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Video Super Resolution Using Temporal Encoding ConvLSTM and Multi-Stage Fusion

Video Super Resolution Using Temporal Encoding ConvLSTM and ...

引用

IEEE visual communications and image processing (VCIP)

作者： Yuhang Zhang Zhenzhong Chen Shan Liu Wuhan University Wuhan China Tencent Media Lab Palo Alto CA USA

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Video super resolution is a challenging task and has attracted the attention of many researchers in recent years. In this paper, we propose a multi-stage spatio-temporal feature fusion network. Different from existing methods that only aggregate features from temporal branch once at a specified s tage of network, the proposed network is organized in a multi-stage manner so that the temporal correlation in features at different stages of the network can be fully exploited. Furthermore, we propose the temporal encoding convLSTM to effectively capture the temporal information at the end of each stage. Experiments on vid4 and viemo-90K demonstrate the effectiveness of the proposed method.

关键词： Feature extraction Correlation Encoding image reconstruction Training Task analysis Decoding

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Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation

Unsupervised Feedforward Feature (UFF) Learning for Point Cl...

引用

IEEE visual communications and image processing (VCIP)

作者： Min Zhang Pranav Kadam Shan Liu C.-C. Jay Kuo Media Communications Lab University of Southern California Los Angeles CA USA Tencent Media Lab Tencent America Palo Alto CA USA

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

In contrast to supervised backpropagation-based feature learning in deep neural networks (DNNs), an unsupervised feedforward feature (UFF) learning scheme for joint classification and segmentation of 3D point clouds is proposed in this work. The UFF method exploits statistical correlations of points in a point cloud set to learn shape and point features in a one-pass feedforward manner through a cascaded encoder-decoder architecture. It learns global shape features through the encoder and local point features through the concatenated encoder-decoder architecture. The extracted features of an input point cloud are fed to classifiers f or shape classification and part segmentation. Experiments are conducted to evaluate the performance of the UFF method. For shape classification, the UFF is superior to existing unsupervised methods and on par with state-of-the-art DNNs. For part segmentation, the UFF outperforms semi-supervised methods and performs slightly worse than DNNs.

关键词： image segmentation Three-dimensional displays Correlation Shape visual communication Feature extraction Feedforward neural networks

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HDR image Compression with Convolutional Autoencoder

HDR Image Compression with Convolutional Autoencoder

引用

IEEE visual communications and image processing (VCIP)

作者： Fei Han Jin Wang Ruiqin Xiong Qing Zhu Baocai Yin Faculty of Information Technology Beijing University of Technology Beijing China Institute of Digital Media Peking University Beijing China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

As one of the next-generation multimedia technology, high dynamic range (HDR) imaging technology has been widely applied. Due to its wider color range, HDR image brings greater compression and storage burden compared with traditional LDR image. To solve this problem, in this paper, a two-layer HDR image compression framework based on convolutional neural networks is proposed. The framework is composed of a base layer which provides backward compatibility with the standard JPEG, and an extension layer based on a convolutional variational autoencoder neural networks and a post-processing module. The autoencoder mainly includes a nonlinear transform encoder, a binarized quantizer and a nonlinear transform decoder. Compared with traditional codecs, the proposed CNN autoencoder is more flexible and can retain more image semantic information, which will improve the quality of decoded HDR image. Moreover, to reduce the compression artifacts and noise of reconstructed HDR image, a post-processing method based on group convolutional neural networks is designed. Experimental results show that our method outperforms JPEG XT profile A, B, C and other methods in terms of HDR-VDP-2 evaluation metric. Meanwhile, our scheme also provides backward compatibility with the standard JPEG.

关键词： image reconstruction image coding Transform coding Decoding Convolutional codes Standards Neural networks

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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 visual communications and image processing (VCIP)

作者： Zhiming Zhou Yu Dong Li Song Rong Xie 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

One of the principal contradictions these days in the field of video i s lying between the booming demand 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 Predictive models Streaming media Solid modeling Prediction algorithms Adaptation models Video recording

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Multi-view high dynamic range reconstruction via gain estimation 34

Multi-view high dynamic range reconstruction via gain estima...

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

作者： Abedi, Firas Liu, Qiong Yang, You Islamic University Computer Technical Engineering Department Najaf54001 Iraq Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China

ISBN: (纸本)9781728137230

Multi-view high dynamic range reconstruction is a challenging problem, especially if the multi-view low dynamic range images are obtained from cameras arranged sparsely with limited shared view of vision among them. In this paper, we address the above challenge in addition to the back-lighting problem. We first enclose the geometry characteristic of the scene to rectify the outlier feature points. Consequently, an exposure gain is calculated according to those rectified features. After that, we extend the dynamic range for the multi-view low dynamic range images based on the estimated gain, then, generate a final high dynamic range image per view. Experimental results demonstrate superior performance for the proposed method over state-of-The-Art methods in both objective and subject comparisons. These results suggest that our method is suitable to improve the visual quality of multi-view low dynamic range images captured in low back-lighting conditions via commercial cameras sparsely located among each other. © 2019 IEEE.

关键词： Cameras

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Unsupervised Point Cloud Registration via Salient Points Analysis (SPA)

Unsupervised Point Cloud Registration via Salient Points Ana...

引用

IEEE visual communications and image processing (VCIP)

作者： Pranav Kadam Min Zhang Shan Liu C.-C. Jay Kuo University of Southern California Los Angeles CA USA Tencent America Palo Alto CA USA

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

An unsupervised point cloud registration method, called salient points analysis (SPA), is proposed in this work. The proposed SPA method can register two point clouds effectively using only a small subset of salient points. It first applies the PointHop++ method to point clouds, finds corresponding salient points in two point clouds based on the local surface characteristics of points and performs registration by matching the corresponding salient points. The SPA method offers several advantages over the recent deep learning based solutions for registration. Deep learning methods such as PointNetLK and DCP train end-to-end networks and rely on full supervision (namely, ground truth transformation matrix and class label). In contrast, the SPA is completely unsupervised. Furthermore, SPA's training time and model size are much less. The effectiveness of the SPA method is demonstrated by experiments on seen and unseen classes and noisy point clouds from the ModelNet-40 dataset.

关键词： Deep learning Training Three-dimensional displays visual communication image processing Registers Noise measurement

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A Multiple Triplet-Ranking Model for Fine-Grained Sketch-Based image Retrieval 34

A Multiple Triplet-Ranking Model for Fine-Grained Sketch-Bas...

引用

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

作者： Xue, Jingyi Zhou, Yun Jiang, Zhuqing Xie, Yao Li, Xiaoyu Beijing University of Posts and Telecommunications China Academy of Broadcasting Science Beijing China

ISBN: (纸本)9781728137230

Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of matching an input sketch with a specific photo containing the same instance. The key challenge of learning a FG-SBIR model is to bridge the domain gap between photo and sketch. Most existing approaches build a joint embedding space where two domains can be directly compared. They only focus on the highly abstract features in final fully connected (FC) layer, ignore some low-level semantic concepts in convolutional layers. In this paper, we propose a multiple triplet-ranking model in FG-SBIR task. Specially, we introduce an auxiliary supervision loss function in the convolutional layer, and we use the fusion of features from convolutional layer and final FC layer to build the joint embedding space. Extensive experiments show that the proposed multiple triplet-ranking model significantly outperforms the state-of-The-Art. © 2019 IEEE.

关键词： Embeddings

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Machine Learning for Photometric Redshift Estimation of Quasars with Different Samples

Machine Learning for Photometric Redshift Estimation of Quas...

引用

IEEE visual communications and image processing (VCIP)

作者： Yanxia Zhang Xin Jin Jingyi Zhang Yongheng Zhao CAS Key Laboratory of Optical Astronomy National Astronomical Observatories Beijing China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

We compare the performance of Support Vector Machine, XGBoost, LightGBM, k-Nearest Neighbors, Random forests and Extra-Trees on the photometric redshift estimation of quasars based on the SDSS_WISE sample. For this sample, LightGBM shows its superiority in speed while k-Nearest Neighbors, Random forests and Extra-Trees show better performance. Then k-Nearest Neighbors, Random forests and Extra-Trees are applied on the SDSS, SDSS_WISE, SDSS_UKIDSS, WISE_UKIDSS and SDSS_WISE_UKIDSS samples. The results show that the performance of an algorithm depends on the sample selection, sample size, input pattern and information from different bands; for the same sample, the more information the better performance is obtained, but different algorithms shows different accuracy; no single algorithm shows its superiority on every sample.

关键词： Support vector machines Machine learning algorithms visual communication Estimation Feature extraction Random forests image reconstruction

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A Progressive network based on residual multi-scale aggregation for image super-resolution 34

A Progressive network based on residual multi-scale aggregat...

引用

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

作者： Liu, Anqi Li, Sumei Chen, Sheng Tianjin University School of Electrical and Information Engineering Tianjin China

ISBN: (纸本)9781728137230

In recent years, with the rapid development of deep learning, single image super-resolution based on convolution neural network has achieved extensive research. However, most CNN-based method has difficulty in training and obtaining high quality images for large scale factors. To address these issues, we propose a network, which reconstructs HR images at large factors by progressively performing 2× SR on the input from the previous level. At each level, cascaded residual multi-scale aggregation blocks are used. The U-residual unit in it makes network simplifier and training easier without performance degradation. The multi-scale dilated unit in it provides more comprehensive information for image reconstruction. Before upsampling, the channel attention mechanism is adopted to recalibrate features. We train the network with two-stage training strategy which could accelerate the convergence and achieve better performance. Experiment results show that our proposed method is superior to the state-of-The-Art methods on most datasets, especially on Urban100. © 2019 IEEE.

关键词： image reconstruction

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CNN-Based Anomaly Detection For Face Presentation Attack Detection With Multi-Channel images

CNN-Based Anomaly Detection For Face Presentation Attack Det...

引用

IEEE visual communications and image processing (VCIP)

作者： Yuge Zhang Min Zhao Longbin Yan Tiande Gao Jie Chen Centre of Intelligent Acoustics and Immersive Communications School of Marine Science and Technology Northwestern Polytechinical University China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Recently, face recognition systems have received significant attention, and there have been many works focused on presentation attacks (PAs). However, the generalization capacity of PAs is still challenging in real scenarios, as the attack samples in the training database may not cover all possible PAs. In this paper, we propose to perform the face presentation attack detection (PAD) with multi-channel images using the convolutional neural network based anomaly detection. Multi-channel images endow us with rich information to distinguish between different mode of attacks, and the anomaly detection based technique ensures the generalization performance. We evaluate the performance of our methods using the wide multi-channel presentation attack (WMCA) dataset.

关键词： Face recognition Feature extraction Faces Anomaly detection Training Task analysis Neural networks

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