Paper Info
Title
Temporally Consistent Tone Mapping of Images and Video Using Optimal K-means Clustering.
Abstract
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.
Year
DOI
Venue
2017
10.1007/s10851-016-0677-1
Journal of Mathematical Imaging and Vision
Keywords
Field
DocType
High dynamic range images,High dynamic range video,Clustering,Dynamic programming
Computer vision,Dynamic programming,k-means clustering,Dynamic range,Computer science,Tone mapping,Artificial intelligence,Cluster analysis,Luminance,High dynamic range,High-dynamic-range imaging
Journal
Volume
Issue
ISSN
57
2
0924-9907
Citations 
PageRank 
References 
1
0.34
32
Authors
1
Name
Order
Citations
PageRank
Magnus Oskarsson119622.85