Robust Image Watermarking Using Adaptive Structure Based Wavelet Tree Quantization

[[abstract]]在本篇論文中，我們提出了一個基於小波樹(Wavelet-tree)之結構式量化的新穎浮水印技術。在此技術中，首先對於小波轉換域之小波樹將被重新編排成超級樹(Super-tree)。接著藉由本論所提出的結構式量化技術再將浮水印位元一對一的嵌入至超級樹中。在此步驟裡，根據浮水印位元之位元狀態，超級樹將被量化成擁有特別的結構特性，並且相較於未嵌入之超級樹，量化後的超級樹擁有強韌的統計特性。根據此一特性，儘管在失真後的嵌入影像(Watermarked Image)中，我們也可以強韌的嵌出正確的浮水印。在文章的最後，針對於嵌入後影像品質的問題，我們另外提出了一個適應性(Adaptive)的方法可以有效的提昇嵌入後影像的峰值訊號雜訊比(PSNR)。相較於未使用此適應性技術之結構式量化的方法，加入此適應性技術後提高了峰值訊號雜訊比約3.21dB。另外相較於參考文獻[17]，我們所提出的適應性結構式量化技術提高了峰值訊號雜訊比約5.83dB。而對於嵌入浮水印的最大容量方面，在本技術中也大幅的提昇了。並且本技術的運算複雜度相對來說減少了許多。在實驗數據方面，對於本論文所提出之適應性結構適量化技術之強韌性，我們採取了許多不同的破壞方式，其中包含濾波器破壞(高斯濾波器、中間值濾波器和銳利化)和幾何破壞(相素位移)。實驗解果顯示，對於以上的破壞，本技術都擁有良好的強韌性。並且對於多重浮水印的破壞，本技術更具有額外的強韌性。[[abstract]]In this paper, we proposed a novel robust wavelet-tree based watermarking method by using structure-based quantization. First, we arrange wavelet-trees into super-trees. Secondly, the watermark bits are embedded into the super-trees by the proposed structure-based quantization method. According to these bits, the super-trees will be quantized into a significant structure. Compared to the unquantized super-tree, the quantized version has strong statistical character. Based on this character, the watermark bits could be extracted robustly from the attack of image distortion. Finally, we further proposed an adaptive method to increase the PSNR value. Compared with the proposed non-adaptive structure-based method, it increases PSNR about 3.21 dB. Compared with Wang et al. [17], it greatly increases PSNR about 5.83 dB. For the consideration of the capacity for embedding, the maximum number of watermark bits is also increased. Besides, the computation load is small. The experimental results show that the proposed watermarking using adaptive structure-based wavelet-tree quantization performs well in JPEG compression, filtering (Gaussian filter, median filter and sharpen) and geometric attacks (pixel shifting). In addition, it is also very robust to against the multiple watermark attack.[[note]]碩

Robust Image Watermarking Using Adaptive Structure Based Wavelet Tree Quantization

This work presents a novel robust wavelet-tree-based watermarking method based on structure-based quantization. Wavelet-trees are arranged into super-trees. The watermark bits are then embedded into the super-trees by using the proposed structure-based quantization method. Next, the super-trees are quantized into a significant structure according to these bits. The quantized super-tree has a stronger statistical characteristic than the unquantized super-tree. Based on this characteristic, the watermark bits could be extracted robustly after an image distortion attack. Finally, an adaptive method is developed to raise the PSNR value. Compared with Wang et al. [17] method, the proposed adaptive method increases PSNR about 5.83dB. The proposed method also has a higher maximum number of watermark bits than other methods, thus increasing the capacity for embedding. Besides, its computation load is low. Experimental results demonstrate that the proposed watermarking method using adaptive structure-based wavelet-tree quantization performs well in JPEG compression, filtering (Gaussian filter, median filter and sharpen) and geometric attacks (pixel shifting and rotation). In addition, it is very robust against multiple watermark attacks

Robust Image Watermarking Using Adaptive Structure Based Wavelet Tree Quantization

[[abstract]]This work presents a novel robust wavelet-tree-based watermarking method based on structure-based quantization. Wavelet-trees are arranged into super-trees. The watermark bits are then embedded into the super-trees by using the proposed structure-based quantization method. Next, the super-trees are quantized into a significant structure according to these bits. The quantized super-tree has a stronger statistical characteristic than the unquantized super-tree. Based on this characteristic, the watermark bits could be extracted robustly after an image distortion attack. Finally, an adaptive method is developed to raise the PSNR value. Compared with Wang et al. [17] method, the proposed adaptive method increases PSNR about 5.83dB. The proposed method also has a higher maximum number of watermark bits than other methods, thus increasing the capacity for embedding. Besides, its computation load is low. Experimental results demonstrate that the proposed watermarking method using adaptive structure-based wavelet-tree quantization performs well in JPEG compression, filtering (Gaussian filter, median filter and sharpen) and geometric attacks (pixel shifting and rotation). In addition, it is very robust against multiple watermark attacks.[[note]]SC

Robust Image Watermarking Using Adaptive Structure Based Wavelet Tree Quantization

[[abstract]]This work presents a novel robust wavelet-tree-based watermarking method based on structure-based quantization. Wavelet-trees are arranged into super-trees. The watermark bits are then embedded into the super-trees by using the proposed structure-based quantization method. Next, the super-trees are quantized into a significant structure according to these bits. The quantized super-tree has a stronger statistical characteristic than the unquantized super-tree. Based on this characteristic, the watermark bits could be extracted robustly after an image distortion attack. Finally, an adaptive method is developed to raise the PSNR value. Compared with Wang et al. [17] method, the proposed adaptive method increases PSNR about 5.83dB. The proposed method also has a higher maximum number of watermark bits than other methods, thus increasing the capacity for embedding. Besides, its computation load is low. Experimental results demonstrate that the proposed watermarking method using adaptive structure-based wavelet-tree quantization performs well in JPEG compression, filtering (Gaussian filter, median filter and sharpen) and geometric attacks (pixel shifting and rotation). In addition, it is very robust against multiple watermark attacks.[[note]]SC