Unequal Error Protection (UEP) Image Transmission System with Lifting Wavelet Transform (LWT) Based Reed Solomon (RS) Coded Cooperation Scheme

2011 ◽  
Vol E94-B (9) ◽  
pp. 2592-2599
Author(s):  
A. H. M. ALMAWGANI ◽  
M. F. M. SALLEH
2013 ◽  
Vol 462-463 ◽  
pp. 288-293
Author(s):  
Wei Song ◽  
Wen Bing Fan

To improve PSNR and coding efficiency, the paper proposes a method by improving algorithm according to image compression technology to pledge the real time of the image transmission and gain the high compression ratio under the image quality. The improved SPIHT image coding algorithm based on fast lifting wavelet transform presents fast lifting wavelet transform to improve transform course, because of many consecutive zero appearing in SPIHT quantification coding, adopting the simultaneous encoding of entropy and SPIHT. Entropy coding adopts run-length-changeable coding. Proved by the experiment, the method could achieve expected purpose, can apply in the image data transmission and storage of remote image surveillance systems.


2015 ◽  
Vol 10 (11) ◽  
pp. 1127
Author(s):  
Nidaa Hasan Abbas ◽  
Sharifah Mumtazah Syed Ahmad ◽  
Wan Azizun Wan Adnan ◽  
Abed Rahman Bin Ramli ◽  
Sajida Parveen

Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.


Author(s):  
Rahul Dixit ◽  
Amita Nandal ◽  
Arvind Dhaka ◽  
Vardan Agarwal ◽  
Yohan Varghese

Background: Nowadays information security is one of the biggest issues of social networks. The multimedia data can be tampered with, and the attackers can then claim its ownership. Image watermarking is a technique that is used for copyright protection and authentication of multimedia. Objective: We aim to create a new and more robust image watermarking technique to prevent illegal copying, editing and distribution of media. Method : The watermarking technique proposed in this paper is non-blind and employs Lifting Wavelet Transform on the cover image to decompose the image into four coefficient matrices. Then Discrete Cosine Transform is applied which separates a selected coefficient matrix into different frequencies and later Singular Value Decomposition is applied. Singular Value Decomposition is also applied to the watermarking image and it is added to the singular matrix of the cover image which is then normalized followed by the inverse Singular Value Decomposition, inverse Discrete Cosine Transform and inverse Lifting Wavelet Transform respectively to obtain an embedded image. Normalization is proposed as an alternative to the traditional scaling factor. Results: Our technique is tested against attacks like rotation, resizing, cropping, noise addition and filtering. The performance comparison is evaluated based on Peak Signal to Noise Ratio, Structural Similarity Index Measure, and Normalized Cross-Correlation. Conclusion: The experimental results prove that the proposed method performs better than other state-of-the-art techniques and can be used to protect multimedia ownership.


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