) of 0.905 has been observed for the estimated and reference BRs, indicating the feasibility of our proposed method in complex motion scenarios.

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

Deep quad path quality evaluator combined aesthetic estimati...

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IEEE visual communications and image processing conference

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

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.

关键词： Feature extraction Convolution Databases Correlation image sequences image quality Prediction algorithms

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Depthwise Separable Convolutional Neural Network for image Forensics

Depthwise Separable Convolutional Neural Network for Image F...

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IEEE visual communications and image processing conference

作者： Yifang Chen Feng Peng Xiangui Kang Z. Jane Wang Guangdong Key Lab of Information Security School of Data and Computer Science Sun Yat-sen University China ECE Dept. University of British Colombia Vancouver Canada

General-purpose forensics on small image patches appears to be feasible and important, but in fact poses a challenge due to insufficient statistics. Furthermore, there is a need to develop a forensic approach that can automatically learn effective and robust features related to image forensics with high parameter efficiency. In this paper, we propose a depthwise separable convolutional neural network (CNN) for the simultaneous detection of eleven types of image manipulations in image patches. Different from the previous CNNs based on standard convolution, depthwise separable convolution is introduced in the proposed CNN to adaptively extract forensics-related features from image patches with better parameter efficiency. When compared with four state-of-the-art methods, experiments demonstrate that the proposed CNN architecture can achieve better performance, e.g., the improvement in terms of accuracy in the detection of 32 × 32 images is up to 7.33%. It also achieves significantly better overall performance for different databases and better robustness against JPEG compression.

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Color image Compression with Transform Domain Down-Sampling and Deep Convolutional Reconstruction

Color Image Compression with Transform Domain Down-Sampling ...

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IEEE visual communications and image processing conference

作者： Yan Wang Shuyuan Zhu Xiandong Meng Bing Zeng Yuanfang Guo Ruiqin Xiong University of Electronic Science and Technology of China Chengdu China The Hong Kong University of Science and Technology Hong Kong China Beihang University Beijing China Peking University Beijing China

In this paper, we build up a new block-based color image compression scheme based on our proposed transform domain down-sampling method and deep convolutional reconstruction algorithm. Specifically, our proposed down-sampling scheme aims to down-sample each N × N transform block into the N/2 × N/2 block for the saving of bit-cost. On the other hand, the proposed deep convolutional reconstruction algorithm is employed to reconstruct the down-sampled block for a full-resolution reconstruction. We apply our proposed methods to both the chrominance components to compress color images. Experimental results show that our proposed method achieves excellent results when used in practice.

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Decomposed-Excitatory Antagonistic Model for Reliable Retinal Vessel Detection

Decomposed-Excitatory Antagonistic Model for Reliable Retina...

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IEEE visual communications and image processing conference

作者： Mohsin Challoob Yongsheng Gao School of Engineering Griffith University Brisbane Australia

A new retinal vessel detection method is introduced using an antagonistic model, consisting of the excitatory and inhibitory regions. It utilizes the model profile containing important image features such as lines and curves to detect retinal vessels. The excitatory area is analyzed into several sub-regions with multiple directions to decompose the local vessel structure, resulting in several profiles that include various vessel trees. Also, the strength of the inhibitory region is adaptively varied to differentiate vessel pixels from background while decreasing false detection. Then, each profile is linked to a multi-bound relaxed median operator for noise removal, and a logistic function for providing a soft transition between vessel and non-vessel regions. The final vessel tree is produced by fusing all profiles. The proposed method is evaluated on high-resolution and low-resolution fundus imaging databases (HRF and DRIVE), on which it outperforms state-of-the-art methods.

关键词： image segmentation Databases Pathology Retinal vessels Sensitivity Mathematical model

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Analysis of Smoothed LHE Methods for processing images with Optical Illusions 33

Analysis of Smoothed LHE Methods for Processing Images with ...

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33rd IEEE International conference on visual communications and image processing (IEEE VCIP)

作者： Ambalathankandy, Prasoon Shimada, Takeshi Takamaeda, Shinya Motomura, Masato Asai, Tetsuya Ikebe, Masayuki Hokkaido Univ Res Ctr Integrated Quantum Elect Sapporo Hokkaido Japan Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido Japan

ISBN: (纸本)9781538644584

To replicate human visual perception, we analyze processing images with optical illusion using edge preserving filters and smoothed local histogram equalization (LHE). images with the optical illusions are good models for gradual/rapid changes in contrast and strong edges, which are good cases for assessing the robustness of image filters. Here, we study and analyze the performance of smoothed LHE filters while processing perceptual illusion. Our studies conclude that, smoothed LHEs are useful in retaining actual edge forms in these images as they can operate using large kernel sizes. These large kernel size filters can construct sawtooth like edge and it corresponds to adequately wide halos. We also demonstrate the usefulness of smoothed LHE like tone mapping techniques in preserving naturalness, and we confirmed it by performing subjective visual test.

关键词： Edge preserving filter lateral inhibition local histogram equalization local tonemap optical illusion visual perception

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

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

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IEEE visual communications and image processing conference

作者： Junjie Lian Yongfang Wang Chuang Wang School of Communication and Information Engineering Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China

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.

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