Journals / Düzce Üniversitesi Bilim ve Teknoloji Dergisi / 2018 / Cilt: 6 - Sayı: 4

One-Dimensional Histogram for Color Images

Renkli Görüntüler İçin Tek Boyutlu Histogram

Pages
1094–1107
Publication Date
2022-10-12
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The loss of information in the images reduced is evaluated by the Peak Signal Noise Ratio (PSNR) criterion.

📌 3 Key Takeaways

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Objective & Hypothesis:Histogram is an important information representation method which shows the distribution of the pixels in digital images.
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Key Findings & Data: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.
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Conclusion & Impact:The loss of information in the images reduced is evaluated by the Peak Signal Noise Ratio (PSNR) criterion.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:KYM Renk Uzayı · Histogram · Eşikleme

Abstract

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.

Özet

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.

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Cite This Article

TANYERİ, U., DEMİRCİ, R., KILIÇASLAN, M. (2022). One-Dimensional Histogram for Color Images. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, Cilt: 6 - Sayı: 4, 1094–1107. https://ajindex.com/articles/494563

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References (7)

  1. 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
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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 quan