Image Enhancement In Video Analytics Systems

Abstract

Recently, video analytics systems are rapidly evolving, and the effectiveness of their work depends primarily on the quality of operations at the initial level of the entire processing process, namely the quality of segmentation of objects in the scene and their recognition. Successful performance of these procedures is primarily due to image quality, which depends on many factors: technical parameters of video sensors, low or uneven lighting, changes in lighting levels of the scene due to weather conditions, time changes in illumination, or changes in scenarios in the scene. This paper presents a new, accurate, and practical method for assessing the improvement of image quality in automatic mode. The method is based on the use of nonlinear transformation function, namely, gamma correction, which reflects properties of a human visual system, effectively reduces the negative impact of changes in scene illumination and due to simple adjustment and effective implementation is widely used in practice. The technique of selection in an automatic mode of the optimum value of the gamma parameter at which the corrected image reaches the maximum quality is developed.