Color image watermarking self-recovery algorithm based on 3D reference matrix

Fast color image encryption scheme based on 3D orthogonal Latin squares and matching matrix

This paper deals with the analysis, implementation, and comparison of several vector-valued total variation (TV) methods that extend the Rudin-Osher-Fatemi variational model to color images. By considering the discrete gradient of a multichannel image as a 3D structure matrix with dimensions corresponding to the spatial extend, the differences to other pixels and the color channels, we introduce in [J. Duran, M. Moeller, C. Sbert, and D. Cremers, 'Collaborative Total Variation: A General Framework for Vectorial TV Models', SIAM Journal on Imaging Sciences, 9(1), pp.116-151, 2016] collaborative sparsity enforcing norms for penalizing the resulting tensor. We call this class of regularizations collaborative total variation (CTV). We first analyze the denoising properties of each collaborative norm for suppressing color artifacts while preserving image features and aligning edges. We then describe the primal-dual hybrid gradient method for solving the minimization problem in detail. The resulting CTV–L2 variational model can successfully be applied to many image processing tasks. On the one hand, an extensive performance comparison of several collaborative norms for color image denoising is provided. On the other hand, we analyze the ability of different CTV methods for decomposing a multichannel image into a cartoon and a textural part. Finally, we also include a short discussion on alternative minimization methods and compare their computational efficiency.

Numerous image content authentication schemes have been proposed to solve potential security problems in transmitting digital images via the Internet. Aiming at achieving high detection successful rates in image tamper areas localization, a novel image content authentication and self-recovery algorithm using rehashing model and reference matrix \( {\text{M}} \) is proposed in this study. A series of hash functions compose a rehashing model for the sake of avoiding numerous collisions of the random authentication numbers in the procedure of image tamper detection. This scheme utilize the rehashing authentication information as the digital watermark to be embedded into the original image by means of the reference matrix \( \varvec{M} \), in order to authenticate the integrity of the received image. The experiment results demonstrate that the proposed method can detect tamper areas more accurately and recover image tamper content nearly up to 50 % with an acceptable visual quality.

SPIHT (Set Partitioning In hierachical trees), being an efficient coding method for wavelet coefficients, has acquired more and more widely application, especially in image/video compression fields. But, conventional SPHIT have some obvious limitions. For example, when for the color image compression, polarmetric SAR image compression, or multispectrum image compression and other multi-channel image compression, there are only very limited image planes(R,G,B for color image, HH, HV,VVfor polarmetric SAR images) but there exist large amount of information redundancy among the image planes. So, considering the support length of discrete wavelet transform, we can’t use the set 8-partition methods such as 3D-SPIHT which has been used for video compression. Another example, when the input image is unsymmetrical such as 16x512 image block even only one line image content, which is very common in hardware design, because it means the larger final chip die size for the many line buffers. But, the traditional SPIHT codec can acquire the best compression performances only for the image is symmetrical in horizontal and vertical dimension. To the above specific applications, I will discuss several kinds of modified SPIHT in this chapter, most of them are the author’s newly research result. In the following section, We will give a simple description about the traditional SPIHT codec, then, we will take the polarimetric SAR intensity image compression as example and give some specific compression method for multi-channel image compression. Certainly, before encoding for the wavelet coefficients, we need a 3D matrix transform to remove the information redundancy among the image plane and in each image plane. For the unsymmetrical image compression used in hardware design, an unsymmetrical SPIHT codec is detailed addressed, at the time, its specific case for only 1 line image compression, 1D SPIHT codec is also given.

We proposed a composite adaptive technique for color image compression. We apply a mapping transform from (R, G, B) space to (Y<SUB>d</SUB>, C<SUB>r</SUB>, C<SUB>b</SUB>) to represent the image as luminance image. The proposed compression technique is based on discrete cosine transform (DCT) with block size of 8 by 8 pixels. The proposed adaptive DCT technique depends on the maximum luminance value and the difference between this maximum and the minimum one in each block. This will help to select one quantization matrix out of 16 different matrices. The quantization matrices represent the whole range of types of the blocks. As an error correction step we create a 3D matrix that represents the whole image. The X axis is the number of blocks per row, the Y axis is the number of blocks per column and the Z axis is the 256 colors of the image. This will replace the coding of C<SUB>r</SUB>, C<SUB>b</SUB> components and it will solve the problem is case where the luminance value represents more than one color. The technique achieves a high compression ratio in the range of 0.65 bpp for Y<SUB>d</SUB> component of the smooth image to 1 bpp for detailed image in addition to overheads in the range from 0.15 to 0.25 bpp for the error correction matrix. The technique gives a higher compression ratio compared ratio compared to the well known technique for standard images, such as Lenna, while maintaining the same quality.

In this paper, we propose a scheme can be divided into two parts: one is about robust embedding of dual visual watermarks using DWT and Singular Value Decomposition (SVD), the other part is about tamper detection and self-recovery algorithm of color image. We stress on the latter part. Dual visual watermarks are original image watermark which is color scale image the same as original image and ownership watermark which is gray scale image, respectively. Both of them are embedded into original image using DWT-SVD to prove robustness. For recovery signal embedding, luminance signal and chrominance signal of original image were embedded into surplus chrominance space of original image using matrix transpose replacement embedding method. For watermark extraction scheme: firstly, threshold value would be set to find tampered region. Then we extract the recovery signals to construct recovery information. Lastly we replace tampered regions with recovery information based on threshold value. We can extract the original image watermark and ownership watermark from recovery image. Experiment demonstrates that the proposed scheme is robust to a wide range of attacks and is good at tampered detection as well as self- recovery of tampered images.