Image Compression with SVD : A New Quality Metric Based On Energy Ratio

Razafindradina, Henri Bruno and Randriamitantsoa, Paul Auguste and Razafindrakoto, Nicolas Raft Image Compression with SVD : A New Quality Metric Based On Energy Ratio. International Journal of Computer Science and Network - IJCSN, 2016, vol. 5, n. 6, pp. 960-965. [Journal article (Paginated)]


Download (302kB) | Preview

English abstract

Digital image compression is a technique that allows to reduce the size of an image in order to increase the capacity storage devices and to optimize the use of network bandwidth. The quality of compressed images with the techniques based on the discrete cosine transform or the wavelet transform is generally measured with PSNR or SSIM. Theses metrics are not suitable to images compressed with the singular values decomposition. This paper presents a new metric based on the energy ratio to measure the quality of the images coded with the SVD. A series of tests on 512 × 512 pixels images show that, for a rank k = 40 corresponding to a SSIM = 0,94 or PSNR = 35 dB, 99,9% of the energy are restored. Three areas of image quality assessments were identified. This new metric is also very accurate and could overcome the weaknesses of PSNR and SSIM.

Item type: Journal article (Paginated)
Keywords: Metric, Assessment, SVD, Singular Value, Image, Compression, PSNR, SSIM
Subjects: I. Information treatment for information services > IH. Image systems.
Depositing user: IJCSN Journal
Date deposited: 26 Jan 2017 12:21
Last modified: 26 Jan 2017 12:21


"SEEK" links will first look for possible matches inside E-LIS and query Google Scholar if no results are found.

[1] A. B. Watson, « Image Compression Using the

Discrete Cosine Transform », Mathematica Journal,

1994, pp. 81-88.

[2] S. C. Meadows, « Color image compression using

wavelet transform », Thesis in Electrical Engineering,

1997, pp. 1-86.

[3] J. Chen, « Image compression with SVD », ECS 289K

Scientific Computation, 2000, pp. 13

[4] A. Abrahamsen and D. Richards, « Image

Compression using Singular Value Decomposition »,

Linear algebra applications, 2001, pp. 1-14.

[5] S. Kahu, R. Rahate, « Image Compression using

Singular Value Decomposition », International Journal

of Advancements in Research & Technology, 2013,

Volume 2, Issue 8, pp. 244-248.

[6] A. R. Sadek, « SVD Based Images Processing

Applications : State of the Art, Contributions and

Research Challenges », International Journal of

Advanced Computer Science and Applications, 2012,

Volume 3, No. 7, pp. 26-34.

[7] Z. Wang, A. C. Bovik, H. R. Sheikh and E. P.

Simoncelli, « Image quality assessment :From error

visibility to structural similarity », IEEE Transactions

on Image Processing, 2004, Volume 13, no. 4, pp.


[8] J. SairaBanu, B. Rajasekhara and R. Pandey,

« Parallel Implementation of Singular Value

Decomposition in Image Compression Using Open Mp

and Sparse Matrix Representation », Indian Journal

of Science and Technology, 2015, Volume 8 (13), pp.


[9] N. K. El Abbadi and Al, « Image Compression based

on SVD and MPQ-BTC », Journal of Computer

Science, 2014, Volume 10, pp. 2095-2104.


Downloads per month over past year

Actions (login required)

View Item View Item