ABSTRACT:

In this paper, a new scaling-based image-adaptive watermarking system has been presented, which exploits human visual model for adapting the watermark data to local properties of the host image. Its improved robustness is due to embedding in the low-frequency wavelet coefficients and optimal control of its strengthfactor from HVS point of view. Maximum likelihood (ML) decoder is used aided by the channel side information. The performance of the proposed scheme is analytically calculated and verified by simulation. Experimental results confirm the imperceptibility of the proposed method and its higher robustness against attacks compared to alternative watermarking methods in the literature.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : Accurate and Robust Video Saliency Detection via Self-Paced Diffusion ABSTRACT: In order to estimate video saliency in the short term, traditional video saliency detection algorithms usually follow the common
PROJECT TITLE : Robust Lane Detection from Continuous Driving ScenesUsing Deep Neural Networks ABSTRACT: For autonomous vehicles and sophisticated driver assistance systems, lane recognition in driving scenes is a critical element.
PROJECT TITLE : Robust Unsupervised Multi-view Feature Learning with Dynamic Graph ABSTRACT: By modeling the affinity associations with a graph to lower the dimension, graph-based multi-view feature learning algorithms learn a
PROJECT TITLE : A Spatially Constrained Probabilistic Model for Robust Image Segmentation ABSTRACT: In probabilistic model based segmentation, the hidden Markov random field (HMRF) is used to describe the class label distribution
PROJECT TITLE : An Adaptive and Robust Edge Detection Method Based on Edge Proportion Statistics ABSTRACT: One of the most important preprocessing steps for high-level tasks in the field of image analysis and computer vision is

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry