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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 coding scheme (discrete cosine transform or bi-orthogonal transform). Experimentally, the developed system has achieved high compression ratios with acceptable quality for color images. The performance results are comparable to those introduced in recent studies; the accomplishment of the introduced image compression system was analyzed and compared with the performance of the JPEG standard. The results of the developed system show better performance than that of the JPEG standard.

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Publication Date
Mon Mar 20 2023
Journal Name
2023 International Conference On Information Technology, Applied Mathematics And Statistics (icitams)
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Publication Date
Thu Jun 01 2017
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
Fri Apr 01 2016
Journal Name
Iosr Journal Of Computer Engineering
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Publication Date
Tue Aug 10 2021
Journal Name
Design Engineering
Lossy Image Compression Using Hybrid Deep Learning Autoencoder Based On kmean Clusteri

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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Publication Date
Mon Jun 01 2015
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
Thu Jan 01 2015
Journal Name
International Journal Of Computer Science And Mobile Computing
Publication Date
Wed Jul 06 2022
Journal Name
Journal Of Al-qadisiyah For Computer Science And Mathematics
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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