Adaptive Spectral Projection | SIGGRAPH Asia 2015

Isaac Kauvar, Samuel J. Yang, Liang Shi, Ian McDowall, Gordon Wetzstein

A computational projection system that provides content-adaptive color primaries, which are optimized in a perceptually-optimal manner.



Fundamental display characteristics are constantly being improved, especially resolution, dynamic range, and color reproduction. However, whereas high resolution and high-dynamic range displays have matured as a technology, it remains largely unclear how to extend the color gamut of a display without either sacrificing light throughput or making other tradeoffs. In this paper, we advocate for adaptive color display; with hardware implementations that allow for color primaries to be dynamicallychosen, an optimal gamut and corresponding pixel states can be computed in a content-adaptive and user-centric manner. We build a flexible gamut projector and develop a perceptually-driven optimization framework that robustly factors a wide color gamut target image into a set of time-multiplexed primaries and corresponding pixel values. We demonstrate that adaptive primary selection has many benefits over fixed gamut selection and show that our algorithm for joint primary selection and gamut mapping performs better than existing methods. Finally, we evaluate the proposed computational display system extensively in simulation and, via photographs and user experiments, with a prototype adaptive color projector.


  • technical paperĀ (pdf)
  • technical paper supplement (pdf)
  • presentation slides (slideshare)



I. Kauvar, S. Yang, L. Shi, I. McDowall, G. Wetzstein. “Adaptive Color Display via Perceptually-driven Factored Spectral Projection”, ACM SIGGRAPH Asia (Transactions on Graphics), 2015.


author = {I. Kauvar and S. Yang and L. Shi and I. McDowall and G. Wetzstein},
title = {{Adaptive Color Display via Perceptually-driven Factored Spectral Projection}},
journal = {ACM Trans. Graph. (SIGGRAPH Asia)},
year = {2015},


A summary of the project: (top left) hardware prototype of flexible gamut projector. (middle) prototype in action. (bottom) convergence of perceptual non-negative matrix factorization (PNMF) algorithm to final gamut selection and gamut mapping, shown here in CIEXYZ color space.


PNMF algorithm performs better than legacy algorithms. (column #1) recovered image, clipped to sRGB color gamut. (columns #2,3,4) error in CIELAB76 color space between recovered image and target multispectral image. PNMF yields the lowest error. (columns #5,6) final color gamuts determined by each algorithm, shown as 2D and 3D representations. (column #7) convergence of each algorithm.


Schematic of formulation as joint gamut selection and gamut mapping as a non-negative matrix factorization problem. Gamut selection corresponds to choosing the linear combination of LED intensities corresponding to each primary color for each time point; gamut mapping consists of choosing the grayscale value of each pixel on the DMD to be displayed at each time point corresponding to a primary color.