Fast Fractal Coding Algorithm Based on Twice Pre-searching

2011 ◽  
Author(s):  
Ming Zhao ◽  
Ling Bai ◽  
Hui Tang ◽  
Shaofa Zhou
Keyword(s):  
Fractal Coding

Error-control-based fractal coding algorithm

2001 ◽  
Author(s):  
Ping Fu ◽  
Shigang Wang ◽  
Su Yan
Keyword(s):  
Error Control ◽  
Fractal Coding

A Fast Face Recognition Method Based on Fractal Coding

2017 ◽  
Vol 11 (7) ◽  
pp. 1221-1228 ◽  
Author(s):  
Zhijie Tang ◽  
Xiaocheng Wu ◽  
Xinnan Leng ◽  
Weiwei Chen
Keyword(s):  
Face Recognition ◽  
Recognition Method ◽  
Fractal Coding

Resolution enhancement of images using fractal coding

1997 ◽  
Author(s):  
Mohammad Gharavi-Alkhansari ◽  
Robert DeNardo ◽  
Yoichi Tenda ◽  
Thomas S. Huang
Keyword(s):  
Resolution Enhancement ◽  
Fractal Coding

Punched Image Watermarking: A Novel Fast Fractal Coding Based Technique

2007 ◽  
Author(s):  
Said E. El-Khamy ◽  
Mohamed Khedr ◽  
Ahmad Al-Kabbany
Keyword(s):  
Image Watermarking ◽  
Fractal Coding

Fractal Coding Based Video Compression Using Weighted Finite Automata

2018 ◽  
Vol 9 (1) ◽  
pp. 115-133 ◽  
Author(s):  
Shailesh D. Kamble ◽  
Nileshsingh V. Thakur ◽  
Preeti R. Bajaj
Keyword(s):  
Video Compression ◽  
Signal To Noise Ratio ◽  
Finite Automata ◽  
Video Quality ◽  
Self Similarity ◽  
Fractal Coding ◽  
Time Compression ◽  
Practical Reality ◽  
Weighted Finite Automata ◽  
Evaluation Parameters

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.

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