Wavelet-Based Image Compression using Mathematical Morphology and Self Organizing Feature Map

2006 ◽  
Author(s):  
A.A. Mohammed ◽  
J. Alirezaie
Keyword(s):  
Image Compression ◽  
Mathematical Morphology ◽  
Feature Map ◽  
Self Organizing

scholarly journals Wavelet-based image compression using mathematical morphology and self organization feature map

2021 ◽  
Author(s):  
Abdul Adeel Mohammed
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Mathematical Morphology ◽  
Self Organization ◽  
Jpeg 2000 ◽  
Compression System ◽  
Feature Map ◽  
Domain Information ◽  
Selection Of ◽  
Self Organizing

Image compression using transform coding technique has been widely used in practice. However, wavelet transform is the only method that provides both spatial and frequency domain information. These properties of wavelet transform greatly help in identification and selection of significant and non-significant coefficients from amongst the wavelet coefficients. Wavelet transform based image compression result in an improved compression ratio as well as image quality and thus both the signficant coefficients and their positions within an image are encoded and transmitted. In this thesis a wavelet based image compression system is presented that uses mathematical morphology and self organizing feature map (MMSOFM). The significance map is preprocessed using mathematical morphology operators to identify and creat clusters of significant coefficients. A self-organizing feature map (SOFM) is then used to encode the significance map. Experimental results are shown and comparisons with JPEG and JPEG 2000 are made to emphasize the results of this compression system.

scholarly journals Wavelet-based image compression using mathematical morphology and self organization feature map

2021 ◽  
Author(s):  
Abdul Adeel Mohammed
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Mathematical Morphology ◽  
Self Organization ◽  
Jpeg 2000 ◽  
Compression System ◽  
Feature Map ◽  
Domain Information ◽  
Selection Of ◽  
Self Organizing

Image compression using transform coding technique has been widely used in practice. However, wavelet transform is the only method that provides both spatial and frequency domain information. These properties of wavelet transform greatly help in identification and selection of significant and non-significant coefficients from amongst the wavelet coefficients. Wavelet transform based image compression result in an improved compression ratio as well as image quality and thus both the signficant coefficients and their positions within an image are encoded and transmitted. In this thesis a wavelet based image compression system is presented that uses mathematical morphology and self organizing feature map (MMSOFM). The significance map is preprocessed using mathematical morphology operators to identify and creat clusters of significant coefficients. A self-organizing feature map (SOFM) is then used to encode the significance map. Experimental results are shown and comparisons with JPEG and JPEG 2000 are made to emphasize the results of this compression system.

Sign in / Sign up

Export Citation Format

Share Document