Research and Advances
Artificial Intelligence and Machine Learning

Optimal histogram matching by monotone gray level transformation


This paper investigates the problem of optimal histogram matching using monotone gray level transformation, which always assigns all picture points of a given gray level i to another gray level T(i) such that if i ≥ j, then T(i) ≥ T(j). The objective is to find a transformed digital picture of a given picture such that the sum of absolute errors between the gray level histogram of the transformed picture and that of a reference picture is minimized. This is equivalent to placing k1 linearly ordered objects of different sizes one by one into k2 linearly ordered boxes of assorted sizes, such that the accumulated error of space underpacked or overpacked in the boxes is minimized; the placement function is monotonic, which ensures a polynomial time solution to this problem. A tree search algorithm for optimal histogram matching is presented which has time complexity O(k1 × k2). If the monotone property is dropped, then the problem becomes NP-complete, even if it is restricted to k2 = 2.

View this article in the ACM Digital Library.

Join the Discussion (0)

Become a Member or Sign In to Post a Comment

The Latest from CACM

Shape the Future of Computing

ACM encourages its members to take a direct hand in shaping the future of the association. There are more ways than ever to get involved.

Get Involved

Communications of the ACM (CACM) is now a fully Open Access publication.

By opening CACM to the world, we hope to increase engagement among the broader computer science community and encourage non-members to discover the rich resources ACM has to offer.

Learn More