Saliency-based parameter tuning for tone mapping

We present a saliency-based parameter tuning algorithm that can optimize the parameters of tone mapping operators automatically by minimizing the saliency distortion caused by the process of tone mapping. The algorithm employs an improved saliency detection model for HDR images, and the saliency distortion is quantified as the Kullback-Leibler divergence between the saliency distributions of the tone mapped images and those of the corresponding HDR images. We show that the minimization can be accomplished by employing an evolution strategy with individuals representing parameter settings and fitness values based on saliency distortion. The effectiveness of our algorithm is demonstrated through experiments using several tone mapping operators and test images.