Effective and Efficient Video Compression by the Deep Learning Techniques

Title

Effective and Efficient Video Compression by the Deep Learning Techniques

Creator

Panneerselvam K.; Mahesh K.; Josephine V.L.H.; Kumar A.R.

Description

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 highquality 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. 2023 CRL Publishing. All rights reserved.

Source

Computer Systems Science and Engineering, Vol-45, No. 2, pp. 1047-1061.

Date

2023-01-01

Publisher

Tech Science Press

Subject

Convolutional neural networks (CNN); generative adversarial network (GAN); K-nearest neighbours (KNN); long short-term memory (LSTM); singular value decomposition (SVD); stochastic gradient descent (SGD)

Coverage

Panneerselvam K., Department of Computer Applications, Alagappa University, Karaikudi, India, Department of Computer Science and Engineering, Lovely professional University, Punjab, Phagwara, India; Mahesh K., Department of Computer Applications, Alagappa University, Karaikudi, India; Josephine V.L.H., School of Business and Management, Christ University, Karnataka, Bengaluru, India; Kumar A.R., Department of Computer Science and Engineering, Lovely professional University, Punjab, Phagwara, India

Rights

All Open Access; Hybrid Gold Open Access

Relation

ISSN: 2676192; CODEN: CSSEE

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.32604/csse.2023.030513

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143842997&doi=10.32604%2fcsse.2023.030513&partnerID=40&md5=a9155ad90a386ff34f9b4621ddea5d16