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K-means clustering based tone-mapping operator for high dynamic range video

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thesis
posted on 22.05.2021, 14:40 by Negar Taherian
The field of high dynamic range (HDR) imaging deals with capturing the luminance of a natural scene, usually varying between 10−3 to 105 cd/m2 and displaying it on digital devices with much lower dynamic range. Here, we present a novel tone mapping algorithm that is based on K-means clustering. Our algorithm takes into account the color information within a frame and using k-means clustering algorithm it builds clusters on the intensities within an image and shifts the values within each cluster to a displayable dynamic range. We also implement a scene change detection to reduce the running time of our algorithm by using the cluster information from the previous frame for frames within the same scene. To reduce the flicker effect, we proposed a new method that multiplies a leaky integer to the centroid values of our clustering results. Our algorithm runs in O( N logK + K logK ) for an image with N input luminance levels and K output levels. We also show how to extend the method to handle video input. We display that our algorithm gives comparable results to state-of-the- art tone mapping algorithms. We test our algorithm on a number of standard high dynamic range images and video sequences and provide qualitative and quantitative comparisons to a number of state-of-the-art tone mapping algorithms for videos.

History

Language

eng

Degree

Master of Applied Science

Program

Electrical and Computer Engineering

Granting Institution

Ryerson University

LAC Thesis Type

Thesis