Öz
Histogram is an important information representation method which shows the distribution of the pixels in digital images. In this context, one-dimensional array is processed in gray level images whereas it is necessary to analyze three-dimensional array in color images. Therefore, the computational cost of histogram processing in color images is high. Combining the one-dimensional histogram information from each channel is also a problem. In this study, a new technique was developed to produce a one-dimensional histogram by using the Red-Green-Blue (RGB) color space for color images. In the proposed approach, thresholds are first obtained for each channel using Otsu and Kapur thresholding methods, and then the color space is divided into 8 prisms by means of the thresholds. The remaining pixels in the generated prism are clustered by assigning into the same class. In addition, the color reduction is done by using the average value of the pixels included in the same class. The loss of information in the images reduced is evaluated by the Peak Signal Noise Ratio (PSNR) criterion.
Anahtar Kelimeler
Kaynakça
- 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, c. 13 s. 4, ss. 600-612, 2004.
- S.O. Abter, and N.A. Abdullah, “An efficient color quantization using color histogram,” In New Trends in Information and Communications Technology Applications (NTICT), 2017 Annual Conference, Baghdad, IRAQ, 2017, ss. 13-17.
- F. Adrian, and R. Alan, Color Space Conversions, Westminster University, London, 1998, ss. 1-31.
- G. H. Joblove, and D. Greenberg, “Color spaces for computer graphics,” In ACM siggraph computer graphics, c. 12, s. 3, ss. 20-25, 1978.
- S. Jennings, Artist's color manual: The complete guide to working with color, Chronicle Books LLC, United States, 2003, ss. 1-192.
- D.S. Bloomberg, “Color quantization using octrees,” Leptonica, ss. 1-10, 2008.
- P.S. Heckbert, “Color image quantization for frame buffer display,” Comput. Graph, c. 16, s. 3, ss. 297-307, 1982.
- A. Kruger, “Median-cut color quantization,” Dr Dobb's Journal-Software Tools for the Professional Programmer, c. 19, s. 10, ss. 46-55, 1994.
- H.J. Park, K.B. Kim, and E.Y. Cha, “An effective color quantization method using octree-based self-organizing maps,” Computational intelligence and neuroscience, c. 2016, ss. 22, 2016.
- C. Lu-yu, and Z. Chun-yan, “Image retrieval algorithm based on block color histogram and GWLBP,” Chinese Journal of Liquid Crystals and Displays, c. 32, s. 9, ss. 755-763, 2017.
- N. Shrivastava, and V. Tyagi, “An efficient technique for retrieval of color images in large databases,” Computers & Electrical Engineering, c. 46, ss. 314-327, 2015.
- N. Varish, J. Pradhan, and A.K. Pal, “Image retrieval based on non-uniform bins of color histogram and dual tree complex wavelet transform,” Multimedia Tools and Applications, c. 76, s. 14, ss. 15885-15921, 2017.
- P. Liu, J.M. Guo, K. Chamnongthai, and H. Prasetyo, “Fusion of color histogram and LBP-based features for texture image retrieval and classification,” Information Sciences, c. 390, ss. 95-111, 2017.
- N. Otsu, “A threshold selection method from gray-level histograms,” IEEE transactions on systems, man, and cybernetics, c. 9, s. 1, ss. 62-66, 1979.
- J.N. Kapur, P.K. Sahoo, and A.K. Wong, “A new method for gray-level picture thresholding using the entropy of the histogram,” Computer vision, graphics, and image processing, c. 29, s. 3, ss. 273-285, 1985.
- M. Sezgin, and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” Journal of Electronic imaging, c. 13, s. 1, ss. 146-168, 2004.
- M.O. İncetaş, U. Tanyeri, M. Kılıçaslan, B. Yakışır Girgin, R. Demirci, “Eşik Seçiminin Benzerliğe Dayalı Kenar Belirlemeye Etkisi,” 1st International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT 2017), Tokat, Türkiye, 2017, ss.102-106.
- M. Kılıçaslan, U. Tanyeri, M.O. İncetaş, B. Yakışır Girgin, R. Demirci, “Eşikleme Tekniklerinin Renk Uzayı Tabanlı Kümeleme Yönteminin Başarısına Etkisi,” 1st International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT 2017), Tokat, Türkiye, 2017, ss. 107-110.
- M.O. İncetaş, E. Veske, N. Emre, R. Demirci, “Automatic Cells Counting in Natt-Herrick Stained Fish Blood,” YUNUS Research Bulletin, c. 2017, s. 3, ss. 283-294, 2017.
- R. Demirci, U. Güvenç ve H.T. Kahraman, “Görüntülerin renk uzayı yardımıyla ayrıştırılması,” İleri Teknoloji Bilimleri Dergisi, c. 3, s. 1, ss. 1-8, 2014.
Öz
Histogram
sayısal görüntülerdeki piksellerin dağılımını gösteren önemli bir bilgi temsil
yöntemidir. Gri seviyeli görüntülerde tek boyutlu dizi işlenirken, renkli
görüntülerde üç boyutlu dizinin analizinin yapılması gereklidir. Dolayısıyla
renkli görüntülerde histogram işleminin hesapsal maliyeti yüksektir. Her
kanaldan alınan tek boyutlu histogram bilgisinin birleştirilmesi ise ayrıca bir
problemdir. Bu çalışmada renkli görüntülerde Kırmızı-Yeşil-Mavi (KYM) renk
uzayı kullanılarak tek boyutlu histogram üreten yeni bir teknik
geliştirilmiştir. Önerilen yaklaşımda, öncelikle her kanal için Otsu ve Kapur
eşikleme yöntemleri kullanılarak eşikler elde edilmiş, akabinde renk uzayı söz
konusu eşikler yardımıyla 8 adet prizmaya bölünmüştür. Oluşturulan prizma
içerisinde kalan pikseller aynı sınıfa atanarak kümeleme yapılmıştır. İlave
olarak aynı sınıfa dâhil olan piksellerin ortalama değeri kullanılarak renk
indirgemesi yapılmıştır. Böylece elde edilen görüntülerdeki bilgi kaybı tepe
sinyal gürültü oranı (Peak Signal Noise Ratio: PSNR) ölçütü ile
değerlendirilmiştir.
Anahtar Kelimeler
Kaynakça
- 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, c. 13 s. 4, ss. 600-612, 2004.
- S.O. Abter, and N.A. Abdullah, “An efficient color quantization using color histogram,” In New Trends in Information and Communications Technology Applications (NTICT), 2017 Annual Conference, Baghdad, IRAQ, 2017, ss. 13-17.
- F. Adrian, and R. Alan, Color Space Conversions, Westminster University, London, 1998, ss. 1-31.
- G. H. Joblove, and D. Greenberg, “Color spaces for computer graphics,” In ACM siggraph computer graphics, c. 12, s. 3, ss. 20-25, 1978.
- S. Jennings, Artist's color manual: The complete guide to working with color, Chronicle Books LLC, United States, 2003, ss. 1-192.
- D.S. Bloomberg, “Color quantization using octrees,” Leptonica, ss. 1-10, 2008.
- P.S. Heckbert, “Color image quantization for frame buffer display,” Comput. Graph, c. 16, s. 3, ss. 297-307, 1982.
- A. Kruger, “Median-cut color quantization,” Dr Dobb's Journal-Software Tools for the Professional Programmer, c. 19, s. 10, ss. 46-55, 1994.
- H.J. Park, K.B. Kim, and E.Y. Cha, “An effective color quantization method using octree-based self-organizing maps,” Computational intelligence and neuroscience, c. 2016, ss. 22, 2016.
- C. Lu-yu, and Z. Chun-yan, “Image retrieval algorithm based on block color histogram and GWLBP,” Chinese Journal of Liquid Crystals and Displays, c. 32, s. 9, ss. 755-763, 2017.
- N. Shrivastava, and V. Tyagi, “An efficient technique for retrieval of color images in large databases,” Computers & Electrical Engineering, c. 46, ss. 314-327, 2015.
- N. Varish, J. Pradhan, and A.K. Pal, “Image retrieval based on non-uniform bins of color histogram and dual tree complex wavelet transform,” Multimedia Tools and Applications, c. 76, s. 14, ss. 15885-15921, 2017.
- P. Liu, J.M. Guo, K. Chamnongthai, and H. Prasetyo, “Fusion of color histogram and LBP-based features for texture image retrieval and classification,” Information Sciences, c. 390, ss. 95-111, 2017.
- N. Otsu, “A threshold selection method from gray-level histograms,” IEEE transactions on systems, man, and cybernetics, c. 9, s. 1, ss. 62-66, 1979.
- J.N. Kapur, P.K. Sahoo, and A.K. Wong, “A new method for gray-level picture thresholding using the entropy of the histogram,” Computer vision, graphics, and image processing, c. 29, s. 3, ss. 273-285, 1985.
- M. Sezgin, and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” Journal of Electronic imaging, c. 13, s. 1, ss. 146-168, 2004.
- M.O. İncetaş, U. Tanyeri, M. Kılıçaslan, B. Yakışır Girgin, R. Demirci, “Eşik Seçiminin Benzerliğe Dayalı Kenar Belirlemeye Etkisi,” 1st International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT 2017), Tokat, Türkiye, 2017, ss.102-106.
- M. Kılıçaslan, U. Tanyeri, M.O. İncetaş, B. Yakışır Girgin, R. Demirci, “Eşikleme Tekniklerinin Renk Uzayı Tabanlı Kümeleme Yönteminin Başarısına Etkisi,” 1st International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT 2017), Tokat, Türkiye, 2017, ss. 107-110.
- M.O. İncetaş, E. Veske, N. Emre, R. Demirci, “Automatic Cells Counting in Natt-Herrick Stained Fish Blood,” YUNUS Research Bulletin, c. 2017, s. 3, ss. 283-294, 2017.
- R. Demirci, U. Güvenç ve H.T. Kahraman, “Görüntülerin renk uzayı yardımıyla ayrıştırılması,” İleri Teknoloji Bilimleri Dergisi, c. 3, s. 1, ss. 1-8, 2014.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 1 Ağustos 2018 |
| Yayımlandığı Sayı | Yıl 2018 Cilt: 6 Sayı: 4 |
