Video Frame Illumination Inconsistency Reduction using CLAHE with Kekre’s LUV Color Space

Abstract

Visual frame quality is of utmost significance and is relevant in numerous computer vision applications such as object detection, video surveillance, optical motion capture, multimedia and human computer interface. Under controlled or uncontrolled environment, the video visual frame quality gets affected due to illumination variations. This may further hamper the interpretability and may lead to significant loss of information for background modeling. An excellent background model can enhance good visual perception. In this work, local enhancement technique with improved background modeling, Clipped Adaptive Histogram Equalization (CLAHE) is explored with Kekre’s LUV color space to reduce the illumination inconsistency especially with darker set of video frames and a significant improved average entropy of 7.7225 has been obtained, which is higher than the existing explored variations of CLAHE. .