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A Variation Prediction Scheme and an HDR Image Data Hiding Algorithm Using a Weighted Modulus Technique
|關鍵字:||資訊隱藏;data hiding;高動態範圍影像;權重模數技術;動態調整;影像註記;偽裝學;預測機制;訊息分佈機率;影像特徵;high dynamic range image;weighted modulus technique;dynamic boundary adjustment;image annotation;steganography;prediction scheme;probability of secret message;medium features||出版社:||資訊網路多媒體研究所||引用:||[Ashi2002] M. Ashikhmin, “A Tone Mapping Algorithm for High Contrast Images,” in Proceedings of the 13th Eurographics Workshop on Rendering, pp. 145-156, 2002. [Boga2003] R. Bogart, F. Kainz, and D. Hess, “The OpenEXR File Format,” SIGGRAPH 2003 Technical Sketch, 2003. [Chan2004] C. K. Chan and L. M. Cheng, “Hiding Data in Images by Simple LSB Substitution,” Pattern Recognition, Vol. 37, Issue 3, pp. 469-474, 2004. [Chao2009] R. M. Chao, H. C. Wu, C. C. Lee, and Y. P. Chu, “A Novel Image Data Hiding Scheme with Diamond Encoding,” EURASIP Journal on Information Security, Vol. 2009, Article ID 658047, 9 pages, 2009. [Chen2009] Y. M. Cheng and C. M. 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Arvo, Academic Press, pp. 80-83, 1991. [Ward1998] G. Ward-Larson and R. A. Shakespeare, Rendering with Radiance, Morgan Kaufmann, San Francisco, 1998. [Yu2011] C. M. Yu, K. C. Wu, and C. M. Wang, “A Distortion-free Data Hiding Scheme for High Dynamic Range Images,” To appear in Displays, 2011. [Yule2010] G. U. Yule, An Introduction to the Theory of Statistics (1919), Kessinger Publishing, USA, 2010. [Zhan2006] X. Zhang and S. Wang, “Efficient Steganographic Embedding by Exploiting Modification Direction,” IEEE Communications Letters, Vol. 10, No. 11, pp. 781-783, 2006.||摘要:||
我們所提第二個演算法是植基於權重模數之高動態範圍影像秘密訊息嵌入預測機制。我們的預測機制考量秘密訊息分佈、訊息嵌入量與掩護影像特徵等三大因素。具言之，我們經數學分析，使用四個獨立矩陣相乘計算後，即可預知偽裝影像之期望變動量。此預測機制可讓使用者在嵌入秘密訊息前即可預知偽裝影像之嵌入結果。此外，對特定掩護影像而言，使用者如欲產生較低變動量之偽裝影像，該機制可在秘密訊息出現機率範圍內，預先求出最佳之數值。我們以低、中、高鍵值的高動態範圍影像進行實驗。實驗結果顯示：我們的預測機制具有高度之準確性，其預測誤差範圍在0.01%～0.59%之間。當實際在每像素嵌入高達15 位元之秘密訊息時，預測之偽裝影像期望變動量符合實際嵌入測得之變動量；此外，偽裝影像經兩種色調映射演算法處理，產生之低動態範圍影像具有高於30 dB之PSNR數值，仍具良好之視覺品質。
High dynamic range images are able to store a greater dynamic range of luminance in order to more accurately represent the range of intensity levels found in real scenes. This thesis presents a dynamic approach for data hiding, and introduces a prediction scheme based on the weighted modulus embedding technique.
We provide a dynamic pixel adjustment approach for data hiding using the weighted modulus embedding technique. In particular, we consider the R, G, and B channel of the Radiance RGBE encoding format as an embedding unit. In contrast to a static approach, our algorithm alters pixels dynamically when they are encountered with an overflow or underflow problem. This allows our method to reduce the pixel variation due to message embedding and produce a stego image that has less distortion than the static approach. In addition, our dynamic approach maintains the integrity of the RGBE image encoding. Consequently, we are able to produce a stego image which causes no suspicion when the legality of the image encoding is inspected. Experimental results show that the dynamic adjustment can reduce 61%~91% of the number of pixels and decrease 0.8%~4.39% the image variation in comparison with the static approach. Tone-mapped images perform with a good visual quality where the PSNR values are over 30 dB when each pixel is conveyed with 15 bits of secret message. Our data hiding algorithm can resist the RS steganalysis attack and provides high correlation coefficients between the pixel histograms of cover and stego images. Our algorithm provides benefits of high embedding capacity, high image quality, and high security.
The second algorithm we present is a prediction scheme that is able to foresee the expected mean squared error. Our scheme considers three factors including the probability appearance of the secret bit “0” and “1,” the embedding capacity, and the medium features of the high dynamic range images. Specifically, we present a mathematical analysis and we compute the expected mean squared error by simply multiplying four independent matrices. Given a cover high dynamic range image and the probability of the secret bits, our scheme can forecast the mean squared error prior to the real message embedding. Given a range of probability appearance, our mechanism can suggest the best values with this range for a specific cover image in order to produce the stego image that has the smallest pixel variation. We include a variety of high dynamic range images including low, middle, and high keys when conducting an experiment. The experimental results show that our scheme reveals a high accuracy of prediction, the error rates being in the range of 0.01%~0.59%. The accuracy is preserved even when each pixel is concealed with 15 bits of secret messages. Tone-mapped images produced by two different tone mapping algorithms demonstrate that the PSNR values are over 30 dB, and produce a good visual quality of stego images.
In conclusion, this study provides three contributions: the dynamic adjustment approach which reduces the pixel variation and maintains the image integrity; the prediction method which performs with accuracy; the prediction scheme which helps users generate the stego image satisfying desirable demands. Our schemes expands applications of data hiding for high dynamic range images.
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