Adaptive wavelet eye-gaze based video compression

We present a novel approach to compression of video frames based on the foveation behavior of the human visual system (HSV). Eye fixations on a video frame, as depicted by eye-gaze trace data, define an imaginary region of interest. The perceived resolution of the frame by the human eye depends totally on this eye-gaze (fixation) point. The resolution, then, decreases dramatically with the distance from the fovea. This behavior of the HSV has gained interest in the image and video processing area recently especially in compression of images or video frames. We present an approach where eye-gaze trace data are intergral to the compression process which has demonstrated its usefulness in yielding high compression performance. We partition a video frame into three regions: the inner-most incudes a point of eye-gaze for which we apply lossless compression; an outer region which encompasses the first and for which we apply visually lossless (near-lossless) compression, and finally an outmost region where lossy compression is applied. Because of its low computational complexity, we use the Haar wavelet transform. Preliminary results are promising and show improvement over other methods which are mainly full frame based