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Lossy Image Compression Using Wavelet Transform, Polynomial Prediction And Block Truncation Coding

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Publication Date
Mon Jun 01 2015
Journal Name
. International Journal Of Computer Science And Mobile Computing
Publication Date
Fri Jul 01 2016
Journal Name
International Journal Of Computer Science And Mobile Computing
Publication Date
Wed Jun 01 2022
Journal Name
V. International Scientific Congress Of Pure, Applied And Technological Sciences
Publication Date
Sun Jun 01 2014
Journal Name
International Journal Of Advanced Research In Computer Science And Software Engineering
Publication 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

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Publication Date
Fri Apr 01 2016
Journal Name
Iosr Journal Of Computer Engineering
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Publication Date
Fri Apr 01 2016
Journal Name
Iosr Journal Of Computer Engineering
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Publication Date
Sun Jun 12 2011
Journal Name
Baghdad Science Journal
Image Compression Using Tap 9/7 Wavelet Transform and Quadtree Coding Scheme

This paper is concerned with the design and implementation of an image compression method based on biorthogonal tap-9/7 discrete wavelet transform (DWT) and quadtree coding method. As a first step the color correlation is handled using YUV color representation instead of RGB. Then, the chromatic sub-bands are downsampled, and the data of each color band is transformed using wavelet transform. The produced wavelet sub-bands are quantized using hierarchal scalar quantization method. The detail quantized coefficient is coded using quadtree coding followed by Lempel-Ziv-Welch (LZW) encoding. While the approximation coefficients are coded using delta coding followed by LZW encoding. The test results indicated that the compression results are com

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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

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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Publication Date
Sun Oct 30 2022
Journal Name
Iraqi Journal Of Science
Image Signal Decomposition Using Polynomial Representation with Hybrid Lossy and Non-Lossy Coding Scheme

This article presents a polynomial-based image compression scheme, which consists of using the color model (YUV) to represent color contents and using two-dimensional polynomial coding (first-order) with variable block size according to correlation between neighbor pixels. The residual part of the polynomial for all bands is analyzed into two parts, most important (big) part, and least important (small) parts. Due to the significant subjective importance of the big group; lossless compression (based on Run-Length spatial coding) is used to represent it. Furthermore, a lossy compression system scheme is utilized to approximately represent the small group; it is based on an error-limited adaptive coding system and using the transform codin

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