A Hybrid Lossy Image Compression based on Wavelet Transform, Polynomial Approximation Model, Bit Plane Slicing and Absolute Moment Block Truncation
Publication Date
Thu Jun 01 2017
Journal Name
Iosr Journal Of Computer Engineering
Publication Date
Sun Jun 01 2014
Journal Name
International Journal Of Advanced Research In Computer Science And Software Engineering
Publication Date
Fri May 16 2014
Journal Name
International Journal Of Computer Applications
(4)
Publication Date
Fri Jul 01 2016
Journal Name
International Journal Of Computer Science And Mobile Computing
Publication Date
Sat Nov 05 2016
Journal Name
Research Journal Of Applied Sciences, Engineering And Technology
Image Compression Based on Cubic Bezier Interpolation, Wavelet Transform, Polynomial Approximation, Quadtree Coding and High Order Shift Encoding
Biorthogonal transform
cubic Bezier interpolation
polynomial approximation and shift coding
In this study, an efficient compression system is introduced, it is based on using wavelet transform and two types of 3Dimension (3D) surface representations (i.e., Cubic Bezier Interpolation (CBI)) and 1 st order polynomial approximation. Each one is applied on different scales of the image; CBI is applied on the wide area of the image in order to prune the image components that show large scale variation, while the 1 st order polynomial is applied on the small area of residue component (i.e., after subtracting the cubic Bezier from the image) in order to prune the local smoothing components and getting better compression gain. Then, the produced cubic Bezier surface is subtracted from the image signal to get the residue component. Then, t
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(2)
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
Sat Oct 01 2011
Journal Name
Journal Of Engineering
IMPROVED IMAGE COMPRESSION BASED WAVELET TRANSFORM AND THRESHOLD ENTROPY
In this paper, a method is proposed to increase the compression ratio for the color images by
dividing the image into non-overlapping blocks and applying different compression ratio for these
blocks depending on the importance information of the block. In the region that contain important
information the compression ratio is reduced to prevent loss of the information, while in the
smoothness region which has not important information, high compression ratio is used .The
proposed method shows better results when compared with classical methods(wavelet and DCT).
Publication Date
Sat Aug 01 2015
Journal Name
International Journal Of Computer Science And Mobile Computing
Publication Date
Tue Aug 10 2021
Journal Name
Design Engineering
Lossy Image Compression Using Hybrid Deep Learning Autoencoder Based On kmean Clusteri
Image compression
Convolutional Autoencoder (CAE)
k-mean algorithm
PSNR
Compression Rate (CR)
MSE
Clustering
CLIC
Kodak
deep learning
lossy
Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye
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