Abstract
This study investigates the design of High Secure Video Encryption with self-integrated compression and denoising block. Depending on the magnitude of the wavelet co-efficient and noise variance for each co-efficient in the hybrid compressor is estimated based on the sub band using the Maximum Likelihood Estimator and Maximum a Posterior (MAP) estimator. The Adaptive threshold is applied to each sub band co-efficient except the approximation sub band of the wavelet. The resultant will yield a superior quality by removing the noise in the video signals obtained during acquisition or transmission of video. The need of an efficient compression Block in the Video encryption is because the original video consumes more bit rate for transmission and storage. The Proposed algorithm is tested with test video considering the PSNR, MSE for evaluation.
Highlights
Video Encryption is a key research area for researchers due to the increase in Amount of Information Transmitted through Video signals in the unsecured channel
The Algorithm that is applied on the Information at the sender end before transmitting in the unsecured channel should be able to protect the information from these attacks
Our previous paper concentrates mainly on Video Encryption using Chaotic Neural Network and we had provided an external Compression block in the pre-processing step to reduce the amount of information transmitted through the channel
Summary
Video Encryption is a key research area for researchers due to the increase in Amount of Information Transmitted through Video signals in the unsecured channel. Our previous paper concentrates mainly on Video Encryption using Chaotic Neural Network and we had provided an external Compression block in the pre-processing step to reduce the amount of information transmitted through the channel. In this study we had proposed an efficient hybrid algorithm for video signals that will denoise, compress and encrypt the video signal using the wavelet and Chaotic Neural Network. In the literature, Donoho and Johnston (1994, 1995) suggested to use the global threshold uniformly throughout the entire wavelet decomposition tree for efficient performance. It is simple by nature, the amount of noise removed is not satisfactory. Step 4: In each of the other high sub bands, coefficients are assigned either significant or insignificant classes depending on the magnitude of their estimated parent relative to the significance threshold T, where T is given by:
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