Nonuniform lattice regression for modeling the camera imaging pipeline

Hai Ting Lin, Zheng Lu, Seon Joo Kim, Michael S. Brown

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

We describe a method to construct a sparse lookup table (LUT) that is effective in modeling the camera imaging pipeline that maps a RAW camera values to their sRGB output. This work builds on the recent in-camera color processing model proposed by Kim et al. [1] that included a 3D gamut-mapping function. The major drawback in [1] is the high computational cost of the 3D mapping function that uses radial basis functions (RBF) involving several thousand control points. We show how to construct a LUT using a novel nonuniform lattice regression method that adapts the LUT lattice to better fit the 3D gamut-mapping function. Our method offers not only a performance speedup of an order of magnitude faster than RBF, but also a compact mechanism to describe the imaging pipeline.

Original languageEnglish
Title of host publicationComputer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings
Pages556-568
Number of pages13
EditionPART 1
DOIs
Publication statusPublished - 2012 Oct 30
Event12th European Conference on Computer Vision, ECCV 2012 - Florence, Italy
Duration: 2012 Oct 72012 Oct 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7572 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other12th European Conference on Computer Vision, ECCV 2012
CountryItaly
CityFlorence
Period12/10/712/10/13

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Lin, H. T., Lu, Z., Kim, S. J., & Brown, M. S. (2012). Nonuniform lattice regression for modeling the camera imaging pipeline. In Computer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings (PART 1 ed., pp. 556-568). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7572 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-33718-5_40