Bottom-up visual saliency can be computed through information theoretic models but existing methods face significant computational challenges. Whilst nonparametric methods suffer from the curse of dimensionality problem and are computationally expensive, parametric approaches have the difficulty of determining the shape parameters of the distribution models. This paper makes two contributions to information theoretic based visual saliency models. First, we formulate visual saliency as center surround conditional entropy which gives a direct and intuitive interpretation of the center surround mechanism under the information theoretic framework. Second, and more importantly, we introduce a fast nonparametric multidimensional entropy estimation solution to make information theoretic-based saliency models computationally tractable and practicable in realtime applications. We present experimental results on publicly available eye-tracking image databases to demonstrate that the proposed method is competitive to state of the art.