Research Article Open Access

An Efficient Video Compression Framework using Deep Convolutional Neural Networks (DCNN)

Kommerla Siva Kumar1, P. Bindhu Madhavi2 and K. Janaki3
  • 1 Department of Computer Science and Engineering, R.V.R and J.C College of Engineering, Andhra Pradesh, India
  • 2 Department of AI and ML, The Oxford College of Engineering, Karnataka, India
  • 3 Department of Computer Science and Engineering-AI, Faculty of Engineering, Jain Deemed to be University, Karnataka, India

Abstract

In the current world, video streaming has grown in popularity and now accounts for a large percentage of internet traffic, making it challenging for service providers to broadcast videos at high rates while utilizing less storage space. To follow inefficient analytical coding design, previous video compression prototypes require non-learning-based designs. As a result, we propose a DCNN technique that integrates OFE-Net, MVE-Net, MVD-Net, MC-Net, RE-Net, and RD-Net for getting an ideal collection of frames by linking each frame pixel with preceding and following frames, then finding linked blocks and minimizing un needed pixels. In terms of MS-SIM and PSNR, the proposed DCNN approach produces good video quality at low bit rates.

Journal of Computer Science
Volume 18 No. 7, 2022, 589-598

DOI: https://doi.org/10.3844/jcssp.2022.589.598

Submitted On: 30 March 2022 Published On: 13 July 2022

How to Cite: Kumar, K. S., Madhavi, P. B. & Janaki, K. (2022). An Efficient Video Compression Framework using Deep Convolutional Neural Networks (DCNN). Journal of Computer Science, 18(7), 589-598. https://doi.org/10.3844/jcssp.2022.589.598

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Keywords

  • Deep Neural Networks
  • Encoding
  • Decoding
  • Video Compression