Title: Scalable image compression mechanism for surveillance video summary

Authors: T. Venkata Satya Vivek; Manoj Kumar Gupta; J. Pradeep Kandhasamy; Renu Kachhoria; Santwana S. Gudadhe; S. Lakshmi Narayanan

Addresses: Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation (Deemed to be University), Vaddeswaram, Guntur, Andhra Pradesh, India ' Trinity Institute of Innovations in Professional Studies, 2B/1, Knowledge Park-III, Greater Noida, Uttar Pradesh-201308, India ' School of Computing, Kalasalingam Academy of Research and Education, Tamil Nadu, India ' Pimpri Chinchwad College of Engineering, Pune, Maharashtra, 411044, India ' Pimpri Chinchwad College of Engineering, Pune, Maharashtra, 411044, India ' Department of Electronics and Communication Engineering, Gojan School of Education, Tamil Nadu, India

Abstract: The use of large-scale video surveillance systems is widespread in important areas such as home and public safety. Recognising and evaluating appropriate security measures is critical since these systems are vulnerable. A clear movie requires good compression. Lossy image compression may decrease the amount of bandwidth needed for picture transmission and the amount of storage available to a device, improving network performance. Neural networks have thrived in image processing thanks to deep learning. We present an image reduction technique based on semantic analysis based on the degree of human attention to each region of the picture. After evaluating the semantic images using a convolutional neural network (CNN), a compression bit-allocation algorithmic technique is used. This technique enhances video surveillance visual quality while keeping the same compression ratio.

Keywords: convolutional neural network; CNN; image compression; recurrent neural network; scalable image; video surveillance.

DOI: 10.1504/IJESMS.2024.139538

International Journal of Engineering Systems Modelling and Simulation, 2024 Vol.15 No.4, pp.153 - 160

Received: 13 Sep 2021
Accepted: 28 Jan 2022
Published online: 04 Jul 2024 *

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