Optimized Visual Internet of Things in Video Processing for Video Streaming

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Published: Jun 2, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i5s.7045
Keywords:
Visual Internet of Things, Visual Sensor, Video streaming, video compression, 5G networks, Packet loss

Main Article Content

Bagade Shilpa
Research Scholar , Department of ECE, KLEF, Hyderabad, India
Budati Anil Kumar
Associate Professor, Department of ECE, KG Reddy College of Engineering &Technology Hyderabad, India
L Koteswara Rao
Principal,KLEF, Hyderabad, India

Abstract

The global expansion of the Visual Internet of Things (VIoT) has enabled various new applications during the last decade through the interconnection of a wide range of devices and sensors.Frame freezing and buffering are the major artefacts in broad area of multimedia networking applications occurring due to significant packet loss and network congestion. Numerous studies have been carried out in order to understand the impact of packet loss on QoE for a wide range of applications. This paper improves the video streaming quality by using the proposed framework Lossy Video Transmission (LVT)  for simulating the effect of network congestion on the performance of  encrypted static images sent over wireless sensor networks.The simulations are intended for analysing video quality and determining packet drop resilience during video conversations.The assessment of emerging trends in quality measurement, including picture preference, visual attention, and audio visual quality is checked. To appropriately quantify the video quality loss caused by the encoding system, various encoders compress video sequences at various data rates.Simulation results for different QoE metrics with respect to user developed videos have been demonstrated which outperforms the existing metrics.

Article Details

How to Cite
Shilpa, B. ., Kumar, B. A. ., & Rao, L. K. . (2023). Optimized Visual Internet of Things in Video Processing for Video Streaming . International Journal on Recent and Innovation Trends in Computing and Communication, 11(5s), 362–369. https://doi.org/10.17762/ijritcc.v11i5s.7045
Issue
Vol. 11 No. 5s (2023): Special Issue on Advances in Computing Systems for Web-Based Machine Learning Applications
Section
Articles

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