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CRC-Based Multi-Error Correction of H.265 Encoded Videos in Wireless communications

CRC-Based Multi-Error Correction of H.265 Encoded Videos in ...

引用

IEEE visual communications and image processing (VCIP)

作者： Vivien Boussard Stéphane Coulombe François-Xavier Coudoux Patrick Corlay Anthony Trioux École de technologie supérieure Montreal Canada UPHF CNRS Univ. Lille ISEN Centrale Lille UMR 8520 - IEMN DOAE Valenciennes France

ISBN: (纸本)9781728173221

This paper analyzes the benefits of extending CRC-based error correction (CRC-EC) to handle more errors in the context of error-prone wireless networks. In the literature, CRC-EC has been used to correct up to 3 binary errors per packet. We first present a theoretical analysis of the CRC-EC candidate list while increasing the number of errors considered. We then analyze the candidate list reduction resulting from subsequent checksum validation and video decoding steps. Simulations conducted on two wireless networks show that the network considered has a huge impact on CRC-EC performance. Over a Bluetooth low energy (BLE) channel with Eb/No=8 dB, an average PSNR improvement of 4.4 dB on videos is achieved when CRC-EC corrects up to 5, rather than 3 errors per packet.

关键词： Bluetooth visual communication Wireless networks image processing Redundancy Error correction Decoding

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UHP-SOT: An Unsupervised High-Performance Single Object Tracker

UHP-SOT: An Unsupervised High-Performance Single Object Trac...

引用

IEEE visual communications and image processing (VCIP)

作者： Zhiruo Zhou Hongyu Fu Suya You Christoph C. Borel-Donohue C.-C. Jay Kuo Media Communications Lab University of Southern California Los Angeles CA USA Army Research Laboratory Adelphi Maryland USA

ISBN: (纸本)9781728173221

An unsupervised online object tracking method that exploits both foreground and background correlations is proposed and named UHP-SOT (Unsupervised High-Performance Single Object Tracker) in this work. UHP-SOT consists of three modules: 1) appearance model update, 2) background motion modeling, and 3) trajectory-based box prediction. A state-of-the-art discriminative correlation filters (DCF) based tracker is adopted by UHP-SOT as the first module. We point out shortcomings of using the first module alone such as failure in recovering from tracking loss and inflexibility in object box adaptation and then propose the second and third modules to overcome them. Both are novel in single object tracking (SOT). We test UHP-SOT on two popular object tracking benchmarks, TB-50 and TB-100, and show that it outperforms all previous unsupervised SOT methods, achieves a performance comparable with the best supervised deep-learning-based SOT methods, and operates at a fast speed (i.e. 22.7-32.0 FPS on a CPU).

关键词： Adaptation models Correlation visual communication image processing Predictive models Benchmark testing Object tracking

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A Novel Self-Knowledge Distillation Approach with Siamese Representation Learning for Action Recognition

A Novel Self-Knowledge Distillation Approach with Siamese Re...

引用

IEEE visual communications and image processing (VCIP)

作者： Duc-Quang Vu Thi-Thu-Trang Phung Jia-Ching Wang National Central University Taoyuan Taiwan Thai Nguyen University Thai Nguyen Vietnam

ISBN: (纸本)9781728173221

Knowledge distillation is an effective transfer of knowledge from a heavy network (teacher) to a small network (student) to boost students' performance. Self-knowledge dis-tillation, the special case of knowledge distillation, has been proposed to remove the large teacher network training process while preserving the student's performance. This paper intro-duces a novel Self-knowledge distillation approach via Siamese representation learning, which minimizes the difference between two representation vectors of the two different views from a given sample. Our proposed method, SKD-SRL, utilizes both soft label distillation and the similarity of representation vectors. Therefore, SKD-SRL can generate more consistent predictions and representations in various views of the same data point. Our benchmark has been evaluated on various standard datasets. The experimental results have shown that SKD-SRL significantly improves the accuracy compared to existing supervised learning and knowledge distillation methods regardless of the networks.

关键词： Representation learning Knowledge engineering Training Computer vision visual communication image processing Supervised learning

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A Full-Reference Quality Assessment Metric for Fine-Grained Compressed images

A Full-Reference Quality Assessment Metric for Fine-Grained ...

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IEEE visual communications and image processing (VCIP)

作者： Zicheng Zhang Wei Sun Xiongkuo Min Tao Wang Wei Lu Guangtao Zhai Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China

ISBN: (纸本)9781728173221

Compressed image quality assessment (IQA) has been a crucial part of a wide range of image services such as storage and transmission. Due to the effect of different bit rates and compression methods, the compressed images usually have different levels of quality. Nowadays, the mainstream full-reference (FR) metrics are effective to predict the quality of compressed images at coarse-grained levels, however, they may perform poorly when quality differences of the compressed images are quite subtle. To better improve the Quality of Experience (QoE) and provide useful guidance for compression algorithms, we propose an FR-IQA metric for fine-grained compressed images, which estimates the image quality by analyzing the difference of structure and texture. Our metric is mainly validated on the fine-grained compression IQA (FGIQA) database and is tested on other commonly used compression IQA databases as well. The experimental results show that our metric outperforms mainstream FR-IQA metrics on the fine-grained compression IQA database and also obtains competitive performance on the coarse-grained compression IQA databases.

关键词： Measurement image quality image coding visual communication Feature extraction Stability analysis Quality assessment

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image Enhancement of Finger Vein Patterns Based on the Guided Filter

Image Enhancement of Finger Vein Patterns Based on the Guide...

引用

7th International conference on communications, Signal processing, and Systems (CSPS)

作者： Zhan, Tao Ma, Hui Hu, Na Heilongjiang Univ Coll Elect Engn Harbin 150080 Peoples R China

ISBN: (纸本)9789811365041;9789811365034

To solve the problem that image enhancement of finger vein patterns based on traditional filtering methods fails to intuitively highlight the feature of edge protection, the experimental study model based on the guided filter is proposed. Through adding the comparison experiment between guided filter and bilateral filter, and doing the binary processing to the finger vein image after the process of the guided filtering and bilateral filtering, it can be found that some noises exist around the vein texture. In order to reduce or eliminate the interference, a traditional average filtering method is applied for denoising, which not only highlights the vein texture details but eliminates the interference in the post-processing, and at the same time, adjusting the filter parameters will cause a significant impact on the enhancement of finger vein image. A comparison experiment in false recognition rate between two filtering algorithms is conducted, and visual and numerical evaluations are performed on finger vein image after the process of enhancement and binarization;the result indicates that the guided filter has better edge protection feature and lower false recognition rate than the bilateral filter.

关键词： processing Guided filter Finger vein image enhancement Bilateral filter Binary processing Median filter

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An Industrial Instrument Panel Identification System Design Based on an Improved YOLOv7 and UAV

An Industrial Instrument Panel Identification System Design ...

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International conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Miao Ye Yuejin Huang Qiuxiang Jiang Peng Wen Hongwen Hu Jin Cheng School of Information and Communications Guilin University of Electronic Technology China

Industrial production often requires collecting and reading visual information on instrument panels. Traditional manual inspection and identification methods require considerable work, resulting in inefficient and inaccurate inspections. It is important to design an industrial instrument panel inspection and recognition system that can be applied in real time. In this paper, an industrial instrument panel recognition system is designed by combining an unmanned aerial vehicle (UAV) platform and deep learning techniques. For mobile UAVs, an ultrawideband (UWB) positioning module is used to implement the real-time UAV positioning and its fixed-point cruising function at each instrument panel location point. As the UAV moves around the instrument panel, a modified YOLOv7 model, compressed through a combination of pruning and quantization, is loaded to perform initial detection and recognition of the instrument panel image to ensure real-time transmission of the captured raw image to the ground side. On the ground-side server, the original YOLOv7 model is deployed to perform instrument panel detection on the initially captured images, filter out redundant background information, and then extract the tick marks and pointer information on the instrument panel through the U-Net semantic segmentation model, after which the tick marks and pointer information are processed using traditional image-processing methods. Finally, the instrument panel is identified through the angle method. This method can alleviate the accuracy loss caused by a lightweight model on the mobile side, thus ensuring the quality of accuracy. Finally, a web client is designed using Flask technology to display the uploaded instrument panel images and recognition results via a web terminal. A series of test experiments and their results show that the designed industrial instrument panel detection and recognition system is reliable and efficient in reading industrial instrument panel information in real tim

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5th International conference on Cognitive Systems and Signal processing, ICCSIP 2020

5th International Conference on Cognitive Systems and Signal...

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5th International conference on Cognitive Systems and Signal processing, ICCSIP 2020

ISBN: (纸本)9789811623356

The proceedings contain 59 papers. The special focus in this conference is on Cognitive Systems and Signal processing. The topics include: Application of Broad Learning System for image Classification Based on Deep Features;emotion Recognition Based on Graph Neural Networks;a Light-Weight Stereo Matching Network with Color Guidance Refinement;overview of Monocular Depth Estimation Based on Deep Learning;Non-contact Physiological Parameters Detection Based on MTCNN and EVM;texture Classification of a Miniature Whisker Sensor with Varied Contact Pose;the Realtime Indoor Localization Unmanned Aerial Vehicle;semantic-Based Road Segmentation for High-Definition Map Construction;transformer Region Proposal for Object Detection;a Feature Fusion Based Object Tracking Algorithm;Detection and Reconstruction of Transparent Objects with Infrared Projection-Based RGB-D Cameras;a Simulation-to-Real Autonomous Driving System Based on End-to-End Learning;Underwater SLAM Based on Forward-Looking Sonar;robust visual Odometry Using Semantic Information in Complex Dynamic Scenes;A Multi-sensor Data Fusion Method Based on Improved XGBoost Model for AGV Localization;Multi UAV Target Tracking Based on the Vision and Communication Information;an Accurate Positioning Method for Robotic Manipulation Based on Vision and Tactile Sensors;l1-Norm and Trace Lasso Based Locality Correlation Projection;episodic Training for Domain Generalization Using Latent Domains;A Novel Attitude Estimation Algorithm Based on EKF-LSTM Fusion Model;METAHACI: Meta-learning for Human Activity Classification from IMU Data;preface;Quantized Separable Residual Network for Facial Expression Recognition on FPGA;fusing Knowledge and Experience with Graph Convolutional Network for Cross-task Learning in visual Cognitive Development;factored Trace Lasso Based Linear Regression Methods: Optimizations and Applications.

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Evaluation Of Bitrate Ladders For Versatile Video Coder

Evaluation Of Bitrate Ladders For Versatile Video Coder

引用

IEEE visual communications and image processing (VCIP)

作者： Reda Kaafarani Mederic Blestel Thomas Maugey Michael Ropert Aline Roumy Mediakind France Rennes Inria Rennes France

ISBN: (纸本)9781728173221

Many video service providers take advantage of bitrate ladders in adaptive HTTP video streaming to account for different network states and user display specifications by providing bitrate/resolution pairs that best fit client's network conditions and display capabilities. These bitrate ladders, however, differ when using different codecs and thus the couples bitrate/resolution differ as well. In addition, bitrate ladders are based on previously available codecs (H.264/MPEG4-AVC, HEVC, etc.), i.e. codecs that are already in service, hence the introduction of new codecs e.g. Versatile Video Coding (VVC) requires re-analyzing these ladders. For that matter, we will analyze the evolution of the bitrate ladder when using VVC. We show how VVC impacts this ladder when compared to HEVC and H.264/AVC and in particular, that there is no need to switch to lower resolutions at the lower bitrates defined in the Call for Evidence on Transcoding for Network Distributed Video Coding (CfE).

关键词： Performance evaluation Codecs image resolution image coding visual communication Bit rate Transcoding

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Discrimination of Haploid and Diploid Maize Seeds Based on Deep Features 28

Discrimination of Haploid and Diploid Maize Seeds Based on D...

引用

28th Signal processing and communications Applications conference (SIU)

作者： Donmez, Emrah Inonu Univ Muhendislik Fak Bilgisayar Muhendisligi Malatya Turkey

ISBN: (纸本)9781728172064

One of the main processes in in vivo maternal haploid breeding method which is widely used in hybrid maize breeding in recent years is the separation of haploid and diploid individuals. Although different approaches have been proposed to make this distinction, the R1-nj color marker is widely and successfully used. The R1-nj color marker allows the visual separation of haploid and diploid individuals during the seed period. Nowadays, this distinction is done manually, causing loss of time and labor as well as high error. In this study, an open access dataset consisting of 3,000 maize seed images was used. The deep features from the FC6, FC7 and FC8 fully connected layers of the AlexNet architecture are classified with the support vector machine. 10-fold cross-validation test was used to evaluate model performances. Experimental results showed that the best classification performance is possible with 89.50% accuracy using deep features obtained from the FC6 fully connected layer.

关键词： haploid maize seed identification image processing convolutional neural networks deep features artificial learning

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Securing Intelligent Vehicular Networks Using AI-Driven Federated Learning

Securing Intelligent Vehicular Networks Using AI-Driven Fede...

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International conference on Computer and communications Technologies (ICCCT)

作者： N Shivaanivarsha J Swetha VL Ram Lashmi G S Yaswanth Rao Department of Electronics and Communication Engineering Sri Sairam Engineering College Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Analysis of Detour Index in Graph Joins and Special Graph Structures Vehicular networks are crucial for modern transportation safety and efficiency. However, in adverse weather conditions such as fog, reduced visibility severely disrupts vehicle-to-vehicle (V2V) communication, increasing the risk of chain accidents. Traditional centralized approaches for managing vehicular communication suffer from high latency, limited scalability, and data privacy concerns, making them unsuitable for real-time decision-making in dynamic environments. To address these challenges, this project proposes a Federated Learning Framework for Securing Vehicular Networks Using AI, enabling decentralized, privacy-preserving, and real-time threat detection in vehicular networks. The proposed system leverages Artificial Intelligence (AI) and Federated Learning (FL) to enhance situational awareness and predict collision risks. A deep learning-based object detection model, utilizing the YOLO algorithm, identifies obstacles, vehicles, and hazardous conditions in low-visibility environments. Edge detection techniques further improve visual clarity and detection accuracy. Federated Learning enables vehicles to collaboratively train AI models on local data without sharing raw information, ensuring data privacy and adaptability to real-world scenarios. Secure vehicle-to-vehicle (V2V) communication enhances decision-making speed, reducing latency in critical situations. By integrating AI-driven predictive analysis, federated learning, and decentralized data processing, this system significantly improves vehicular safety in challenging weather conditions. It ensures real-time hazard detection, reduces the likelihood of chain accidents, enhances data privacy, and scales effectively for large vehicular networks. This innovation marks a critical step toward intelligent, autonomous, and resilient transportation systems in smart cities.

关键词： YOLO Data privacy Federated learning image edge detection Decision making Vehicular ad hoc networks Transportation Real-time systems Artificial intelligence Meteorology

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