Adaptive block-size transform based just-noticeable difference profile for videos

In this paper, we propose a novel adaptive block-size transform (ABT) based just-noticeable difference (JND) model for videos. Firstly, the ABT-based spatial JND profile is extended to spatial-temporal JND model for videos by considering temporal contrast sensitivity function (TCSF), eye movement, and the motion information of the objects in video sequence. Furthermore, a metric named motion characteristics distance (MCD) is proposed to depict the motion characteristics similarity between a macroblock and its corresponding sub-blocks. Based on the proposed MCD and the obtained spatial image content information, a novel balanced strategy is proposed to determine which transform size is employed to generate the resulting JND model. Experimental results have demonstrated that our proposed scheme could tolerate more distortions while preserving better perceptual quality than other JND profiles, which means that the proposed model consists well with human vision system (HVS). Moreover, for the balanced strategy, experiments have shown that temporal motion characteristics accord very well with the spatial image content information, which has demonstrated the efficiency of our proposed balanced strategy.