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DOI: 10.14569/IJACSA.2021.0120360
PDF

Comprehensive Analysis of Flow Incorporated Neural Network based Lightweight Video Compression Architecture

Author 1: Sangeeta
Author 2: Preeti Gulia
Author 3: Nasib Singh Gill

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 3, 2021.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: The increasing video content over the internet motivated the exploration of novel approaches in the video compression domain. Though neural network based architectures have already emerge as de-facto in the field of image compression and analytics, their application in video compression also result in promising outputs. Adaptive and efficient compression techniques are required for video transmission over varying bandwidth. Several deep learning based techniques and enhancements were proposed and experimented but they didn’t exhibit full optimal behavior and are not end to end trained and optimized. In the zest of a pure and end to end trainable compression technique, a deep learning based video compression architecture has been proposed comprises of frame autoencoder, flow autoencoder and motion extension network for the reconstruction of predicted frames. The video compression network has been designed incrementally and trained with random emission steps strategy. The proposed work results in significant improvement in visual perception quality measured in SSIM and PSNR when compared to some state-of-art techniques but in trade-off with frame reconstruction time sheet.

Keywords: Deep learning; video compression; autoencoder; SSIM; PSNR

Sangeeta , Preeti Gulia and Nasib Singh Gill, “Comprehensive Analysis of Flow Incorporated Neural Network based Lightweight Video Compression Architecture” International Journal of Advanced Computer Science and Applications(IJACSA), 12(3), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120360

@article{2021,
title = {Comprehensive Analysis of Flow Incorporated Neural Network based Lightweight Video Compression Architecture},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120360},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120360},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {3},
author = {Sangeeta and Preeti Gulia and Nasib Singh Gill}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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