Novel video keyframe extraction using KPE vector quantization with assorted similarity measures in RGB and LUV color spaces

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 do need to get processed. This leads to redundant slow processing speed and complexity, time consumption. Video summarization using key frames can ease the speed up of video processing. In this paper, novel key frames extraction method is proposed with Kekere´s Proportionate Error (KPE) codebook generation techniques of vector quantization with ten different codebook sizes and two color spaces (RGB and KLUV). Experimentation done with help of the test bed of videos has shown that higher codebook sizes of KPE have given better completeness in key frame extraction for video summarization. The LUV color space with Euclidean Distance with 512 codebook size gives best performance. In square chord Distance, Mean Square Error and Euclidean Distance LUV color space gives better completeness than RGB color space for proposed KPE based video Key frame Extraction.