Notice Board: Call for Paper Vol. 8 Issue 10      Submission Start Date: September 30, 2021      Acceptence Notification Start: October 12, 2021      Submission End: October 18, 2021      Final MenuScript Due: October 22, 2021      Publication Date: October 31, 2021






Volume 4 Issue 12

Author Name
Anjna Singh, Mayank Bhargava, Jitendra Kumar Mishra
Year Of Publication
2017
Volume and Issue
Volume 4 Issue 12
Abstract
The advancement of digital technology needs fast processing of data. the processing speed of data depends on the size, the big size of data required more space and bandwidth for the processing of data. the efficient process of image compression reduces the size of data and increase the speed of the processing of data. In this paper proposed efficient image compression algorithm based on discrete wavelet transform and particle swarm optimization. The discrete wavelet transforms used for the processing of data decomposition and particle swarm optimization used for the process of searching and scanning of decomposed block. The nature of particle swarm optimization is dual and diverse, so the processing of compression is fast instead of DWT and DCT transform function. The proposed algorithm gives the better PSNR and compression ratio of DWT and DCT transform function
PaperID
2017/12/IJMERT/12/312

Author Name
Swati Sarawgi , Surendra Vishwakarma
Year Of Publication
2017
Volume and Issue
Volume 4 Issue 12
Abstract
Image processing plays an important role in computer vision. The process of image segmentation provides the partition of image into different segments according to their feature attribute. Region based segmentation is a type similarity based segmentation. Another type of segmentation is called thresholding based segmentation. In thresholding based segmentation method some thresholding techniques are used. Thresholding techniques are classified into two major categories as, Global and Local. In global thresholding, pixel values are categorized in two classes, one class belong to object and another class belong to background. We use one threshold value in global thresholding for whole image that belongs to single level thresholding and if threshold value used in segmentation is more than one, technique is called multilevel thresholding. Local thresholding belongs to multilevel thresholding method. In this paper a comparative analysis of global thresholding and local thresholding methods
PaperID
2017/12/IJMERT/12/313



Notice Board :

Call for Paper
Vol. 8 Issue 10

Submission Start Date:
September 30, 2021

Acceptence Notification Start:
October 12, 2021

Submission End:
October 18, 2021

Final MenuScript Due:
October 22, 2021

Publication Date:
October 31, 2021