PERCEPTUAL VIDEO CODING WITH BLOCK-LEVEL STAIRCASE JUST NOTICEABLE DISTORTION

Perceptual video coding (PVC) is able to improve video compression efficiency by employing just noticeable distortion (JND) models. However, there are limitations of conventional JND models on simulating complex human visual system. To address this issue, a novel PVC framework is proposed in this work, in which a JND model based on staircase perceptual characteristics is designed to calculate block-level JND (BLJND) levels and a convolutional neural network based predictive model is developed to predict BLJND levels for video coding. Experimental results demonstrate that the proposed PVC framework is effective and robust in terms of video compression efficiency and subjective video quality.