Comparative Performance Analysis of Interpolative Decomposition and Singular Value Decomposition for Image Reduction

Authors

  • Wei Shean Ng Department of Mathematical and Actuarial Sciences, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Jalan Sungai Long, Bandar Sungai Long, Cheras 43000, Kajang, Selangor, MALAYSIA. https://orcid.org/0000-0003-1270-2635
  • How Hui Liew Department of Mathematical and Actuarial Sciences, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Jalan Sungai Long, Bandar Sungai Long, Cheras 43000, Kajang, Selangor, MALAYSIA. https://orcid.org/0000-0002-3432-8317
  • Huey Voon Chen Department of Mathematical and Actuarial Sciences, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Jalan Sungai Long, Bandar Sungai Long, Cheras 43000, Kajang, Selangor, MALAYSIA. https://orcid.org/0000-0003-3873-2242
  • Hao Tian Ng Department of Mathematical and Actuarial Sciences, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Jalan Sungai Long, Bandar Sungai Long, Cheras 43000, Kajang, Selangor, MALAYSIA. https://orcid.org/0009-0003-5627-2486

DOI:

https://doi.org/10.22452/

Keywords:

Interpolative decomposition, singular value decomposition, image reduction

Abstract

Matrix decomposition techniques, such as Singular Value Decomposition (SVD), have traditionally been used for image compression, achieving data reduction while preserving image fidelity. Recently, randomised linear algebra algorithms, including Interpolative Decomposition (ID), have gained attention due to their efficiency in computational expense and memory consumption. This study presents a comparative analysis of ID and randomised SVD for image compression using a large collection of images from the USC-SIPI Image Database. Experimental results demonstrate that both methods effectively reduce image sizes, with SVD yielding greater compression ratios, while ID better preserves image quality at lower computational costs. The analysis also demonstrated that both techniques faced limitations when compressing images with dark or low-contrast areas, with ID performing best on images exhibiting repetitive or structured patterns. These findings indicate that ID is more suitable for memory-limited applications, such as reducing large tabular data, which is important in marketing data analysis, whereas SVD is preferable for image compression.

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Published

30-06-2026

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Vol 45 No 2 (2026): June 2026

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Original Articles

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