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检索条件"任意字段=Conference on Visual Communications and Image Processing"

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Quality comparison of 360° 8K images compressed by conventional and deep learning algorithms

Quality comparison of 360° 8K images compressed by conventi...

引用

International conference on Radioelektronika

作者： Jan Kufa Adam Budac Dept. of Radio Electronics Brno Faculty of Electrical Engineering and Communications Czech Republic

Due to the accessibility of virtual reality in recent years, there has been a great interest in producing and streaming omnidirectional $(360 ^{\circ}$ field of view) high resolution images and videos. Since both high resolution and high quality are demanding for the storage and distribution of such content, the use of advanced compression methods is a key factor in achieving this goal. This paper provides an objective comparison of conventional image compression codecs (JPEG, JPEG XL, HEIC, AVIF, VVC Intra) and deep learning image compression algorithms with a JPEG AI framework recommendation. The visual quality evaluation is based on ten images from publicly available databases compressed to predetermined bit rates. Six full reference objective metrics (WS-PSNR, MS-SSIM, VIFp, FSIMc, GMSD, VMAF) are used to evaluate the visual quality of the compressed images. Modern image compression codecs outperform the oldest and most widely used codec JPEG in terms of bandwidth reduction but require more processing power and system resources.

关键词： Deep learning visualization image coding Codecs image resolution Bit rate Transform coding

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Localization-Aware Multi-Scale Representation Learning for Repetitive Action Counting

Localization-Aware Multi-Scale Representation Learning for R...

引用

IEEE visual communications and image processing (VCIP)

作者： Sujia Wang Xiangwei Shen Yansong Tang Xin Dong Wenjia Geng Lei Chen Shenzhen International Graduate School Tsinghua University Department of Automation Tsinghua University

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Repetitive action counting (RAC) aims to estimate the number of class-agnostic action occurrences in a video without exemplars. Most current RAC methods rely on a raw frame-to-frame similarity representation for period prediction. However, this approach can be significantly disrupted by common noise such as action interruptions and inconsistencies, leading to sub-optimal counting performance in realistic scenarios. In this paper, we introduce a foreground localization optimization objective into similarity representation learning to obtain more robust and efficient video features. We propose a Localization-Aware Multi-Scale Representation Learning (LMRL) framework. Specifically, we apply a Multi-Scale Period-Aware Representation (MPR) with a scale-specific design to accommodate various action frequencies and learn more flexible temporal correlations. Furthermore, we introduce the Repetition Foreground Localization (RFL) method, which enhances the representation by coarsely identifying periodic actions and incorporating global semantic information. These two modules can be jointly optimized, resulting in a more discerning periodic action representation. Our approach significantly reduces the impact of noise, thereby improving counting accuracy. Additionally, the framework is designed to be scalable and adaptable to different types of video content. Experimental results on the RepCountA and UCFRep datasets demonstrate that our proposed method effectively handles repetitive action counting.

关键词： Representation learning Location awareness Adaptation models Correlation visual communication image processing Noise Semantics Optimization Monitoring

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Development of a VS ultrasound diagnostic system with image evaluation functions

Development of a VS ultrasound diagnostic system with image ...

引用

IEEE Global conference on Consumer Electronics (GCCE)

作者： Jiayi Zhou Norihiro Koizumi Yu Nishiyama Ryosuke Tsumura Hiroyuki Tsukihara Naoki Matsumoto Department of Mechanical and Intelligent Systems The University of Electro-Communications Tokyo Japan Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan Health and Medical Research Institute National Institute of Advanced Industrial Science and Technology Tokyo Japan Graduate School of Engineering The University of Tokyo Tokyo Japan Department of Gastroenterology and Hepatology Nihon University Tokyo Japan

ISBN: (纸本)9781665492331

An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross-section of the target organ, which is constantly shifting due to patient respiratory motions. Therefore, we developed an ultrasound diagnostic robot that works in cooperation with a visual servo system based on deep learning that will help alleviate the burdens imposed on physicians. Two different image processing methods (BiSeNet V2) were used to detect the target kidney location, as well as to evaluate the appropriateness of the obtained ultrasound images (ResNet 50) in developed system.

关键词： Deep learning visualization Ultrasonic imaging image processing Medical services Probes Servomotors

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Error concealment based on robust optical flow

Error concealment based on robust optical flow

引用

International conference on communications, Circuits and Systems (ICCCAS)

作者： Zhi-Heng Zhou Sheng-Li Xie College of Electronic & Information Engineering South China University of Technology Guangzhou China

ISBN: (纸本)0780390156

During transmission of encoded digital video sequences over noisy communication channels, the information of the images is altered or lost. A new error concealment method based on robust optical flow is proposed, as a post-processing tool at the decoder side to solve this problem. The conventional error concealment methods often result in blocking artifacts because of inaccurate motion vector estimation. We use the attractive property of optical flow technique on motion estimation to recover the lost block in a pixel-wise manner. In order to increase the robustness, the Lorentzian function is used to construct the energy function. Simulation results show that the proposed method can recover the higher quality image on both subjective visual evaluation and objective numerical metrics, compared to the existing error concealment methods.

关键词： Robustness image motion analysis Computer errors Motion estimation Optical sensors Video compression Educational institutions Video sequences Decoding Motion compensation

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Depth-based image processing for 3d video rendering applications

Depth-based image processing for 3d video rendering applicat...

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International conference on Systems, Signals and image processing, IWSSIP

作者： Terence Zarb Carl James Debono Department of Communications and Computer Engineering University of Malta Msida Malta

Continuous advances in video rendering platforms and image processing techniques have made possible innovative multimedia services that are changing the way visual media is consumed. An interesting application is free-viewpoint television (FTV), which allows the viewer to interactively select the scene viewing angle. To allow smooth navigation within the scene, virtual views must be rendered between the actual cameras. One solution for generating 3D scenes is through the capture of multi-view videos and their corresponding depth maps, with the latter supplying scene geometry information, which is essential for view synthesis. In this paper, we present a depth image-based rendering (DIBR) algorithm which utilizes improved image processing techniques to synthesize virtual views with rendering quality surpassing that of current state-of-the-art algorithms. On average, a peak signal-to-noise ratio (PSNR) gain of 2.5 dB and 1.5 dB for the Ballet and Breakdancers sequences, respectively, is achieved when compared to current solutions.

关键词： Rendering (computer graphics) Educational institutions image resolution Brightness IEC standards Transform coding

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Deep Blind Video Quality Assessment for User Generated Videos

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

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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

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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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Handling Class Imbalance in Black-Box Unsupervised Domain Adaptation with Synthetic Minority Over-Sampling

Handling Class Imbalance in Black-Box Unsupervised Domain Ad...

引用

IEEE visual communications and image processing (VCIP)

作者： Yawen Zou Chunzhi Gu Zi Wang Guang Li Jun Yu Chao Zhang Graduate School of Science & Engineering University of Toyama Toyama Japan Department of Computer Science & Engineering Toyohashi University of Technology Toyohashi Japan Institute of Science & Technology Niigata University Niigata Japan MDS Center Hokkaido University Hokkaido Japan Faculty of Engineering University of Toyama Toyama Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Black-box unsupervised domain adaptation (BBUDA) is a challenging task that transfers knowledge from the source domain to the target domain without access to the source data and source model, thus alleviating public concerns about data security. However, BBUDA requires the source model to function as a black-box predictor for the target data, and the pseudo-labels often exhibit class imbalance, which degrades the performance. To tackle this problem, we propose employing the synthetic minority oversampling technique (SMOTE) and adaptive sampling to rebalance data. Given that predictions often contain errors, we first select reliable high-confidence data before using SMOTE to generate synthetic samples for the minority class. Second, we incrementally select high-confidence data from the remaining low-confidence data with an adaptive sampling rate for each class, in which the minority class (with the fewest samples) is assigned a higher sampling rate and the majority class (with the most samples) is assigned a lower sampling rate. The experimental results demonstrate that our method can mitigate the class imbalance and further improve the performance of the target model.

关键词： Adaptation models Accuracy visual communication image processing Data security Closed box Predictive models Data models Reliability

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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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A Theory of Occlusion for Improving Rendering Quality of Views

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

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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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