Automatic, Viewing-Condition Dependent Contrast Grading based on Perceptual Models
Cameras automatically apply non-linear transformations to the sensor data, allowing for perceptually-uniform quantization suited to standard dynamic range displays in dim conditions. In the cinema industry, data is recorded in raw (linear) format and non-linearly corrected in post-production by a skilled technician who optimizes image appearance for cinema (dark) conditions. We propose a method that automatically performs this non-linear transformation taking into account the intended viewing conditions. It is based on visual perception models and produces results that look natural, without any spatio-temporal artifacts. User preference tests show that our method outperforms state of the art approaches. The technique is fast and could be implemented on camera hardware. It can be used for on-set monitoring on regular displays, as a substitute for gamma-correction, and as a way of providing the colorist with content that is both natural looking and has a crisp, clear image.