Abstract: The main objective of this paper is to describe an efficient hardware-based concept of a digital video WM system which features low power consumption, efficient and low cost implementation, high processing speed, reliability and invisible, semi-fragile watermarking in compressed video streams. The presented WM system can be integrated with video compressor unit and it achieves performance that matches complex software algorithms within a simple efficient hardware implementation. The system also features minimum video quality degradation and can withstand certain potential attacks i.e. cover-up attack, cropping, segment removal on video sequences. The proposed WM system is implemented using Verilog hardware description language (HDL) synthesized into a field programming gate array (FPGA) and then experimented using a custom versatile breadboard for performance evaluation is describe through various research papers which give a vital review related to digital video watermarking system fo

• Publication Date: 28-Apr-2013
• DOI: 10.15224/978-981-07-6260-5-34
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Abstract: K-Nearest Neighbor (kNN) technique is used in different application areas, as it is very simple, highly efficient and effective. Its main advantage is simplicity but the disadvantages can’t be ignored. Hence many researchers proposed different forms of kNN technique under various situations. Broadly kNN techniques are categorized into structure based and non-structure based (structure less) techniques. The objective of this paper is to introspect the key idea, pros and cons and target data or application area behind every kNN technique. Principal Axix Tree (PAT), Orthogonal Structure Tree(OST) , Nearest Feature Line(NFL), Center Line (CL) ,k-d Tree, Ball Tree, Tunable NN etc. are structure based techniques whereas weighted kNN, Model based kNN, Ranked NN (RNN),Condensed NN, Reduced NN, Pseudo/Generalized NN, Clustered kNN (CkNN) , Mutual kNN ( MkNN), constrained RkNN etc. are structure less techniques developed on the basis of kNN. Structure based techniques reduces the computational c

• Publication Date: 28-Apr-2013
• DOI: 10.15224/978-981-07-6260-5-65
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• Downloads: 0