Detecting salient objects in non-stationary video image sequence for analyzing user perceptions of digital video contents

The ubiquitous utilization of video applications in recent years has made research on video quality of experience paramount. Lack of sufficient bandwidth deters the effective transmission of raw video contents to users. This bandwidth challenge has given rise to encoders for compressing digital video contents for transmission over an internet protocol infrastructure. However, transmitting compressed video color images still has an intrinsic limitation of high bandwidth consumption. Simple linear iterative clustering algorithm was applied for binary segmentation of video color images to circumvent the challenge of efficiently transmitting video contents. Compressed binary segmented images are generally fast to transmit and require lower bandwidth consumption as opposed to compressed video color images. However, since color images contain more useful information than binary image counterparts, evaluation of binary segmentation results was performed using the mean opinion score metric to determine user quality of experience of the transmitted video contents. The practical application of our method will lead to the development of a novel encoder that can deliver binary video contents faster, hence solving the bandwidth hiccup.