Hybrid Color Image Compression Using Signals Decomposition with Lossy and Lossless Coding Schemes
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
Fri Nov 01 2019
Journal Name
International Journal Of Computer Science And Mobile Computing
Publication Date
Sat Jun 01 2019
Journal Name
International Journal Of Computer Science And Mobile Computing
Publication Date
Thu Jun 01 2017
Journal Name
Iosr Journal Of Computer Engineering
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 Oct 01 2021
Journal Name
International Journal Of Electrical And Computer Engineering (ijece)
Color image compression based on spatial and magnitude signal decomposition
<p>In this paper, a simple color image compression system has been proposed using image signal decomposition. Where, the RGB image color band is converted to the less correlated YUV color model and the pixel value (magnitude) in each band is decomposed into 2-values; most and least significant. According to the importance of the most significant value (MSV) that influenced by any simply modification happened, an adaptive lossless image compression system is proposed using bit plane (BP) slicing, delta pulse code modulation (Delta PCM), adaptive quadtree (QT) partitioning followed by an adaptive shift encoder. On the other hand, a lossy compression system is introduced to handle the least significant value (LSV), it is based on
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(6)
(1)
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
... Show MorePublication Date
Mon Feb 04 2019
Journal Name
Journal Of The College Of Education For Women
Astronomi cal Color Image Compression Using Multilevel Block Truncation Coding –Modified Vector Quantization Technique
A common approach to the color image compression was started by transform
the red, green, and blue or (RGB) color model to a desire color model, then applying
compression techniques, and finally retransform the results into RGB model In this
paper, a new color image compression method based on multilevel block truncation
coding (MBTC) and vector quantization is presented. By exploiting human visual
system response for color, bit allocation process is implemented to distribute the bits
for encoding in more effective away.
To improve the performance efficiency of vector quantization (VQ),
modifications have been implemented. To combines the simple computational and
edge preservation properties of MBTC with high c