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General Information
    • ISSN: 2010-3700 (Online)
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.

IJMLC 2019 Vol.9(4): 393-400 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.4.816

Retrieval of Color Space Conversion Matrix via Convolutional Neural Network

Larry Pearlstein, Alexander Benasutti, Skyler Maxwell, Matthew Kilcher, Jake Bezold, and Warren Seto
Abstract—The problem of proper identification of the color space associated with digital luma and chroma data has been widely reported by video processing professionals. The problem arises from confusing, and sometimes conflicting, statements regarding color space usage and description. Although standards allow for the carriage of descriptive metadata regarding color space, some applications do not require the presence of such metadata. Those standards typically recite assumptions on color space that should be followed in the absence of embedded color space descriptors. The unfortunate result of this approach is a state of confusion in the industry, and consequently the possibility of errors in rendering the output of decoded video and images. Our work represents the first known attempt to determine color space directly from luma/chroma pixel data, and provides an alternative to sole reliance on potentially missing or incorrect metadata, or weakly followed defaults. Although a color space is defined by many parameters, such as primary chromaticity, transfer characteristics and matrix coefficients, we chose to focus on determining which standard for matrix coefficients had been used to create a given luma/chroma image. We addressed the problem via deep convolutional neural networks (DCNNs), trained on millions of images. Our results are encouraging, and suggest that DCNNs can be used to solve this ill-posed problem.

Index Terms—BT.601, BT.709, color space, convolutional, neural network.

L. Pearlstein, S. Maxwell (email), A. Benasutti (email), M. Kilcher (email), J. Bezold (email) are with Department of Electrical and Computer Engineering, The College of New Jersey, Ewing, NJ, 08628 USA (e-mail: pearlstl@tcnj.edu, maxwels2@tcnj.edu, benasua1@tcnj.edu, kilchm2@tcnj.edu, bezoldj1@tcnj.edu).
W. Seto was with Department of Electrical and Computer Engineering, The College of New Jersey, Ewing, NJ, 08628 USA (e-mail setow1@tcnj.edu).

[PDF]

Cite: Larry Pearlstein, Alexander Benasutti, Skyler Maxwell, Matthew Kilcher, Jake Bezold, and Warren Seto, "Retrieval of Color Space Conversion Matrix via Convolutional Neural Network," International Journal of Machine Learning and Computing vol. 9, no. 4, pp. 393-400, 2019.

Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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E-mail: ijmlc@ejournal.net