Video Shot Boundary Detection Using Dual Tree and Double Density Dual Tree Complex Wavelet Transform: A Comparison

Videos are everywhere these days-in thousands of scientific (e.g., astronomical, bio-medical), consumer, industrial, and artistic applications. Moreover they come in a wide range of the electromagnetic spectrum-from visible light and infrared to gamma rays and beyond. The ability to process video data is therefore an incredibly important skill to master for engineering/science students, software developers, and practicing scientists. Video processing continues to enable the multimedia technology revolution we are experiencing today. The innovation being done in multimedia streams from last few years necessitates the development of efficient and more effective methodologies for storing information related to video, audio, text etc. Video stream is segmented into its primary blocks for accessing content based video. The general objectives are to segment a given video sequence into its constituent shots, and to identify and classify the different shot transitions in the sequence. The video stream consists of a number of shots, each sequence of frames are represented using a single camera. Switching from one frame to another indicates the transition from a shot to the next one. Therefore, the detection of these transitions, known as scene change or shot boundary detection, is the first step in any video-analysis system. The basis of detecting shot boundaries in video sequences is the fact that frames surrounding a boundary generally display a significant change in their visual contents. Many techniques for shot boundary detection are available, but the major challenges to them is Detection of gradual transition and the elimination of disturbances caused by illumination change or fast object and camera motion as well as the execution time. On the other hand, efficiency is also crucial due to the voluminous amounts of information found in video streams. In this research paper, a new robust and efficient algorithm capable of detecting transitions in AVI videos is developed