Hierarchal Polynomial Coding of Grayscale Lossless Image Compression
Publication Date
Thu Jan 01 2015
Journal Name
International Journal Of Computer Science And Mobile Computing
Publication Date
Fri Apr 01 2016
Journal Name
Iosr Journal Of Computer Engineering
(1)
Publication Date
Fri Apr 01 2016
Journal Name
Iosr Journal Of Computer Engineering
(1)
Publication Date
Wed Jul 25 2018
Journal Name
International Journal Of Engineering Trends And Technology
(1)
Publication Date
Wed Jun 01 2022
Journal Name
V. International Scientific Congress Of Pure, Applied And Technological Sciences
Publication Date
Wed Jul 06 2022
Journal Name
Journal Of Al-qadisiyah For Computer Science And Mathematics
Publication Date
Wed Nov 27 2019
Journal Name
Iraqi Journal Of Science
Fourier Transform Coding-based Techniques for Lossless Iris Image Compression
decoded image
zipper
Today, the use of iris recognition is expanding globally as the most accurate and reliable biometric feature in terms of uniqueness and robustness. The motivation for the reduction or compression of the large databases of iris images becomes an urgent requirement. In general, image compression is the process to remove the insignificant or redundant information from the image details, that implicitly makes efficient use of redundancy embedded within the image itself. In addition, it may exploit human vision or perception limitations to reduce the imperceptible information.
This paper deals with reducing the size of image, namely reducing the number of bits required in representing the
(2)
Publication Date
Sun May 01 2016
Journal Name
International Journal Of Computer Applications
Publication Date
Fri May 16 2014
Journal Name
International Journal Of Computer Applications
(4)
Publication Date
Fri Jun 24 2022
Journal Name
Iraqi Journal Of Science
Image Compression based on Adaptive Polynomial Coding of Hard & Soft Thresholding
Image compression
polynomial coding and hard and soft thresholding
In this paper, an adaptive polynomial compression technique is introduced of hard and soft thresholding of transformed residual image that efficiently exploited both the spatial and frequency domains, where the technique starts by applying the polynomial coding in the spatial domain and then followed by the frequency domain of discrete wavelet transform (DWT) that utilized to decompose the residual image of hard and soft thresholding base. The results showed the improvement of adaptive techniques compared to the traditional polynomial coding technique.