Post-processing approach for radiometric self-calibration of video

We present a novel data-driven technique for radiometric self-calibration of video from an unknown camera. Our approach self-calibrates radiometric variations in video, and is applied as a post-process; there is no need to access the camera, and in particular it is applicable to internet videos. This technique builds on empirical evidence that in video the camera response function (CRF) should be regarded time variant, as it changes with scene content and exposure, instead of relying on a single camera response function. We show that a time-varying mixture of responses produces better accuracy and consistently reduces the error in mapping intensity to irradiance when compared to a single response model. Furthermore, our mixture model counteracts the effects of possible nonlinear exposure-dependent intensity perturbations and white-balance changes caused by proprietary camera firmware. We further show how radiometrically calibrated video improves the performance of other video analysis algorithms, enabling a video segmentation algorithm to be invariant to exposure and gain variations over the sequence. We validate our data-driven technique on videos from a variety of cameras and demonstrate the generality of our approach by applying it to internet video.