The human visual system (HVS) tries to pick salient areas to scale back process efforts. visual attention attempt to predict the vital areas of videos or images viewed by the human eye. Such models, is applied to areas like computer work, video coding, and quality assessment. though many models are projected, few of them is applicable to high dynamic range (HDR) image content, and no work has been done for HDR videos. Moreover, the drawback within the existing models is that they can’t simulate the characteristics of HVS under the wide luminous range found in HDR content. This paper overcome these problems by the procedure approach to model the bottom-up visual saliency for HDR input by combining spatial and temporal visual features. An analysis of eye movement knowledge ensure the effectiveness of the proposed model. Comparisons using 3 well-known quantitative metrics show that the proposed model substantially improves predictions of visual attention for HDR content.