Autor/Autorin: Khalsa Nikkoo
Dr. Nikkoo Khalsa is an Assistant Professor at Prof. Ram Meghe Institute of Technology & Research, Badnera- Amravati. He received his Ph.D. from SGBAU, Amravati in 2015. His research interest is in Image Classification and Compression.
The present age of information explosion is envisaging incredible development in both communication medium and hardware escalation. This in turn, is engendering a huge volume of digital signals in the form of images, videos, audio and texts, which proves to be challenging in terms of storage and broadcast. Even though, several breakthroughs in the price and performance of digital hardware and firmware have been put into practice, the demand for high data storage capacity and data-transmission bandwidth continues to outstrip the capabilities of available technologies. This research work proposes a novel method of classifying images into two categories Artificial and Natural. Then investigates and compares numerous wavelet based image compression techniques such as Haar, Daubenchies, Coieflet, Symlet, Biorthogonal and Discrete Meyer wavelet. From the results, it is concluded that classification of an image on the basis of various parameters as Artificial and Natural and selection of mother wavelet can provide a better reference for application developers to choose an optimal wavelet compression system for relevant application.
Über den Autor
Dr. Nikkoo Khalsa is an Assistant Professor at Prof. Ram Meghe Institute of Technology & Research, Badnera- Amravati. He received his Ph.D. from SGBAU, Amravati in 2015. His research interest is in Image Classification and Compression.
Klappentext
The present age of information explosion is envisaging incredible development in both communication medium and hardware escalation. This in turn, is engendering a huge volume of digital signals in the form of images, videos, audio and texts, which proves to be challenging in terms of storage and broadcast. Even though, several breakthroughs in the price and performance of digital hardware and firmware have been put into practice, the demand for high data storage capacity and data-transmission bandwidth continues to outstrip the capabilities of available technologies. This research work proposes a novel method of classifying images into two categories Artificial and Natural. Then investigates and compares numerous wavelet based image compression techniques such as Haar, Daubenchies, Coieflet, Symlet, Biorthogonal and Discrete Meyer wavelet. From the results, it is concluded that classification of an image on the basis of various parameters as Artificial and Natural and selection of mother wavelet can provide a better reference for application developers to choose an optimal wavelet compression system for relevant application.
