Temporally Consistent Tone Mapping of Images and Video Using Optimal K-means Clustering

The field of high dynamic range imaging addresses the problem of capturing and displaying the large range of luminance levels found in the world, using devices with limited dynamic range. In this paper we present a novel tone mapping algorithm that is based on K-means clustering. Using dynamic programming we are able to not only solve the clustering problem efficiently, but also find the global optimum. Our algorithm runs in $$\hbox {O}(N^2K)$$O(N2K) for an image with N input luminance levels and K output levels. We show that our algorithm gives comparable results to state-of-the-art tone mapping algorithms, but with the additional large benefit of a minimum of parameters. We show how to extend the method to handle video input. We test our algorithm on a number of standard high dynamic range images and video sequences and give qualitative and quantitative comparisons to a number of state-of-the-art tone mapping algorithms.