Showing posts with label Wavelet. Show all posts
Showing posts with label Wavelet. Show all posts

Monday, 9 May 2022

Towards Wavelet Based Video Codec | Book Publisher International

 Since 1980, new video codecs have been created that produce low data rates while preserving the integrity of raw visual data. Videos are typically kept on general-purpose computers and a variety of portable devices. The amount of capacity required to store raw video data in those devices is enormous. As a result, video compression is a fascinating and important issue. There are two types of compression methods: lossy and lossless. Lossy compression is a method of increasing compression efficiency by losing image and video quality. The preservation of the original data is more important than compression performance in some applications; this is where lossless compression comes in. As a result, this book concentrates on lossy compression. Furthermore, motion estimation and compensation are thought to be important.


Author(s) Details

S. Sowmyayani
St. Mary’s College, India.

P. Arockia Jansi Rani
Manonmaniam Sundaranar University, India.

View Book:- https://stm.bookpi.org/TWBVC/article/view/6677

Tuesday, 8 February 2022

Model Development of Analogue Wavelet Transform Based the Predicted Imaginary Part of the Dynamics of Rational Map Having Zeros | Chapter 02 | Recent Advances in Mathematical Research and Computer Science Vol. 7

Quadratic polynomial dynamics have already attracted a lot of attention. The kinetics and attributes of rational functions are both fascinating. In this chapter, we look at Witten's [1] solution to the classical Yang-Mills problem and use a C++ computer programme to generate computer graphics. We next develop an artificial neural network model based on RMS kind of error from two samples of points extracted from the created images using predictive modelling software. The imaginary component of sample II might be predicted by feeding the real sections of sample I and sample II [2] to the artificial neural network. The real part of sample II is more essential than the actual part of sample I in anticipating the imaginary component of sample II. The projected imaginary component of Sample II is then loaded via Matlab workspace into Matlab Signal Processing Tool (SPTool). To remove noise from the model for analysis, we use a reliable band pass filter. The methodology can be used to explore the attributes of computer generated images formed from the generated wavelet by producing a modulated signal. The projected imaginary component of sample II is then imported into autoSIGNAL software for continuous wavelet transform time-frequency analysis. The goal of this research is to close the gap between the dynamics of rational maps and the dynamics of electric power systems [3,4].


Author(S) Details

Jean-Bosco Mugiraneza
African Centre of Excellence in Energy for Sustainable Development (ACEESD) , University of Rwanda (UR), Kigali, Rwanda.

View Book:- https://stm.bookpi.org/RAMRCS-V7/article/view/5487