Video Quality Assessment Metrics for Infrared Video Frames using Different Edge Detection Algorithms

Infrared signal processing algorithms and architectures are important for the development of a high performance infrared imaging systems. Infrared signal processing deals with two types of processing sensor signal processing and infrared image processing for contrast enhancement and target detection. The sensor signal processing primarily deals with non uniformity correction for infrared sensors. Assessing the quality of the Infrared video frame is a complex and firm process since human’s opinion is affected by physical and psychological parameters. Infrared Video frame quality assessment plays an important role in the field of video processing. Structural Similarity Index has become a standard among image quality metrics. It is a framework for quality assessment based on the degradation of structural information of video frame. In this paper SSIM values are computed and compared for Infrared video frames by applying different edge detection approaches by using different color models to assess the quality of the frames. Experimental results comparisons demonstrate the effectiveness of the proposed method.