雜訊在自然界中經常被視為只會破壞原訊號，而沒有任何的益處，由於隨機共振觀念的提出，適當雜訊可以加強訊號辨識度的事實已被各界所關注，許多跟各領域訊號處理有關的研究也開始以此方法去做應用性的延伸。
本論文主要是在探討在彩色影像中紅綠藍成份強度變化對於人眼視覺所產生隨機共振現象，論文的內容主要是將先前與灰階影像和人類視覺有關的隨機共振研究做延伸，一開始會先簡述隨機共振的原理與人眼視覺的量化方法，再介紹論文裡所會用到的影像評估模型，接著說明實驗方法，本論文有兩個實驗，第一個實驗 (實驗A) 會將原本的彩色影像與亮度被降低後的彩色影像分別去做隨機共振處裡，再將其結果做比較並驗證微弱的彩色影像訊號的確會較容易對人類視覺產生隨機共振的現象，第二個實驗 (實驗B) 主要是把之前相關研究中的灰階測試影像轉換到紅綠藍色彩空間再加入不同的色彩背景，完成後再加入不同強度的雜訊與通過不同的門檻值去產生本論文所會用到的測試影像，將測試影像製成影片後找不同的受測者去做測試，並以影像品質評估模型去對測試影像做影像品質評估測試，之後會將動態影片的受測結果與靜態影像的品質評估結果以表格呈現並做分析與探討，動態影像的受測結果是以特性歸納與比較為主，靜態影像的品質評估結果將會以影像品質評估模型的特性或數學公式去分別對不同的模型做推論。
本論文除了保留先前灰階影像研究中所用的模型，會再加入其他三個模型去做測試，以增加論文的應用性。

Noise in nature is often seen to destroy the original signal without any benefits. Because the concept of the Stochastic Resonance has been proposed, the fact that noise can enhance signal recognition is widely concerned and researches in many fields of signal processing are started with this way for the extension of application.
Our study is mainly focused on discussion for the Stochastic Resonance effect on human visual perception created with strength variation of R, G, and B part in color images. This thesis is the extension of the previous researches of Stochastic Resonance related to gray-level image and human visual perception. We will begin our research with the explanation of Stochastic Resonance and the quantization for human visual perception then introduce the image quality estimation models used in this thesis. After introducing those models, the experiment procedure will be presented. There are two experiments in this thesis. In the first experiment (experiment A), original natural color images and luminance-shifted are corrupted with various noises and thresholds. The comparison of their results will then be done to verify the fact that weak image is easier to cause the SR effect for human visual perception. For the second experiment (experiment B), we transform the gray-level test image used in previous researches to RGB color space with different color background then make the test images used in this thesis with various noise-threshold. Those test images are used to create the test videos. Ten human subjects are asked to evaluate the test videos and their perception results will be averaged. The image quality results for those test images will be evaluated with image quality estimation models. We present the results with tables and do the analysis and discussion in the text of this thesis. For the results of subjects, we summarize their properties and do the comparison. For the results of image quality estimation models, we perform the inference individually with their properties or mathematical functions.
In this experiment, in addition to the image quality estimation models used in previous studies, we will use three other models to make the application wider.

[1] R. Benzi, A. Sutera and A. Vulpiani, “The Mechanism of Stochastic Resonance,” J. Phys. A: Math. Gen., pp. 453-457, 1981.
[2] E. Simonotto, M. Riani, C. Seife, M. Roberts, J. Twitty, and F. Moss, “Visual Perception of Stochastic Resonance,” Phys. Rev. Lett., vol. 78, pp. 1186-1189, 1997.
[3] Y. Zhang, Y. G. Leng, S. M. Wan, G. L. Huang, “The Application of Bistable Stochastic Resonance in Image Processing via Tuning System Parameters,” International Conference on Information and Automation, Zhangjiajie, China, June, 2008.
[4] T. C. Lin, “Effect of Low-Pass Image Filtering to the Stochastic Resonance Phenomenon in Human Visual System,” Master Thesis, EE-NTHU, 2010.
[5] V.P. Subramanyam Rallabandi, “Enhancement of Ultrasound Images Using Stochastic Resonance-based Wavelet Transform,” Computerized Medical Imaging and Graphics, vol. 32, pp. 316-320, 2008.
[6] R. Benzi, G. Parist, A. Sutera and A. Vulpiani, “Stochastic Resonance in Climatic Change,” Tellus, vol. 34, pp. 10-16, 1982.
[7] P. E. Greenwood, L. M. Ward, D. F. Russell, A. Neiman, and F. Moss, “Stochastic Resonance Enhances the Electrosensory Information Available to Paddlefish for Prey Capture,” Phys. Rev. Lett., vol. 84, pp. 4773-4776, 2000.
[8] P. Hanggi, “Stochastic Resonance in Biology”, CHEMPHYSCHEM, pp. 285-290, 2002.
[9] A. Roorda, “Human Visual System—Image Formation,” University of Houston College of Optometry Houston, TX.
[10] How we see (web site): http://www.nei.nih.gov/healthyeyes/howwesee.asp.
[11] Q. Huynh-Thu and M. Ghanbari, “Scope of Validity of PSNR in Image/Video Quality Assessment,” Electron. Lett., vol. 44, pp. 800-801, 2008.
[12] Z. Wang, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Trans. Image Processing, vol. 13, pp. 600-612, 2004.
[13] D. M. Chandler, “VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images,” IEEE Trans. Image Processing, vol. 16, pp. 2284-2298, 2007.
[14] D. M. Chandler and S. S. Hemami, “Suprathreshold Image Compression Based on Contrast Allocation and Global Precedence,” Proc. SPIE Human Vision and Electronic Imaging VIII, 2003.
[15] C. Poynton, “The Rehabilitation of Gamma,” Proc. SPIE Human Vision and Electronic Imaging III, 1998.
[16] C. K. Chui, “An Introduction to Wavelets,” Academic Press, 1992.
[17] J. Kuo, Y. K. Lai and C.-C., “A Haar Wavelet Approach to Compressed Image Quality Measurement,” Journal of Visual Communication and Image Representation, vol. 11, pp. 17-40, 2000.
[18] A. V. Oppenheim and J. S. Lim, “The Importance of Phase in Signals,” Proc. IEEE, vol. 69, pp. 529-541, 1981.
[19] P. D Shukla, “Complex Wavelet Transforms and Their Applications,” The Committee for Postgraduate Studies of the University of Strathclyde, 2003.
[20] M. P. Sampat, Z. Wang, S. Gupta, A. C. Bovik, and M. K. Markey, “Complex Wavelet Structural Similarity: A New Image Similarity Index,” IEEE Trans. Image Processing, vol. 18, pp. 2385-2401, 2009.
[21] C. A. Curcio, K. R. Sloan, R. E. Kalina, and A. E. Hendrickson, “Human Photoreceptor Topography,” J Comp Neurol., pp. 497-523, 1990.
[22] The Stockman & Sharpe (2000) 2° cone fundamentals (web site): http://www.cvrl.org/database/text/cones/ss2_10.htm.
[23] “Colorimetry,” (CIE 1971) Publication, no. 15, 1986, 1971.
[24] B. Hill, Th. Roger, and F. W. Vorhagen, “Comparative Analysis of the Quantization of Color Spaces on the Basis of the CIE LAB Color-Difference Formula,” ACM Transactions on Graphics, vol. 16, pp. 109-154, 1997.
[25] X. Zhang and B. A. Wandell, “A Spatial Extension of CIE LAB for Digital Color Image Reproduction,” Journal of the Society for Information Display, vol. 5, pp. 61-63, 1997.
[26] G. M. Johnson and M. D. Fairchild, “A Top Down Description of S-CIELAB and CIEDE2000,” Color Research & Application, vol. 28, pp. 425-435, 2003.
[27] Z. Wang and A. C. Bovik, “A Universal Image Quality Index,” IEEE Signal Processing Letters, vol. 9, pp. 81-84, 2002.