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Effective and Efficient Video Compression by the Deep Learning Techniques

Karthick Panneerselvam1,2,*, K. Mahesh1, V. L. Helen Josephine3, A. Ranjith Kumar2

1 Department of Computer Applications, Alagappa University, Karaikudi, India
2 Department of Computer Science and Engineering, lovely professional University, Phagwara, Punjab, India
3 School of Business and Management, Christ University, Bengaluru, Karnataka, India

* Corresponding Author: Karthick Panneerselvam. Email: email

Computer Systems Science and Engineering 2023, 45(2), 1047-1061. https://doi.org/10.32604/csse.2023.030513

Abstract

Deep learning has reached many successes in Video Processing. Video has become a growing important part of our daily digital interactions. The advancement of better resolution content and the large volume offers serious challenges to the goal of receiving, distributing, compressing and revealing high-quality video content. In this paper we propose a novel Effective and Efficient video compression by the Deep Learning framework based on the flask, which creatively combines the Deep Learning Techniques on Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN). The video compression method involves the layers are divided into different groups for data processing, using CNN to remove the duplicate frames, repeating the single image instead of the duplicate images by recognizing and detecting minute changes using GAN and recorded with Long Short-Term Memory (LSTM). Instead of the complete image, the small changes generated using GAN are substituted, which helps with frame-level compression. Pixel wise comparison is performed using K-nearest Neighbours (KNN) over the frame, clustered with K-means and Singular Value Decomposition (SVD) is applied for every frame in the video for all three colour channels [Red, Green, Blue] to decrease the dimension of the utility matrix [R, G, B] by extracting its latent factors. Video frames are packed with parameters with the aid of a codec and converted to video format and the results are compared with the original video. Repeated experiments on several videos with different sizes, duration, Frames per second (FPS), and quality results demonstrated a significant resampling rate. On normal, the outcome delivered had around a 10% deviation in quality and over half in size when contrasted, and the original video.

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Cite This Article

APA Style
Panneerselvam, K., Mahesh, K., Josephine, V.L.H., Kumar, A.R. (2023). Effective and efficient video compression by the deep learning techniques. Computer Systems Science and Engineering, 45(2), 1047-1061. https://doi.org/10.32604/csse.2023.030513
Vancouver Style
Panneerselvam K, Mahesh K, Josephine VLH, Kumar AR. Effective and efficient video compression by the deep learning techniques. Comput Syst Sci Eng. 2023;45(2):1047-1061 https://doi.org/10.32604/csse.2023.030513
IEEE Style
K. Panneerselvam, K. Mahesh, V.L.H. Josephine, and A.R. Kumar "Effective and Efficient Video Compression by the Deep Learning Techniques," Comput. Syst. Sci. Eng., vol. 45, no. 2, pp. 1047-1061. 2023. https://doi.org/10.32604/csse.2023.030513



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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