Image Signal Decomposition Using Polynomial Representation with Hybrid Lossy and Non-Lossy Coding Scheme


  • Zainab J. Ahmed Department of Biology Science, College of Science, University of Baghdad, Baghdad, Iraq
  • Loay E. George University of Information Technology and Communications, Baghdad, Iraq
  • Raad Ahmed Hadi Al-Iraqia University, Baghdad, Iraq



Polynomial Representation, Image Compression, Discrete Cosine Transform, Run Length Coding, Quantization


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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How to Cite

Ahmed, Z. J., George, L. E., & Hadi, R. A. (2022). Image Signal Decomposition Using Polynomial Representation with Hybrid Lossy and Non-Lossy Coding Scheme. Iraqi Journal of Science, 63(10), 4559–4575.



Computer Science

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