PROJECT TITLE :

Attention Driven Foveated Video Quality Assessment - 2014

ABSTRACT:

Distinction sensitivity of the human visual system to visual stimuli can be considerably laid low with several mechanisms, e.g., vision foveation and a focus. Existing studies on foveation based mostly video quality assessment solely take into consideration static foveation mechanism. This paper 1st proposes a sophisticated foveal imaging model to get the perceived representation of video by integrating visual attention into the foveation mechanism. For accurately simulating the dynamic foveation mechanism, a completely unique approach to predict video fixations is proposed by mimicking the essential functionality of eye movement. Consequently, an advanced distinction sensitivity function, derived from the attention driven foveation mechanism, is modeled and then integrated into a wavelet-based distortion visibility live to build a full reference attention driven foveated video quality (AFViQ) metric. AFViQ exploits adequately perceptual visual mechanisms in video quality assessment. Intensive evaluation results with respect to many publicly on the market eye-tracking and video quality databases demonstrate promising performance of the proposed video attention model, fixation prediction approach, and quality metric.