The substantial gap between the EOVD data collection rate and the transmission bandwidth has greatly restricted remote surveillance applications in smart cities. Applying the proposed coding method to EOVD will facilitate remote surveillance, which can foster the development of online smart city applications. Bitrate savings reached 25–35% when applied to satellite video data with arbitrarily collected reference images. Experimental results show that encoding UAV video clips with the proposed method saved an average of more than 54% bits using references generated under the same conditions. Next, it proposes a method of generating references for encoding current video and develops the encoding and decoding framework for EOVD compression. Then, it analyzes the factors affecting the variations in the image representations of the background. First, this study proposes eliminating LBR by creating a long-term background referencing library (LBRL) containing high-definition geographically registered images of an entire area. Eliminating LBR improves EOVD coding efficiency considerably. LBR is induced by the repetition of static landscapes across multiple video clips and becomes significant as the number of video clips shot of the same area increases. Long-term background redundancy (LBR) (in contrast to local spatial/temporal redundancies in a single video clip) is a new form of redundancy common in Earth observatory video data (EOVD). High efficiency compression of the data is in high demand. However, the real-time utilization of remotely sensed surveillance data via unmanned aerial vehicles (UAVs) or video satellites is hindered by the considerable gap between the high data collection rate and the limited transmission bandwidth. City surveillance enables many innovative applications of smart cities.
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