QoECenter

Contributions

Development of a data acquisition and analysis platform for video QoE: track video viewing experiences in real time

Development and evaluation of QoE metrics: supports multiple algorithms of objective video quality evaluation, and provides subjective QoE evaluations based on user opinions on customized video presentation

Development of a DASH enabled content generation tool and web client for video adaption

System Block Diagram

Terminal and End User: records terminal information, monitors user-viewing activities, and collects users’ ratings

Environment and Interface: the software framework and runtime environment for the user terminal

QoECenter Controller the target end-to-end video distribution flows from video source to end user terminal

QoECenter Core Modules three levels of parameter control, data acquisition, and data-driven analysis per the streaming video lifecycle

Cloud Resources a collection of scalable networking, compute, storage, and data resources

Web Client

Video list recommendation

User data & user-viewing activities recoding

Ratings on customized aspects of user perception

A dash-enable web client allowing for adaptive bitrate streaming of video

Objective Video Quality Evaluation

Peak Signal to Noise Ratio (PSNR)

Structural Similarity Index Metric (SSIM)

Video Quality Measurement (VQM)

QoECenter provides analysis results of multiple algorithms of objective video quality evaluation in real time.

Brain-Controlled Satellite

Brain-Computer Interface Executes Control Commands

Onboard Payload Application Control

Video Recording and Transmission

Satellite Testing and Control

Satellite Management

All the Data You Need

Source Analysis for Video Information

QoS Parameter Setting for Encoding and Network

DASH Content Generation Parameters & MPD Files

Objective Video Quality Evaluation & Subjective User Ratings

User and Terminal Information

QoECenter exposes visualized interfaces for parameter control and data acquisition from video source level, system process level and end user level, and it provides data analysis consisting of both objective and subjective QoE evaluation.

Publications

Lingyan Zhang, Wanyu Ling, Shuwen Daizhou, and Li Kuang*. Feature Separation Graph Convolutional Networks for Skeleton-Based Action Recognition. Pacific Graphics, 2024. (CCF B)

Lingyan Zhang, Wanyu Ling, Shuwen Daizhou, and Li Kuang*. Feature Separation Graph Convolutional Networks for Skeleton-Based Action Recognition. ICASSP, 2024. (CCF B)

Zhaowen Wang, Qi Xie, Huan Zhang, Weihuan Min, Li Kuang*, and Lingyan Zhang*. RegGPT: A Tool for Cross-Domain Service Regulation Language Conversion, IEEE ICWS, 2024. (CCF B)

Kehua Guo, Ze Tao, Lingyan Zhang*, Bin Hu, and Xiaoyan Kui. Generalize Deep Neural Networks with Adaptive Regularization for Classifying, IEEE Transactions on Computational Social Systems, 2023.

Kehua Guo, Lingyan Zhang*, Jian Kang, Yifei Wang, Xiaokang Zhou, Feihong Zhu. LesionTalk:Core Data Extraction and Multi-Class Lesion Detection in IoT based Intelligent Healthcare. Transactions on Sensor Networks, 2022.

Qingmi Liang, Lingyan Zhang*, Li Kuang*, et al. MisuseHint: A Service for API Misuse Detection Based on Building Knowledge Graph from Document and Codebase.IEEE ICWS, 2022. (CCF B)

Zhe Tang, Lingyan Zhang*, Zhengyun Chen, Fang Qi, Shuhong Chen, Pest-YOLO: Deep Image Mining and Multi-Feature Fusion for Real-Time Agriculture Pest Detection. IEEE ICDM, 2021. (CCF B)

Lingyan Zhang, Yu-Chee Tseng, Yi-Bing Lin, Hung-Cheng Lin, Hung-Cheng Lin. FusionTalk: An IoT-based Reconfigurable Object Identification System. IoT Journal, 2020.

Affiliation and Foundation

School of Computer Science and Enginerring, Central South University, China

“Research on Mobile Edge Computing based QoE Evaluation for Streaming Services”

“Research on User Modeling and QoE Calculation with Edge Cloud Collaboration for Streaming Service”

Supported by the National Natural Science Foundation of China (No.62102458), Hunan Provincial Natural Science Foundation of China (2022JJ40640)