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Neural Network based Inter bi-prediction Blending

Neural Network based Inter bi-prediction Blending

引用

IEEE visual communications and image processing (VCIP)

作者： Franck Galpin Philippe Bordes Thierry Dumas Pavel Nikitin Fabrice Le Leannec InterDigital Inc.

ISBN: (纸本)9781728173221

This paper presents a learning-based method to improve bi-prediction in video coding. In conventional video coding solutions, the motion compensation of blocks from already decoded reference pictures stands out as the principal tool used to predict the current frame. Especially, the bi-prediction, in which a block is obtained by averaging two different motion-compensated prediction blocks, significantly improves the final temporal prediction accuracy. In this context, we introduce a simple neural network that further improves the blending operation. A complexity balance, both in terms of network size and encoder mode selection, is carried out. Extensive tests on top of the recently standardized VVC codec are performed and show a BD-rate improvement of −1.4% in random access configuration for a network size of fewer than 10k parameters. We also propose a simple CPU-based implementation and direct network quantization to assess the complexity/gains tradeoff in a conventional codec framework.

关键词： Video coding Learning systems Quantization (signal) visual communication image processing Neural networks Motion compensation

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Data Transformer for Anomalous Trajectory Detection

Data Transformer for Anomalous Trajectory Detection

引用

IEEE visual communications and image processing (VCIP)

作者： Hsuan-Jen Psan Wen-Jiin Tsai National Yang Ming Chiao Tung University HsinChu Taiwan

ISBN: (纸本)9781728173221

Anomaly detection is an important task in many traffic applications. Methods based on deep learning networks reach high accuracy; however, they typically rely on supervised training with large annotated data. Considering that anomalous data are not easy to obtain, we present data transformation methods which convert the data obtained from one intersection to other intersections to mitigate the effort of collecting training data. The proposed methods are demonstrated on the task of anomalous trajectory detection. A General model and a Universal model are proposed. The former focuses on saving data collection effort; the latter further reduces the network training effort. We evaluated the methods on the dataset with trajectories from four intersections in GTA V virtual world. The experimental results show that with significant reduction in data collecting and network training efforts, the proposed anomalous trajectory detection still achieves state-of-the-art accuracy.

关键词： Training Deep learning visual communication image processing Training data Data collection Transformers

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visually Optimized Two-Pass Rate Control for Video Coding Using the Low-Complexity XPSNR Model

Visually Optimized Two-Pass Rate Control for Video Coding Us...

引用

IEEE visual communications and image processing (VCIP)

作者： Christian R. Helmrich Ivan Zupancic Jens Brandenburg Valeri George Adam Wieckowski Benjamin Bross Video Communication and Applications Group Fraunhofer Heinrich Hertz Institute (HHI) Berlin Germany

ISBN: (纸本)9781728173221

Two-pass rate control (RC) schemes have proven useful for generating low-bitrate video-on-demand or streaming catalogs. visually optimized encoding particularly using latest-generation coding standards like Versatile Video Coding (VVC), however, is still a subject of intensive study. This paper describes the two-pass RC method integrated into version 1 of VVenC, an open VVC encoding software. The RC design is based on a novel two-step rate-quantization parameter (R-QP) model to derive the second-pass coding parameters, and it uses the low-complexity XPSNR visual distortion measure to provide numerically as well as visually stable, perceptually R-D optimized encoding results. Random-access evaluation experiments confirm the improved objective as well as subjective performance of our RC solution.

关键词： Video coding visualization image coding visual communication Distortion Software Numerical models

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SalGFCN: Graph Based Fully Convolutional Network for Panoramic Saliency Prediction

SalGFCN: Graph Based Fully Convolutional Network for Panoram...

引用

IEEE visual communications and image processing (VCIP)

作者： Yiwei Yang Yucheng Zhu Zhongpai Gao Guangtao Zhai School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781728173221

The saliency prediction of panoramic images is dramatically affected by the distortion caused by non-Euclidean geometry characteristic. Traditional CNN based saliency pre-diction algorithms for 2D images are no longer suitable for 360-degree images. Intuitively, we propose a graph based fully convolutional network for saliency prediction of 360-degree images, which can reasonably map panoramic pixels to spherical graph data structures for representation. The saliency prediction network is based on residual U-Net architecture, with dilated graph convolutions and attention mechanism in the bottleneck. Furthermore, we design a fully convolutional layer for graph pooling and unpooling operations in spherical graph space to retain node-to-node features. Experimental results show that our proposed method outperforms other state-of-the-art saliency models on the large-scale dataset.

关键词： Geometry visual communication image processing Distributed databases Predictive models Prediction algorithms Distortion

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Optimization of Probability Distributions for Residual Coding of Screen Content

Optimization of Probability Distributions for Residual Codin...

引用

IEEE visual communications and image processing (VCIP)

作者： Hannah Och Tilo Strutz André Kaup Friedrich-Alexander University Erlangen-Nürnberg (FAU) Multimedia Communications and Signal Processing Erlangen Germany Deutsche Telekom AG Leipzig University of Telecommunications Institute of Communications Engineering Leipzig Germany

ISBN: (纸本)9781728173221

Probability distribution modeling is the basis for most competitive methods for lossless coding of screen content. One such state-of-the-art method is known as soft context formation (SCF). For each pixel to be encoded, a probability distribution is estimated based on the neighboring pattern and the occurrence of that pattern in the already encoded image. Using an arithmetic coder, the pixel color can thus be encoded very efficiently, provided that the current color has been observed before in association with a similar pattern. If this is not the case, the color is instead encoded using a color palette or, if it is still unknown, via residual coding. Both palette-based coding and residual coding have significantly worse compression efficiency than coding based on soft context formation. In this paper, the residual coding stage is improved by adaptively trimming the probability distributions for the residual error. Furthermore, an enhanced probability modeling for indicating a new color depending on the occurrence of new colors in the neighborhood is proposed. These modifications result in a bitrate reduction of up to 2.9 % on average. Compared to HEVC (HM-16.21 + SCM-8.8) and FLIF, the improved SCF method saves on average about 11 % and 18 % rate, respectively.

关键词： Adaptation models image coding image color analysis visual communication Bit rate Probability distribution Optimization

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Progressive Co-Attention Network for Fine-Grained visual Classification

Progressive Co-Attention Network for Fine-Grained Visual Cla...

引用

IEEE visual communications and image processing (VCIP)

作者： Tian Zhang Dongliang Chang Zhanyu Ma Jun Guo Pattern Recognition and Intelligent System Lab. Beijing University of Posts and Telecommunications Beijing China Beijing Academy of Artificial Intelligence Beijing China

ISBN: (纸本)9781728173221

Fine-grained visual classification aims to recognize images belonging to multiple sub-categories within a same category. It is a challenging task due to the inherently subtle variations among highly-confused categories. Most existing methods only take an individual image as input, which may limit the ability of models to recognize contrastive clues from different images. In this paper, we propose an effective method called progressive co-attention network (PCA-Net) to tackle this problem. Specifically, we calculate the channel-wise similarity by encouraging interaction between the feature channels within same-category image pairs to capture the common discriminative features. Considering that complementary information is also crucial for recognition, we erase the prominent areas enhanced by the channel interaction to force the network to focus on other discriminative regions. The proposed model has achieved competitive results on three fine-grained visual classification benchmark datasets: CUB-200-2011, Stanford Cars, and FGVC Aircraft.

关键词： visualization image recognition visual communication Atmospheric modeling Force Benchmark testing Automobiles

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An Error Self-learning Semi-supervised Method for No-reference image Quality Assessment

An Error Self-learning Semi-supervised Method for No-referen...

引用

IEEE visual communications and image processing (VCIP)

作者： Yingjie Feng Sumei Li Sihan Hao School of Electrical and Information Engineering Tianjin University TianJin China

ISBN: (纸本)9781728173221

In recent years, deep learning has achieved significant progress in many respects. However, unlike other research fields with millions of labeled data such as image recognition, only several thousand labeled images are available in image quality assessment (IQA) field for deep learning, which heavily hinders the development and application for IQA. To tackle this problem, in this paper, we proposed an error self-learning semi-supervised method for no-reference (NR) IQA (ESSIQA), which is based on deep learning. We employed an advanced full reference (FR) IQA method to expand databases and supervise the training of network. In addition, the network outputs of expanding images were used as proxy labels replacing errors between subjective scores and objective scores to achieve error self-learning. Two weights of error back propagation were designed to reduce the impact of inaccurate outputs. The experimental results show that the proposed method yielded comparative effect.

关键词： image quality Deep learning Training Backpropagation image recognition visual communication Databases

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Deep Motion Flow Aided Face Video De-identification

Deep Motion Flow Aided Face Video De-identification

引用

IEEE visual communications and image processing (VCIP)

作者： Yunqian Wen Bo Liu Rong Xie Jingyi Cao Li Song Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science University of Technology Sydney Sydney Australia

ISBN: (纸本)9781728173221

Advances in cameras and web technology have made it easy to capture and share large amounts of face videos over to an unknown audience with uncontrollable purposes. These raise increasing concerns about unwanted identity-relevant computer vision devices invading the characters's privacy. Previous de-identification methods rely on designing novel neural networks and processing face videos frame by frame, which ignore the data feature in redundancy and continuity. Besides, these techniques are incapable of well-balancing privacy and utility, and per-frame evaluation is easy to cause flicker. In this paper, we present deep motion flow, which can create remarkable de-identified face videos with a good privacy-utility tradeoff. It calculates the relative dense motion flow between every two adjacent original frames and runs the high quality image anonymization only on the first frame. The de-identified video will be obtained based on the anonymous first frame via the relative dense motion flow. Extensive experiments demonstrate the effectiveness of our proposed de-identification method.

关键词： Privacy Computer vision visual communication image processing Redundancy Neural networks Cameras

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The Effect of SAR Speckle Removal in SAR-Optical image Fusion

The Effect of SAR Speckle Removal in SAR-Optical Image Fusio...

引用

IEEE Signal processing and communications Applications (SIU)

作者： Semih Genç ay Caner Ö zcan Bilgisayar Teknolojileri Manisa Celal Bayar &#x00DC niversitesi Manisa T&#x00FC rkiye Yaz&#x0131 l&#x0131 m M&#x00FC hendisli&#x011F i Karab&#x00FC k &#x00DC niversitesi Karab&#x00FC k T&#x00FC

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450935

Due to the imaging mechanism of Synthetic Aperture Radar (SAR) and the noise in the images, visual identification of objects in the scene is not as easy as in optical images. SAR images have limited color information and cannot reflect the spectral information of objects. Optical images, on the other hand, have rich spectral information. SAR-Optical image fusion is an important area of study so that SAR data can be easily evaluated by anyone, but it is difficult to find a matching SAR and optical image of the same scene. In order to overcome this difficulty, Sentinel-1 and Sentinel-2 datasets have been published and image fusion studies have been carried out with various methods. However, it has been observed that the effect of SAR noise removal before merging on image fusion methods has not been investigated. In the studies conducted to investigate this effect, five different fusion algorithms used in the literature were tested with twenty different image groups using different noise reduction ratios. The success of the fusion results obtained was compared with five different metrics that are widely used in the literature. The images and metric results obtained as a result of the tests showed that the removal of speckle noise in the SAR data has a positive effect on the fusion results.

关键词： Measurement visualization Speckle Optical imaging Adaptive optics Optical sensors Optical reflection

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Learn A Compression for Objection Detection - VAE with a Bridge

Learn A Compression for Objection Detection - VAE with a Bri...

引用

IEEE visual communications and image processing (VCIP)

作者： Yixin Mei Fan Li Li Li Zhu Li School of Information and Communications Engineering Xi'an Jiaotong University Xi'an China University of Science and Technology of China Hefei China University of Missouri-Kansas City Kansas City MO USA

ISBN: (纸本)9781728173221

Recent advances in sensor technology and wide deployment of visual sensors lead to a new application whereas compression of images are not mainly for pixel recovery for human consumption, instead it is for communication to cloud side machine vision tasks like classification, identification, detection and tracking. This opens up new research dimensions for a learning based compression that directly optimizes loss function in vision tasks, and therefore achieves better compression performance vis-a-vis the pixel recovery and then performing vision tasks computing. In this work, we developed a learning based compression scheme that learns a compact feature representation and appropriate bitstreams for the task of visual object detection. Variational Auto-Encoder (VAE) framework is adopted for learning a compact representation, while a bridge network is trained to drive the detection loss function. Simulation results demonstrate that this approach is achieving a new state-of-the-art in task driven compression efficiency, compared with pixel recovery approaches, including both learning based and handcrafted solutions.

关键词： Bridges Training visualization image coding visual communication Transforms Object detection

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