Araştırma Makalesi
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Görüntü Sıkıştırmada Dalgacık Ailelerinin Karşılaştırmalı Analizi ve Yeni bir Dalgacık Ailesi Önerisi

Yıl 2024, Cilt: 19 Sayı: 1, 279 - 294, 28.03.2024
https://doi.org/10.55525/tjst.1428424

Öz

Görüntü sıkıştırma, tıbbi görüntülerden uydu görüntülerine ve günlük fotoğrafçılığa kadar dijital medyanın verimli ve maliyet etkili kullanımı için temel bir gerekliliktir. Dalgacık dönüşümü, görüntü sıkıştırmada kullanılan en iyi yöntemlerden biridir. Bu araştırma, en çok bilinen dalgacık ailelerinin görüntü sıkıştırma performansını çeşitli analizlerle değerlendirmiştir. İlave olarak nwi adlı yeni bir dalgacık ailesi üretilmiş ve performansı bilinen dalgacık aileleri ile karşılaştırılmıştır. Sıkıştırma Oranı (CR) ve Tepe Sinyal-Gürültü Oranı (PSNR) gibi nicel ölçüleri kullanarak, tablolar ve şekillerde sunulan sonuçlar, farklı dalgacık dönüşümlerinin performansını göstermektedir. Sonuçlar, tüm test görüntüleri için ortalama %75 sıkıştırma oranının 38 dB PSNR değeri ile elde edilebileceğini göstermektedir. En iyi sonuç, önerilen NWI dalgacığı ile test-2 görüntüsünde sıkıştırma performansı (CP) 3312,08 değeri ile elde edilmiştir. Bu çalışmada, sekiz dalgacık ailesi değerlendirilmekte ve görüntü sıkıştırma performansının hem görüntü türüne hem de seçilen dalgacık ailesine bağlı olduğu sonucu çıkmaktadır. Kodlama algoritması tüm dalgacık aileleri için aynı tutularak sadece dalgacık dönüşüm performansı analiz edilmiştir. Görüntü sıkıştırmada yeni ve etkili dalgacık ailelerinin gerçekleştirilebileceği NWI önerisinde olduğu gibi gösterilmiştir.

Kaynakça

  • Ayiluri SR, Yelchuri SK, Laxumudu V, Sajan GP, Kumar, AYP, Kaur K. Jpeg Image Compression Using Matlab. International Research Journal of Modernization in Engineering Technology and Science. 2022;3(5) :663-669
  • Oz I. İki boyutlu ayrık dalgacık dönüşüm filtreleri kullanarak sabit ve hareketli görüntü sıkıştırma. PhD, Sakarya University, Sakarya, Turkey, 2006.
  • Aranzado, J. D. R., Barbosa, G. K., Linget, K. F., & Agustin, V. A. Enhancement of the Huffman Algorithm with Discrete Wavelet Transform Applied to Lossless Image Compression.
  • Bulut, F. Low dynamic range histogram equalization (LDR-HE) via quantized Haar wavelet transform. The Visual Computer, 2022;38(6), pp.2239-2255
  • Chen Y. An introduction to wavelet analysis with applications to image and jpeg 2000. In 2022 4th International Conference on Intelligent Medicine and Image Processing. 2022; pp. 49-57.
  • Wang D, Zhang L, Vincent, A. Improvement of JPEG2000 using curved wavelet transform. In Proceedings.(ICASSP'05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005; Vol. 2, pp. ii-365, IEEE.
  • Yilmaz Ö, Aksoy M, Kesilmiş Z. Misalignment fault detection by wavelet analysis of vibration signals. International Advanced Researches and Engineering Journal, 2019;. 3(3), 156-163.
  • Viswanthan P, Kalavathi P. Subband Thresholding for Near-Lossless Medical Image Compression. International Journal of Computing and Digital Systems. 2023;14(1), 1-1.
  • Toraman S, Turkoglu I. A new method for classifying colon cancer patients and healthy people from FTIR signals using Wavelet transform and machine learning techniques. Journal of the Faculty of Engineering and Architecture of Gazi University. 2020; 35(2), 933-942.
  • Saffor A, Ramli AR, Ng KHA. Comparative study of image compression between JPEG and Wavelet. Malaysian Journal of computer science. 2001;14(1), 39-45.
  • Mishra D, Singh SK, Singh RK. Wavelet-based deep auto encoder-decoder (wdaed)-based image compression. IEEE Transactions on Circuits and Systems for Video Technology. 2020; 31(4), 1452-1462.
  • Taujuddin NSAM, Ibrahim R, Sari S. An improved technique to wavelet thresholding at details subbands for image compression. ARPN Journal of Engineering and Applied Sciences. 2016;11(18), 10721-10726.
  • Starosolski R. Hybrid adaptive lossless image compression based on discrete wavelet transform. Entropy. 2020; 22(7), 751.
  • Oz I, Oz C, Yumusak N. Image compression csing 2-D multiple-level discrete davelet transform (DWT). Eleco 2001 International Conference on Electrical and Electronics Engineering 2001; Turkey.
  • Boujelbene R. Jemaa YB, Zribi M. A comparative study of recent improvements in wavelet-based image coding schemes. Multimedia Tools and Applications. 2019; 78, 1649-1683.
  • Ranjan R, Kumar P. An Improved Image Compression Algorithm Using 2D DWT and PCA with Canonical Huffman Encoding. Entropy.2023; 25(10), 1382.
  • Aranzado JDR, Barbosa GK, Linget KF, Agustin VA. Enhancement of the huffman algorithm with discrete wavelet transform applied to lossless image compression. United International Journal for Research & Technology. 2023; Volume 04, Issue 08, pp 83-90.
  • Onufriienko D, Taranenko Y. Filtering and compression of signals by the method of discrete wavelet decomposition into one-dimensional series. Cybernetics and Systems Analysis. 2023;1-8.
  • Martin MB, Bell AE. New Image compression techniques using multiwavelets and multiwavelet packets. in IEEE Transactions on Image Processing, 2001; vol. 10, no. 4, pp. 500-510.
  • Keser S. An image compression method based on subspace and down sampling. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, . 2023;12(1), 215-225
  • Keser S, Gerek ÖN, Seke E, Gülmezoğlu MBA. Subspace based progressive coding method for speech compression. Speech Communication, ,2017; 94, 50-61.
  • Bindulal TS. Performance analysis of modified wavelet difference reduction methods in image compression and transmission. International Journal of Advanced and Applied Sciences. 2023; 10(10), Pages: 229-238.
  • Viswanthan P, Kalavath P. Subband Thresholding for Near-Lossless Medical Image Compression. International Journal of Computing and Digital Systems. 2023; 14(1), 1-1.
  • Ahamad MG, Almazyad A, Ali SA. Design and development of new parametric wavelet for image denoising. International Journal of Electronics and Communication Engineering. 2011; 4 (1), pp.1-9
  • Da Silva PCL. Use of daubechies wavelets in the representation of analytical functions. In Wavelet Theory. IntechOpen. 2020.
  • Ince IF, Bulut F, Kilic I., Yildirim ME, Ince OF. Low dynamic range discrete cosine transform (LDR-DCT) for high-performance JPEG image compression. The Visual Computer, 2022;38(5), pp.1845-1870.
  • Bulut F. Huffman Algoritmasıyla Kayıpsız Hızlı Metin Sıkıştırma. El-Cezeri, 2016; 3, no. 2.

Comparative Analysis of Wavelet Families in Image Compression, Featuring the Proposed New Wavelet

Yıl 2024, Cilt: 19 Sayı: 1, 279 - 294, 28.03.2024
https://doi.org/10.55525/tjst.1428424

Öz

Image compression is fundamental to the efficient and cost-effective use of digital media, including but not limited to medical imagery, satellite images, and daily photography. Wavelet transform is one of the best methods used in compression. This study conducts a meticulous comparative analysis of various established wavelet families and introduces a novel wavelet named new wavelet for image compression (NWI), shedding light on its performance compared to well-established counterparts. This research conducts a meticulous comparative analysis of various wavelet families to assess their performance in image compression. The results show that an average compression ratio of around 75% can be achieved with a 38 dB PSNR value for all test images. The best result was achieved with the test-2 image, with a compression performance (CP) of 3312.08, using the proposed NWI wavelet. The research evaluates eight wavelet families and shows that the performance of image compression depends on both image type and selected wavelet family while keeping the coding algorithm the same for all calculations of image processing scenarios. In image compression, introducing new wavelet families, such as the NWI, can enhance performance and achieve better results.

Kaynakça

  • Ayiluri SR, Yelchuri SK, Laxumudu V, Sajan GP, Kumar, AYP, Kaur K. Jpeg Image Compression Using Matlab. International Research Journal of Modernization in Engineering Technology and Science. 2022;3(5) :663-669
  • Oz I. İki boyutlu ayrık dalgacık dönüşüm filtreleri kullanarak sabit ve hareketli görüntü sıkıştırma. PhD, Sakarya University, Sakarya, Turkey, 2006.
  • Aranzado, J. D. R., Barbosa, G. K., Linget, K. F., & Agustin, V. A. Enhancement of the Huffman Algorithm with Discrete Wavelet Transform Applied to Lossless Image Compression.
  • Bulut, F. Low dynamic range histogram equalization (LDR-HE) via quantized Haar wavelet transform. The Visual Computer, 2022;38(6), pp.2239-2255
  • Chen Y. An introduction to wavelet analysis with applications to image and jpeg 2000. In 2022 4th International Conference on Intelligent Medicine and Image Processing. 2022; pp. 49-57.
  • Wang D, Zhang L, Vincent, A. Improvement of JPEG2000 using curved wavelet transform. In Proceedings.(ICASSP'05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005; Vol. 2, pp. ii-365, IEEE.
  • Yilmaz Ö, Aksoy M, Kesilmiş Z. Misalignment fault detection by wavelet analysis of vibration signals. International Advanced Researches and Engineering Journal, 2019;. 3(3), 156-163.
  • Viswanthan P, Kalavathi P. Subband Thresholding for Near-Lossless Medical Image Compression. International Journal of Computing and Digital Systems. 2023;14(1), 1-1.
  • Toraman S, Turkoglu I. A new method for classifying colon cancer patients and healthy people from FTIR signals using Wavelet transform and machine learning techniques. Journal of the Faculty of Engineering and Architecture of Gazi University. 2020; 35(2), 933-942.
  • Saffor A, Ramli AR, Ng KHA. Comparative study of image compression between JPEG and Wavelet. Malaysian Journal of computer science. 2001;14(1), 39-45.
  • Mishra D, Singh SK, Singh RK. Wavelet-based deep auto encoder-decoder (wdaed)-based image compression. IEEE Transactions on Circuits and Systems for Video Technology. 2020; 31(4), 1452-1462.
  • Taujuddin NSAM, Ibrahim R, Sari S. An improved technique to wavelet thresholding at details subbands for image compression. ARPN Journal of Engineering and Applied Sciences. 2016;11(18), 10721-10726.
  • Starosolski R. Hybrid adaptive lossless image compression based on discrete wavelet transform. Entropy. 2020; 22(7), 751.
  • Oz I, Oz C, Yumusak N. Image compression csing 2-D multiple-level discrete davelet transform (DWT). Eleco 2001 International Conference on Electrical and Electronics Engineering 2001; Turkey.
  • Boujelbene R. Jemaa YB, Zribi M. A comparative study of recent improvements in wavelet-based image coding schemes. Multimedia Tools and Applications. 2019; 78, 1649-1683.
  • Ranjan R, Kumar P. An Improved Image Compression Algorithm Using 2D DWT and PCA with Canonical Huffman Encoding. Entropy.2023; 25(10), 1382.
  • Aranzado JDR, Barbosa GK, Linget KF, Agustin VA. Enhancement of the huffman algorithm with discrete wavelet transform applied to lossless image compression. United International Journal for Research & Technology. 2023; Volume 04, Issue 08, pp 83-90.
  • Onufriienko D, Taranenko Y. Filtering and compression of signals by the method of discrete wavelet decomposition into one-dimensional series. Cybernetics and Systems Analysis. 2023;1-8.
  • Martin MB, Bell AE. New Image compression techniques using multiwavelets and multiwavelet packets. in IEEE Transactions on Image Processing, 2001; vol. 10, no. 4, pp. 500-510.
  • Keser S. An image compression method based on subspace and down sampling. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, . 2023;12(1), 215-225
  • Keser S, Gerek ÖN, Seke E, Gülmezoğlu MBA. Subspace based progressive coding method for speech compression. Speech Communication, ,2017; 94, 50-61.
  • Bindulal TS. Performance analysis of modified wavelet difference reduction methods in image compression and transmission. International Journal of Advanced and Applied Sciences. 2023; 10(10), Pages: 229-238.
  • Viswanthan P, Kalavath P. Subband Thresholding for Near-Lossless Medical Image Compression. International Journal of Computing and Digital Systems. 2023; 14(1), 1-1.
  • Ahamad MG, Almazyad A, Ali SA. Design and development of new parametric wavelet for image denoising. International Journal of Electronics and Communication Engineering. 2011; 4 (1), pp.1-9
  • Da Silva PCL. Use of daubechies wavelets in the representation of analytical functions. In Wavelet Theory. IntechOpen. 2020.
  • Ince IF, Bulut F, Kilic I., Yildirim ME, Ince OF. Low dynamic range discrete cosine transform (LDR-DCT) for high-performance JPEG image compression. The Visual Computer, 2022;38(5), pp.1845-1870.
  • Bulut F. Huffman Algoritmasıyla Kayıpsız Hızlı Metin Sıkıştırma. El-Cezeri, 2016; 3, no. 2.
Toplam 27 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Görüntü İşleme
Bölüm TJST
Yazarlar

İbrahim Öz 0000-0003-4593-917X

Yayımlanma Tarihi 28 Mart 2024
Gönderilme Tarihi 30 Ocak 2024
Kabul Tarihi 28 Mart 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 19 Sayı: 1

Kaynak Göster

APA Öz, İ. (2024). Comparative Analysis of Wavelet Families in Image Compression, Featuring the Proposed New Wavelet. Turkish Journal of Science and Technology, 19(1), 279-294. https://doi.org/10.55525/tjst.1428424
AMA Öz İ. Comparative Analysis of Wavelet Families in Image Compression, Featuring the Proposed New Wavelet. TJST. Mart 2024;19(1):279-294. doi:10.55525/tjst.1428424
Chicago Öz, İbrahim. “Comparative Analysis of Wavelet Families in Image Compression, Featuring the Proposed New Wavelet”. Turkish Journal of Science and Technology 19, sy. 1 (Mart 2024): 279-94. https://doi.org/10.55525/tjst.1428424.
EndNote Öz İ (01 Mart 2024) Comparative Analysis of Wavelet Families in Image Compression, Featuring the Proposed New Wavelet. Turkish Journal of Science and Technology 19 1 279–294.
IEEE İ. Öz, “Comparative Analysis of Wavelet Families in Image Compression, Featuring the Proposed New Wavelet”, TJST, c. 19, sy. 1, ss. 279–294, 2024, doi: 10.55525/tjst.1428424.
ISNAD Öz, İbrahim. “Comparative Analysis of Wavelet Families in Image Compression, Featuring the Proposed New Wavelet”. Turkish Journal of Science and Technology 19/1 (Mart 2024), 279-294. https://doi.org/10.55525/tjst.1428424.
JAMA Öz İ. Comparative Analysis of Wavelet Families in Image Compression, Featuring the Proposed New Wavelet. TJST. 2024;19:279–294.
MLA Öz, İbrahim. “Comparative Analysis of Wavelet Families in Image Compression, Featuring the Proposed New Wavelet”. Turkish Journal of Science and Technology, c. 19, sy. 1, 2024, ss. 279-94, doi:10.55525/tjst.1428424.
Vancouver Öz İ. Comparative Analysis of Wavelet Families in Image Compression, Featuring the Proposed New Wavelet. TJST. 2024;19(1):279-94.