臺灣博碩士論文加值系統

現今彩色影像轉至灰階影像的方法，大部分是藉由直接利用影像的亮度來計算其灰階值，在影像顏色不同但亮度相同的情況下這些方法的轉換結果可能會使影像原本的特徵消失，此外人的視覺系統在看一個影像時容易將有相似顏色以及結構類似的物體視為一個整體，觀看一個物體時視覺也容易受到其背景的影響。
為了改善以上的問題，以及考慮到人類視覺系統的特性，故本篇論文探討的是一種區塊間互相影響彩色轉至灰階影像的方法，他是以將一個影像劃分成幾個不同區塊的方式來計算影像的灰階值，並在灰階的計算加入各區塊的色彩資訊以及機率。
首先將影像劃分成兩個區塊，以區塊轉換後亮度值與轉換前色彩差異的能量最小化為目標算出各區塊轉換後的亮度。接著將兩個區塊分別再劃分成兩個子區塊，區塊內的所有像素分別與屬於另一子區塊中的一個代表性像素配對，利用像素的關聯性計算各區塊的色彩權重，計算方式為使兩個配對像素轉換前後像素的顏色差異能量最小化。最後將像素的亮度與求得的色彩資訊及轉換前後的亮度差異加起來可以算出像素屬於各個區塊的灰階值，再將各區塊灰階值結合像素屬於各區塊的機率即可完成影像的灰階轉換。
我們用所提出的方法去進行實驗，發現調整影像各區塊間的對比度參數，其值越大，區塊間的對比度會越高。另一方面，調整像素屬於各區塊的機率，值太小或太大時，因受到其他區塊影響的程度也過小或過大，結果均不理想，適當的機率值才能產生理想的結果。實驗結果顯示本論文的方法可供使用者做參數調整獲得不同的效果，轉換的效果更具有彈性。

Most methods that convert color images to grayscale images use the intensity value directly for the converted grayscale value. When two pixels have different colors but the same intensity values, the conversion causes the original contrast to disappear. In addition, human visual system has a tendency of regarding regions with similar colors or structures as a whole.

To solve the aforementioned problem and incorporating the characteristics of the human visual system into the color conversion, this thesis proposes a color-to-grayscale conversion method that considers the influence between image blocks. The method divides an image into several different blocks to calculate an overall grayscale value for each block. Then, it uses the color of a pixel and the probability that a pixel belongs to each block to better differentiate each pixel after grayscale conversion.

The first step is to divide an image into two blocks. The grayscale value of each block is calculated by minimizing the energy of the difference between the grayscale value after conversion and the color distance before conversion. The second step further divides each block into two subblocks. Each pixel in a subblock is paired with a representative pixel in another subblock. Two weights for the chrominance values are calculated by minimizing the energy of color distances before and after conversion. In the third step, the grayscale value for each block is calculated by combining the grayscale value calculated in the first step, the weighted chrominance values calculated in the second step, and the difference between the intensity values before and after conversion. Finally, the grayscale value for each block is multiplied by the probability that a pixel belongs to each block to form the final grayscale value.

In the experiments, we find that adjusting a parameter in the first step increases the contrasts among different blocks. On the other hand, the probability that a pixel belongs to each block cannot be too small or too large because it makes the influence among block either too weak or too strong. The experimental results show that the method can be adjusted to fit different conversion needs.

誌謝 I
摘要 II
Abstract III
目錄 IV
圖目錄 V
第一章 緒論 1
1.1　簡介 1
1.2　研究方法 1
1.3　論文架構 2
第二章　背景介紹 3
2.1　CIELAB色彩空間 3
2.2　YUV色彩空間 4
第三章 彩色至灰階影像轉換 5
3.1　加入區塊色彩資訊之灰階轉換 5
3.1.1　灰階轉換之計算 5
3.1.2 各區塊灰階轉換後之平均亮度 6
3.1.3　各區塊之色彩權重計算 7
3.2　結合機率灰階轉換之結果 9
第四章 實驗結果與比較 10
4.1　軟硬體與分割程式介紹 10
4.2　不同參數γ下的實驗結果 11
4.3　不同機率差異離散度α下的實驗結果 13
4.4　與其他方法之結果比較 16
第五章 結論與未來展望 17
5.1　結論 17
5.2　未來展望 17
參考文獻 18

[1]Metzger, W.: Laws of Seeing. The MIT Press, Cambridge (2006)

[2]Gilchrist, A., Kossyfidis, C., Bonato, F., Agostini, T., Cataliotti, J., Li, X., Spehar, B., Annan, V., Economou, E.: An anchoring theory of lightness perception. Psychol. Rev. 106, 795–834 (1999)

[3]Wyszecki, G., Stiles, W.: Color Science, Concepts and Methods, Quantitative Data and Formulae. Wiley, New York (2000)

[4]Cadik, M.: Perceptual evaluation of color-to-grayscale image conversions. Computer Graphics Forum 27(7), 1745–1754 (2008)

[5]David, H.: The Method of Paired Comparisons. Oxford University. Press, London (1988)

[6]Bala, R., Eschbach, R.: Spatial color-to-grayscale transform preserving chrominance edge information. In: Color Imaging Conference,IS&T—The Society for Imaging Science and Technology,pp. 82–86 (2004)

[7]Grundland, M., Dodgson, N.: Decolorize: fast, contrast enhancing, color to grayscale conversion. Pattern Recogn. 40(11), 2891–2896(2007)

[8]Gooch, A., Olsen, S., Tumblin, J., Gooch, B.: Color2gray: salience-preserving color removal. ACM Trans. Graph. 24(3), 634–639 (2005)

[9]Kim, Y., Jang, C., Demouth, J., Lee, S.: Robust color-to-gray via nonlinear global mapping. ACM Trans. Graph. 28(5), (2009)

[10]Kuhn, G.R., Oliveira, M.M., Fernandes, L.A.F.: An improved contrast enhancing approach for colortograyscale mappings. Vis. Comput. 24(7), 505–514 (2008)

[11]Wu•J,Shen•X,Liu•L:Interactive two-scale color-to-gray.In:Springer-Verlag (2012)

[12]Neumann, L.Cadik, M., Nemcsics, A.: An efficient perceptionbased adaptive color to gray transformation. In: Proceedings of Computational Aesthetics 2007, pp. 73–80. Eurographics Association, Banff (2007)

[13]Rasche, K., Geist, R., Westall, J.: Detail preserving reproduction of color images for monochromats and dichromats. IEEE Comput. Graph. Appl. 25(3), 22–30 (2005)

[14]Rasche, K., Geist, R., Westall, J.: Re-coloring images for gamuts of lower dimension. Comput. Graph. Forum 24(3), 423–432(2005)