Image compression using self-organization networks

1994 ◽  
Vol 4 (5) ◽  
pp. 480-489 ◽  
Author(s):  
O.T.-C. Chen ◽  
B.J. Sheu ◽  
W.-C. Fang
Keyword(s):  
Image Compression ◽  
Self Organization ◽  
Organization Networks

Applications of self-organization networks spatially isomorphic to patterns

1999 ◽  
Vol 114 (1-4) ◽  
pp. 23-39 ◽  
Author(s):  
K. Shanmukh ◽  
C.N.S. Ganesh Murthy ◽  
Y.V. Venkatesh
Keyword(s):  
Self Organization ◽  
Organization Networks

scholarly journals A System Framework for LTE SON

2012 ◽  
Vol 4 ◽  
pp. 22-26
Author(s):  
Zhan Jun Liu ◽  
Cong Ren ◽  
Cheng Chao Liang
Keyword(s):  
Reference Point ◽  
Self Organization ◽  
Human Intervention ◽  
System Framework ◽  
Organization Networks

To meet the requirements of reducing human intervention and operational expenditures (POEX) of LTE SON (Self-organization Networks), a system framework for LTE SON is designed in this paper. In order to match the standard IRP (Integration Reference Point), the framework is divided into six layers, and then functions of some layers are presented.

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.

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