A new method for video stream brightness stabilization: algorithms and assessment of effectiveness

Abstract

Subject of study. In this study, for the first time, an original method for estimating the change in the brightness of video data under the influence of changes in the lighting conditions of the scene and external noise is proposed. Algorithms for stabilizing the brightness of the video data are also proposed. An objective assessment of the quality of video data because of pre-processing is given. The purpose of this study is to create a methodology for analyzing the variability of video data parameters under the influence of negative factors and to develop effective algorithms for stabilizing the parameters of the received video stream. A thorough verification of the reliability of the method on real video recordings made under various conditions is given. Objectives: To determine the most universal, resistant to external influences, and informative indicator necessary for an objective assessment of the quality of video data under various shooting conditions and scene lighting features; develop and programmatically implement algorithms for stabilizing video parameters based on modern programming tools. Research methods. Statistical analysis and pre-processing of video stream parameters as a random spatio-temporal process, algorithms for processing video data by digital filtering, and adaptive stabilization of video stream parameters. Research results. It has been proposed and experimentally proven that the optimal indicator of video stream quality is the average frame brightness (AFB). An algorithm for spatiotemporal processing of video data is proposed that generates a sequence of AFB values from the original video stream. This paper also proposes digital algorithms for filtering and stabilizing the brightness of a video stream and investigates the effectiveness of their application. Conclusions. The scientific novelty of the results obtained lies in a new method for analyzing and evaluating the parameters of video surveillance data and algorithms for filtering and stabilizing the brightness of the video stream. The performance of the proposed algorithms was tested on real data. The algorithms were implemented in the Python software environment using the functions of the OpenCV library.