Data Hiding Scheme for Video Watermarking Using Horizontal and Vertical Coefficients of Single Level Discrete Wavelet Transform Method

Author(s):  
J. Jerisha Liby ◽  
T. Jaya

This paper proposes a new watermarking algorithm based on a single-level discrete wavelet transform (DWT). This method initially chooses ‘[Formula: see text]’ number of carrier frames to hide the data. After estimating the carrier frames, each frame is separated into RGB frames. Each R, G, and B frames are decomposed using a single-level DWT. The horizontal and vertical coefficients are selected to embed the watermark information since small changes in the horizontal and vertical coefficients do not highly affect the quality of the video frame. The watermark image pixels are shuffled using a predetermined key before embedding. The shuffled pixels are converted to binary, and they are grouped into three data matrices. Each data matrix is embedded in horizontal and vertical coefficients of the R, G and B frames of the video frame. After embedding the data, the watermarked video is reconstructed using the original approximation coefficients, the embed coefficients, and the original diagonal coefficients. During the extraction process, the watermark is extracted from the horizontal and vertical coefficients of the watermarked video. Experimental result reveals that the proposed method outperforms other related methods in terms of video quality and structural similarity index measurement.

2015 ◽  
Vol 81 ◽  
pp. 56-64 ◽  
Author(s):  
U. Rajendra Acharya ◽  
K. Sudarshan Vidya ◽  
Dhanjoo N. Ghista ◽  
Wei Jie Eugene Lim ◽  
Filippo Molinari ◽  
...  

2013 ◽  
Vol 284-287 ◽  
pp. 2402-2406 ◽  
Author(s):  
Rong Choi Lee ◽  
King Chu Hung ◽  
Huan Sheng Wang

This thesis is to approach license-plate recognition using 2D Haar Discrete Wavelet Transform (HDWT) and artificial neural network. This thesis consists of three main parts. The first part is to locate and extract the license-plate. The second part is to train the license-plate. The third part is to real time scan recognition. We select only after the second 2D Haar Discrete Wavelet Transform the image of low-frequency part, image pixels into one-sixteen, thus, reducing the image pixels and can increase rapid implementation of recognition and the computer memory. This method is to scan for car license plate recognition, without make recognition of the individual characters. The experimental result can be high recognition rate.


2010 ◽  
Vol 139-141 ◽  
pp. 2029-2032
Author(s):  
Dong Cao ◽  
Jian Wei Ye ◽  
Jun Yi ◽  
Wen Jie Ruan ◽  
Chong Chen

The human pulse-condition diagnosis is an important part of the traditional Chinese medicine (TCM) which is difficult to recognize accurately by doctor’s subjective experience. Objective identification of pulse-conditions has important meanings for modernization of TCM. In this paper human pulse-condition system transfer function model and model parameter estimation were introduced, which are used to construct four kinds of typical pulse-conditions simulation signals. There are normal pulse, taut pulse, slippery pulse and thready pulse. And then, discrete wavelet transform for extracting the multi-scale energy characteristics and wavelet packet decomposition for extracting the multi-band energy characteristics are proposed so as to recognize the pulse-conditions simulation signals. The results show that the recognition effect of discrete wavelet transform method is better. Moreover, the data features of characteristic parameters demonstrate the reality of simulation signals.


2017 ◽  
Vol 2017 ◽  
pp. 1-13
Author(s):  
Shanshan Chen ◽  
Bensheng Qiu ◽  
Feng Zhao ◽  
Chao Li ◽  
Hongwei Du

Compressed sensing (CS) has been applied to accelerate magnetic resonance imaging (MRI) for many years. Due to the lack of translation invariance of the wavelet basis, undersampled MRI reconstruction based on discrete wavelet transform may result in serious artifacts. In this paper, we propose a CS-based reconstruction scheme, which combines complex double-density dual-tree discrete wavelet transform (CDDDT-DWT) with fast iterative shrinkage/soft thresholding algorithm (FISTA) to efficiently reduce such visual artifacts. The CDDDT-DWT has the characteristics of shift invariance, high degree, and a good directional selectivity. In addition, FISTA has an excellent convergence rate, and the design of FISTA is simple. Compared with conventional CS-based reconstruction methods, the experimental results demonstrate that this novel approach achieves higher peak signal-to-noise ratio (PSNR), larger signal-to-noise ratio (SNR), better structural similarity index (SSIM), and lower relative error.


In today’s era use of digital media is most popular way of communication. Digital media covers images, videos and animations available online. The easy methods of accessing, copying and editing digital media have made them more popular. With several advantages these easy methods of copying and editing data have created some big issues like ownership identification. This increases the demand of protecting online digital media. Watermarking is solution of such problem. In this work, a block-based method has been proposed for video watermarking that uses a key at the time of embedding and extraction. Some frames are selected from the video according to a key. Watermark is embedded on the selected frames after dividing into parts called blocks. Each part of the watermark is embedded in one selected frame of the video. This method increases the security of the system as the complete watermark cannot be extracted without knowing the positions of watermarked frames and the position of the block in that frame. Watermarking is performed in the Discrete Wavelet Transform domain after scaling of watermark data. To show the authenticity of proposed scheme various attacks are applied on different watermarked video frames and extracted watermark results are shown under different tables.


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