Video key frame identification using Thepade´s Transform Error Vector Rotation algorithm with Haar transform and assorted similarity measures

In the current era, most of the digital information is in the form of multimedia with a giant share of videos. Videos do have audio and visual content where the visual content has number of frames put in a sequence. Most of the consecutive frames do have very little discriminative contents. In video summarization process, several frames containing similar information need to get processed, this leads to slower processing speed and higher complexity, consuming. Video more time summarization using key frames can ease the speed up of video processing. In this paper, novel key frames extraction method is proposed with Thepade´s Transform Error Vector Rotation (TTEVR) with Haar transform with ten different codebook sizes is proposed. Experimentation done with help of the test bed of videos has shown that higher codebook sizes of have given better completeness in key frame extraction for video summarization. Experimental results are discussed for video content summarization with five assorted similarity measures like Euclidean Distance, Canberra Distance, Square-Chord Distance, Mean Square Error, Sorensen Distance with proposed TTEVR using Haar transform.