The smart city concept has attracted high research attention in recent years within diverse\napplication domains, such as crime suspect identification, border security, transportation, aerospace,\nand so on. Specific focus has been on increased automation using data driven approaches, while\nleveraging remote sensing and real-time streaming of heterogenous data from various resources,\nincluding unmanned aerial vehicles, surveillance cameras, and low-earth-orbit satellites. One of\nthe core challenges in exploitation of such high temporal data streams, specifically videos, is the\ntrade-off between the quality of video streaming and limited transmission bandwidth. An optimal\ncompromise is needed between video quality and subsequently, recognition and understanding and\nefficient processing of large amounts of video data. This research proposes a novel unified approach\nto lossy and lossless video frame compression, which is beneficial for the autonomous processing\nand enhanced representation of high-resolution video data in various domains. The proposed fast\nblock matching motion estimation technique, namely mean predictive block matching, is based on\nthe principle that general motion in any video frame is usually coherent. This coherent nature of\nthe video frames dictates a high probability of a macroblock having the same direction of motion as\nthe macroblocks surrounding it. The technique employs the partial distortion elimination algorithm\nto condense the exploration time, where partial summation of the matching distortion between the\ncurrent macroblock and its contender ones will be used, when the matching distortion surpasses the\ncurrent lowest error. Experimental results demonstrate the superiority of the proposed approach\nover state-of-the-art techniques, including the four step search, three step search, diamond search,\nand new three step search.
Loading....