Applications like computer vision, multimedia, and remote sensing all heavily rely on the field of image and video processing. For analysing, modifying, and enhancing photos and movies, mathematical models are effective tools. The construction of mathematical models that are specifically suited for image and video processing jobs is examined in this research study. The paper outlines the fundamental ideas and procedures used to develop these models, emphasising their uses and benefits across a range of industries. It also includes case studies and experiments to show how well the suggested models work. The research develops and uses models in a methodical manner. In order to do this, it is necessary to identify the main issues with image and video processing, formulate mathematical models, and then rigorously assess these models using relevant benchmark datasets. The models are made to handle problems like video compression, object detection, motion estimation, noise reduction, and image enhancement. The suggested mathematical models integrate ideas from several branches of mathematics, including signal processing, linear algebra, optimisation, and statistical analysis. They use cutting-edge methods like deep learning, machine learning, and probabilistic modelling to conduct image and video processing tasks at the highest level of performance. Implemented and integrated into current image and video processing frameworks are the created mathematical models. The implementation includes designing effective algorithms, enhancing computational performance, and working with current hardware designs. The models are assessed quantitatively and qualitatively, and their effectiveness is contrasted with that of current state-of-the-art techniques.
Key words: Development , Mathematical , Models , Image , Video
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