Welcome to the InfoSci Platform
Reference Hub6
Fractal Coding Based Video Compression Using Weighted Finite Automata

Fractal Coding Based Video Compression Using Weighted Finite Automata

Shailesh D. Kamble, Nileshsingh V. Thakur, Preeti R. Bajaj
Copyright: © 2018 |Volume: 9 |Issue: 1 |Pages: 19
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781522543527|DOI: 10.4018/IJACI.2018010107
Cite Article Cite Article

MLA

Kamble, Shailesh D., et al. "Fractal Coding Based Video Compression Using Weighted Finite Automata." IJACI vol.9, no.1 2018: pp.115-133. http://doi.org/10.4018/IJACI.2018010107

APA

Kamble, S. D., Thakur, N. V., & Bajaj, P. R. (2018). Fractal Coding Based Video Compression Using Weighted Finite Automata. International Journal of Ambient Computing and Intelligence (IJACI), 9(1), 115-133. http://doi.org/10.4018/IJACI.2018010107

Chicago

Kamble, Shailesh D., Nileshsingh V. Thakur, and Preeti R. Bajaj. "Fractal Coding Based Video Compression Using Weighted Finite Automata," International Journal of Ambient Computing and Intelligence (IJACI) 9, no.1: 115-133. http://doi.org/10.4018/IJACI.2018010107

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Main objective of the proposed work is to develop an approach for video coding based on Fractal coding using the weighted finite automata (WFA). The proposed work only focuses on reducing the encoding time as this is the basic limitation why the Fractal coding not becomes the practical reality. WFA is used for the coding as it behaves like the Fractal Coding (FC). WFA represents an image based on the idea of fractal that the image has self-similarity in itself. The plane WFA (applied on every frame), and Plane FC (applied on every frame) coding approaches are compared with each other. The experimentations are carried out on the standard uncompressed video databases, namely, Traffic, Paris, Bus, Akiyo, Mobile, Suzie etc. and on the recorded video, namely, Geometry and Circle. Developed approaches are compared on the basis of performance evaluation parameters, namely, encoding time, decoding time, compression ratio, compression percentage, bits per pixel and Peak Signal to Noise Ratio (PSNR). Though the initial number of states is 256 for every frame of all the types of videos, but we got the different number of states for different frames and it is quite obvious due to minimality of constructed WFA for respective frame. Based on the obtained results, it is observed that the number of states is more in videos namely, Traffic, Bus, Paris, Mobile, and Akiyo, therefore the reconstructed video quality is good in comparison with other videos namely, Circle, Suzie, and Geometry.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.