Different Types of File Format Image Compression using Transform Domain Up-Down Conversion

Author(s):  
Sunil Kumar Mishra ◽  
Shivendra Singh ◽  
Rajesh Nema
2014 ◽  
Vol 518 ◽  
pp. 320-323
Author(s):  
Kun Geng

Gibbs effect appears inevitable in the decoded image can result in filter DFB. In this paper, through in-depth study of the nature of WBCT transform domain analysis of the causes of WBCT Gibbs effect, transformed itself in the parameter selection gives a solution, and in the transform domain creatively put forward the direction of the weighted model, in a WBCT The best way in JPEG2000 coding framework.


Author(s):  
Hajar Maseeh Yasin ◽  
Adnan Mohsin Abdulazeez

Image compression is an essential technology for encoding and improving various forms of images in the digital era. The inventors have extended the principle of deep learning to the different states of neural networks as one of the most exciting machine learning methods to show that it is the most versatile way to analyze, classify, and compress images. Many neural networks are required for image compressions, such as deep neural networks, artificial neural networks, recurrent neural networks, and convolution neural networks. Therefore, this review paper discussed how to apply the rule of deep learning to various neural networks to obtain better compression in the image with high accuracy and minimize loss and superior visibility of the image. Therefore, deep learning and its application to different types of images in a justified manner with distinct analysis to obtain these things need deep learning.


Author(s):  
Amir Athar Khan ◽  
Amanat Ali ◽  
Sanawar Alam ◽  
N. R. Kidwai

This paper concerns Image compression obtained with wavelet-based compression techniques such as set–partitioning in hierarchical trees (SPIHT)yield very good results The necessity in image compression continuously grows during the last decade, different types of methods is used for this mainly EZW, SPIHT and others. In this paper we used discrete wavelet transform and after this set-partitioning in hierarchical trees (SPIHT) with some improvement in respect of encoding and decoding time with better PSNR with respect to EZW coding.


Micromachines ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 505
Author(s):  
Adar Hacohen ◽  
Hadass R. Jessel ◽  
Alon Richter-Levin ◽  
Orit Shefi

The ability to manipulate and selectively position cells into patterns or distinct microenvironments is an important component of many single cell experimental methods and biological engineering applications. Although a variety of particles and cell patterning methods have been demonstrated, most of them deal with the patterning of cell populations, and are either not suitable or difficult to implement for the patterning of single cells. Here, we describe a bottom-up strategy for the micropatterning of cells and cell-sized particles. We have configured a micromanipulator system, in which a pneumatic microinjector is coupled to a holding pipette capable of physically isolating single particles and cells from different types, and positioning them with high accuracy in a predefined position, with a resolution smaller than 10 µm. Complementary DNA sequences were used to stabilize and hold the patterns together. The system is accurate, flexible, and easy-to-use, and can be automated for larger-scale tasks. Importantly, it maintains the viability of live cells. We provide quantitative measurements of the process and offer a file format for such assemblies.


Sign in / Sign up

Export Citation Format

Share Document