Bayesian Methods for Radiometric Calibration in Motion Picture Encoding Workflows

The accurate estimation of a camera response function (CRF) allows for proper encoding of camera exposures into motion picture post-production workflows like the Academy Color Encoding System (ACES), helping minimize noncreative manual adjustments. Although there are well-known standard CRFs implemented in typical video camera workflows, motion picture camera workflows and new high dynamic range (HDR) workflows have introduced new standard CRFs, as well as custom and proprietary CRFs. Current methods to estimate this function rely on the use of test charts, restrictive exposure and/or lighting conditions, or assume a simplistic model of the function’s shape. All of these methods become problematic and tough to fit into motion picture production and post-production workflows, where the use of test charts and varying camera or scene setups becomes impractical. We propose a method initially based on the work of Lin, Gu, Yamazaki, and Shum that considers edge color mixtures in an image or image sequence that are affected by the nonlinearity introduced by a CRF. This feature is then used in a Bayesian framework to estimate a posterior probability distribution function of the CRF model parameters approximated by a Markov Chain Monte Carlo (MCMC) algorithm, allowing for a more robust description of the CRF over methods like maximum likelihood (ML) and maximum a posteriori (MAP).