Araştırma Makalesi

Lossy Image Compression Using Karhunen-Loeve Transform Based Methods

Cilt: 9 Sayı: 2 31 Mayıs 2022
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Lossy Image Compression Using Karhunen-Loeve Transform Based Methods

Öz

In this paper, we discuss image compression techniques based on the eigenvector matrices used the Karhunen-Loeve Transform (KLT) is obtained. Two novel methods are proposed for the grouping of eigenvectors via vector quantization in the KLT subspace. Various codebook sizes are tested for image compression purposes. The first grouping approach uses eigenvectors of autocorrelation matrices for geometrically clustering into fewer numbers of vectors. In this approach, the quantization is performed using principal component directions of the eigenvector matrices. The second approach has used the eigenvectors according to their usage frequencies. The qualities of reconstructed test images are compared with DCT based JPEG and Wavelet Transform based JPEG2000 compression methods using the PSNR metric. Experimental results show that the proposed methods, particularly the second method, give plausible and competitive results.

Anahtar Kelimeler

Kaynakça

  1. [1]. Anil Kumar Katharotiya, Swati Patel, Mahesh Goyani, “Comparative Analysis between DCT & DWT Techniques of Image Compression”. Journal of Information Engineering and Applications, Vol. 1, No. 2, 2011.
  2. [2]. CLARKE, Roger John. Transform coding of images. Astrophysics, 1985.
  3. [3]. Ahmed, N., Natarajan, T., Rao, K. R., 1974. Discrete cosine transform. IEEE transactions on Computers. 100(1), 90-93.
  4. [4]. Roy A.B., Dey D., Mohanty B. and Banerjee D. Comparison of FFT, DCT, DWT, WHT compression techniques on electro cardiogram and photo plethysmography signals. Int. J. Comp. Appl. 2012; 975-888.
  5. [5]. W. B. Pennebaker and J. L. Mitchell, “JPEG –Still Image Data Compression Standard,”Newyork: International Thomsan Publishing, 1993.
  6. [6]. Singh H, Sharma S. Hybrid image compression using DWT, DCT and Huffman encoding techniques. Int J Emerg Technol Adv Eng 2012; 2: 300-306.
  7. [7]. Goyal, V. K., 2001. Theoretical foundations of transform coding. IEEE Signal Processing Magazine. 18(5), 9-21.
  8. [8]. A. Oliva and A. Torralba, (2001) "Modeling the shape of the scene: a holistic representation of the spatial envelope," International Journal of Computer Vision, vol. 42, no. 3, pp. 145-175.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Mühendislik

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

31 Mayıs 2022

Gönderilme Tarihi

12 Haziran 2021

Kabul Tarihi

10 Ocak 2022

Yayımlandığı Sayı

Yıl 2022 Cilt: 9 Sayı: 2

Kaynak Göster

APA
Keser, S. (2022). Lossy Image Compression Using Karhunen-Loeve Transform Based Methods. El-Cezeri, 9(2), 424-435. https://doi.org/10.31202/ecjse.951417
AMA
1.Keser S. Lossy Image Compression Using Karhunen-Loeve Transform Based Methods. ECJSE. 2022;9(2):424-435. doi:10.31202/ecjse.951417
Chicago
Keser, Serkan. 2022. “Lossy Image Compression Using Karhunen-Loeve Transform Based Methods”. El-Cezeri 9 (2): 424-35. https://doi.org/10.31202/ecjse.951417.
EndNote
Keser S (01 Mayıs 2022) Lossy Image Compression Using Karhunen-Loeve Transform Based Methods. El-Cezeri 9 2 424–435.
IEEE
[1]S. Keser, “Lossy Image Compression Using Karhunen-Loeve Transform Based Methods”, ECJSE, c. 9, sy 2, ss. 424–435, May. 2022, doi: 10.31202/ecjse.951417.
ISNAD
Keser, Serkan. “Lossy Image Compression Using Karhunen-Loeve Transform Based Methods”. El-Cezeri 9/2 (01 Mayıs 2022): 424-435. https://doi.org/10.31202/ecjse.951417.
JAMA
1.Keser S. Lossy Image Compression Using Karhunen-Loeve Transform Based Methods. ECJSE. 2022;9:424–435.
MLA
Keser, Serkan. “Lossy Image Compression Using Karhunen-Loeve Transform Based Methods”. El-Cezeri, c. 9, sy 2, Mayıs 2022, ss. 424-35, doi:10.31202/ecjse.951417.
Vancouver
1.Serkan Keser. Lossy Image Compression Using Karhunen-Loeve Transform Based Methods. ECJSE. 01 Mayıs 2022;9(2):424-35. doi:10.31202/ecjse.951417