Compression of HD videos by a contrast-based human attention algorithm
The emergence of social networks combined with the prevalence of mobile technology has led to an increasing demand of high definition video transmission and storage. One of the challenges of video compression is the ability to reduce the video size without significant visual quality loss. In this paper, we propose a new method that achieves compression reduction levels ranging from 2.6% to 16.9% while maintaining or improving subjective quality. Precisely, our approach is a saliency-aware mechanism that predicts and classifies regions-of-interests (ROIs) of a typical human eye gaze according to the static attention model (SAM) from the human visual system (HVS). We coin the term contrast human attention regions of interest (Contrast-HAROIs) to refer to those identified regions. Finally, we reduce the data load of those non Contrast-HAROIs via a smoothing spatial filter. Experimental results carried on eight sequences show that our technique reduces the size of HD videos further than the standard H.264/AVC. Moreover, it is in average 30% times faster than another saliency and motion aware algorithm.