Depth-Based View-Invariant Blind 3D Image Watermarking

In this thesis, the basics of disparity map and watermarking are reviewed extensively. In order to embed binary information into images, a 3D image watermarking system was proposed. This embedded information was to survive the 3D Image rendering process of Disparity maps, to help identify malicious user who would distribute the watermarked image through an unauthorized system. The proposed system adopted the concept of hidden pixel and introduced an algorithm that identifies all known hidden pixels within the image. This information is combined with the Disparity map to generate a hidden pixel disparity map (HPDM); using the information in the HPDM a decision matrix is generated. This decision matrix is used to guide the watermark embedding process to ensure that information embedded in the Left Image can survive the 3D rendering process. Using the decision matrix, the watermark detector is capable of extracting the image from either the left or right image with no effect on the overall bit rate. This achievement is due to two original additions to the detection process: (1) Reverse rendering and (2) Cyclical Redundancy check. The proposed reverse rendering process expands the decision matrix into a reduced disparity map. This reduced disparity map is used to reverse the right image into a reduced left image. The identification of the image (left or right) is achieved through the use of a CRC check, which is also capable of detecting any errors in the extracted message, thus reducing the number of misidentification. The proposed system was implemented and tested using MATLAB. The bit efficiency of the proposed system varied between 38% and 88%. This variance is caused by the complexity of the depth scene as well as the cost function used in the depth estimation process. The watermark embedding system proposed had a PSNR of 45 dB (when no mark was embedded); this value is primarily attributed to some of the quantization that occurs during the DCT transform. However, a PSNR of 33dB is attained when the watermark was added at full strength.

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Digital watermarking of 3D disparity map based images for transaction tracking

10.32920/ryerson.14657502.v1 ◽

2021 ◽

Author(s):

Ramzy Jaber

Keyword(s):

Image Watermarking ◽

Depth Estimation ◽

3D Image ◽

Disparity Map ◽

Decision Matrix ◽

Dct Transform ◽

Watermark Embedding ◽

Binary Information ◽

Left Image ◽

The Right

In this thesis, the basics of disparity map and watermarking are reviewed extensively. In order to embed binary information into images, a 3D image watermarking system was proposed. This embedded information was to survive the 3D Image rendering process of Disparity maps, to help identify malicious user who would distribute the watermarked image through an unauthorized system. The proposed system adopted the concept of hidden pixel and introduced an algorithm that identifies all known hidden pixels within the image. This information is combined with the Disparity map to generate a hidden pixel disparity map (HPDM); using the information in the HPDM a decision matrix is generated. This decision matrix is used to guide the watermark embedding process to ensure that information embedded in the Left Image can survive the 3D rendering process. Using the decision matrix, the watermark detector is capable of extracting the image from either the left or right image with no effect on the overall bit rate. This achievement is due to two original additions to the detection process: (1) Reverse rendering and (2) Cyclical Redundancy check. The proposed reverse rendering process expands the decision matrix into a reduced disparity map. This reduced disparity map is used to reverse the right image into a reduced left image. The identification of the image (left or right) is achieved through the use of a CRC check, which is also capable of detecting any errors in the extracted message, thus reducing the number of misidentification. The proposed system was implemented and tested using MATLAB. The bit efficiency of the proposed system varied between 38% and 88%. This variance is caused by the complexity of the depth scene as well as the cost function used in the depth estimation process. The watermark embedding system proposed had a PSNR of 45 dB (when no mark was embedded); this value is primarily attributed to some of the quantization that occurs during the DCT transform. However, a PSNR of 33dB is attained when the watermark was added at full strength.

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Fuzzy-connected 3D image segmentation at interactive speeds

Graphical Models ◽

10.1016/s1524-0703(02)00005-9 ◽

2003 ◽

Author(s):

L Nyúl

Keyword(s):

Image Segmentation ◽

3D Image ◽

3D Image Segmentation

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