This paper proposes a novel framework for enhancing head-mounted eye tracking data analysis by utilizing state-of-art computer vision techniques. Recently, the development of mobile eye tracking sensors allowed researchers to explore more comprehensive eye movements in an unobtrusive environment. It opened a door that stationary eye tracking devices could never approach. Literature demonstrates applications of mobile eye tracking technology to fields such as psychology, education and learning, usability, marketing, and medical diagnostics. The eye tracking research community is interested in analyzing the details of what and where a person is looking at using large-scale head-mounted eye tracking data. We formulated this problem to be eye tracking video processing, which can be resolved by locating at region-of-interests (ROI) based on fixation location, cropping and zooming in the ROI and enhancing the partial image by super-resolution image transformation. Experimental results and evaluation using image quality measurements show the effectiveness, efficiency, and robustness of the proposed prototype system. Furthermore, we discuss and demonstrate potential real-time applications using the proposed framework with emphasis on using an Augmented Reality (AR) headset with eye tracking capabilities.