臺灣博碩士論文加值系統

隨著資訊與網路的發達，資訊嵌入這項技術在資訊安全的議題中有越來越被重視之趨勢，由於高動態範圍影像所能夠表現出的豐富色彩遠遠的大於低動態範圍影像，所以高動態範圍影像有日漸成為主流影像的趨勢。因此本論文針對高動態範圍影像提出極低失真與無失真的資訊嵌入演算法。
我們所提的第一個演算法為「高動態範圍影像三區間編碼資訊嵌入演算法」，簡稱TRICOD。我們充分善用所有高動態範圍影像的同質性像素提供之狀態數，加上三區間編碼的技巧來進行資訊的嵌入，故此演算法可以在不提高影像變動量的前提下，增加訊息嵌入量。實驗結果顯示：我們所提出的演算法相較於最新文獻，可提升5.52%~5.79%之資訊嵌入量。高動態範圍偽裝影像經色調映射後產生的低動態範圍影像，經量化與視覺化差異量測(HDR-VDP)並無影像失真。
我們所提的第二個演算法為「使用Null Pixel的高動態範圍影像的資訊嵌入演算法」，簡稱NULPIX。我們發現先前學者所提出的高動態影像像素分類中，尚有一類像素可以使用，只要針對分類為Null之像素的E通道來進行，即能夠完成嵌入資訊演算法。實驗結果顯示：相較於最新的文獻，NULPIX演算法平均可以提高48.37%之嵌入量，高動態範圍偽裝影像經由色調映射後之影像，經量化或視覺化差異量測下並無影像失真。
我們所提的第三個演算法為「高動態範圍影像最低失真資訊嵌入演算法」，簡稱FEAPIX。此演算法是文獻首創的演算法。我們提出一個最佳化計算機制，將原本無法被使用的像素，經由最佳化的計算後，使得資訊能夠順利的嵌入。實驗結果顯示：演算法可大幅提高2.77～3.02倍之秘密訊息嵌入量。高動態範圍影像色調映射後之影像經由量化後其PSNR仍高達76.60～84.44 dB；影像雖略有些微失真，但經由人眼視覺化差異量測的演算法得知，仍無法辨識差異性。
我們所提的第四個演算法為「多像素群組機制的高動態範圍影像資訊嵌入演算法」，簡稱CLUTCOD。我們利用同質性像素排列組合之特性並配合三區間編碼的技巧，使用M個像素(M>=2)當作一個群組來嵌入訊息，故可以在不增加偽裝影像變動量的前提下，提升資訊的嵌入量。實驗結果顯示：演算法可提升317～2397個位元的資訊嵌入量。我們使用色調映射技術將高動態掩護與偽裝影像轉換成低動態掩護與偽裝影像，以供量化差異與視覺化差異分析。量化分析結果顯示：低動態掩護影像與偽裝影像間並無PSNR數值的差異；我們使用視覺化差異預測器(HDR-VDP)來量測低動態掩護與偽裝影像之視覺化差異。視覺化差異分析所產出之機率地圖顯示：掩護與偽裝影像之間，被人眼察覺有差異之機率為零。
本論文主要的貢獻有下列四項：第一、使用三區間編碼的技巧善加利用所有的狀態數，改進先前無失真演算法的侷限嵌入量，進一步提高資訊嵌入藏量。第二、針對之前從未被使用過的像素，提出了一種全新的演算法，使其能夠被善加利用，提高嵌入量。第三、提出最佳化計算機制，充分利用原先無法被使用的像素，能順利的嵌入資訊，雖造成影像有極低的失真，但仍可進一步提高資訊的嵌入量。第四、對於三區間編碼的技巧，我們以多像素為一群來嵌入訊息，在不造成失真情況下，進一步提升嵌入量。本論文提出的四個演算法擴大高動態範圍影像的影像註記與藏密學應用之範疇。

In this paper, we investigate data embedding algorithms for high dynamic range images encoded by the RGBE image format. We present four algorithms that have the distortion-free feature and one algorithm that demonstrates the feature of very low distortion.
Our first algorithm belongs to the distortion-free manner. In this algorithm, we make use of all statuses produced by the pixel variation and employ triplet coding technology to increase the embedding capacity. Comparing with the previous work, our algorithm can improve the embedding capacity in the range between 5.52% and 5.79%. No image distortion is encountered when tone mapping the high dynamic range embedded images to produce the low dynamic range embedded image.
The second algorithm we introduce belongs to the distortion-free manner. In this algorithm, we take advantages of the “null” pixel, a new pixel category produced by the E channel, where we embed messages into these pixels to expand the embedding capacity. Experimental results show that comparing to our counterparts, our algorithm can offer an average of 48.37% embedding capacity without causing any image distortion.
The third algorithm we develop belongs to the very low distortion manner. We adopt an optimization computation mechanism for the R, G, B channels to generate a number of potential pixels, referred to as “promising” and “feasible” pixels. These pixels cause the least image distortion when operating the message embedding. Comparing to our counterparts, the algorithm can largely increase the amount of embedding capacity with the magnitude between 2.77 and 3.02. The tone mapped image presents high PSNR values (76.60~84.44 dB) showing no perceivable visual difference.
The final algorithm we propose is with the distortion-free manner. We take advantage of homogeneous pixel representation and combine a group of M pixels (M>=2) as a pixel cluster to generate sufficient statuses for message embedding. This approach allows us to adopt the triplet coding technique to increase the embedding capacity without incurring any image distortion. We compare our scheme of using 2 pixels as a cluster with previous results of using a single pixel. The comparison indicates that our algorithm can provide larger payloads in the range of 317~2397 bits. We adopt the tone mapping scheme to produce low dynamic range images to quantize the image difference, and we employ the HDR-VDP technique to inspect the visual difference between the cover and stego images. The image difference quantization results show that no distortion is encountered. The probability map produced by HDR-VDP inspection is in grey color indicating that the detection probability of visual difference is null.
In conclusion, our work offers the following four contributions: we exploit the triplet coding technology and increase the capacity for non-distortion algorithm; we make use of a new pixels to convey messages, raising the embedding capacity; we develop the optimization computation mechanism fully using pixels not available in our counterpart to further increase the embedding capacity; we adopt the pixel cluster scheme allowing the increase of concealed messages without causing image distortion. The four algorithms developed are adequate for applications of image annotation and image steganography.

誌 謝 i
摘 要 ii
Abstract iv
目 次 vi
圖 目 次 viii
表 目 次 ix

第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 3
1.3 論文架構 4

第二章 相關文獻探討 5
2.1 Radiance RGBE高動態範圍影像 5
2.2 Yu學者的演算法回顧 7

第三章 高藏量極低失真的高動態範圍影像 10
3.1 高動態範圍影像三區間編碼資訊嵌入演算法 10
3.2 使用Null Pixel的高動態範圍影像的資訊嵌入演算法 16
3.3 高動態範圍影像最低失真資訊嵌入演算法 19
3.4 實驗結果 22
3.4.1 實驗影像與資訊 23
3.4.2 使用三區間編碼實驗結果與比較 24
3.4.3 使用Null Pixel實驗結果與比較 27
3.4.4 使用最低失真資訊嵌入演算法實驗結果與比較 31
3.5 小結 35

第四章 多像素群機制的高動態範圍影像 36
4.1 單像素群之期望嵌入量分析 36
4.2 多像素群之期望嵌入量分析 40
4.3 高動態範圍影像多群資訊嵌入演算法 46
4.4 實驗結果 50
4.4.1 CLUTCOD演算法理論分析結果 51
4.4.2 CLUTCOD演算法實際嵌入結果 61
4.4.3視覺差異評估結果 65
4.5 小結 69

第五章 總結與未來工作 70
5.1 總結 70
5.2 未來工作 71

參考文獻 73
中英對照表 78
英中對照表 81
附錄A 84

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