Fractal video compression for IOT-based smart cities applications using motion vector estimation

Recently, Internet of the things (IoTs) devices have been used highly in surveillance applications in smart environments, cities, and buildings. Most of the surveillance data recorded by the IoT devices is in the form of video and needs to be transmitted to the admin or server by video streaming technology. Fractal video compression uses a self-similarity concept for video compression, meaning the fractal image contains self-similarity of itself that is explained and denoted by change. Computational cost is more in fractal video coding; different methods have been developed to reduce this computational cost. The proposed work's main objective is to implement a video streaming approach by combining the Diamond-search-pattern- block-matching motion estimation algorithm and hash-based fractal video compression algorithm to reduce the encoding time. It improves both the delay and security of the block matching system and the developed approach measures based on evaluation parameters: Peak signal-to-noise ratio (PSNR), encoding time, decoding time, Mean squared error (MSE), and Compression ratio for Smart City Applications. Compared to other algorithms, the IoTs video data is streamed efficiently in a smart city to analyse the abnormalities.