Human attention region-of interest in I-frame for video coding

We propose a new scheme that exploits characteristics of motion vectors combined with luminance contrast to automatically detect human attention regions of interest (HAROIs) in every I-frame or intra-coded blocks in a group of pictures (GOP). These HAROIs can then be used for adaptive quantization. Motion vectors information is collected before the encoding phase. Our ultimate goal is to obtain a generic HAROI detection scheme of relatively low additional complexity per I-frame that can be used to improve compression while maintaining video quality and perception. Experimental results show consistency between actual human attention regions and the most relevant regions identified by our algorithm. Our algorithm also produces better compression of I-frames while improving both peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM).