Perceptual dynamic range method for generating images of more natural appearance (Ref.TEC0114)

The contrast in a scene is measured by the ratio, called dynamic range, of light intensity values between its brightest and darkest points. While common natural scenes may have a contrast of 1,000,000:1 or more, our visual system allows us to perceive contrasts of roughly 10,000:1, while the vast majority of displays, including digital cinema projectors, have a limited contrast capability in the order of 1,000:1 and below.

It is not uncommon for camera sensors to have a dynamic range of 3 or 4 orders of magnitude, therefore matching in theory the range of human visual perception, but because of the constrained range of displays the image signals captured by sensors have to be non-linearly transformed so that their contrast is reduced, at the same time trying to maintain the natural appearance and visibility of details of the original. or HDR images as input.

We have developed a vision model for contrast perception that can be realized as an image-dependent transformation that can accurately reproduce the detail and contrast visible in the original scene. The model is based on findings from psychophysical and neurophysiological studies and is well suited to the statistics of natural images. It has been validated through psychophysical tests that confirm that it outperforms other state of the art algorithms in terms of users’ preference.

Our technology is of interest to companies in the media industries, all along the production chain: camera manufacturers (mobile, still photography, TV, cinema), producers of film, TV or videogames, content distributors (broadcast, internet streaming), and display manufacturers (mobile devices, TV sets, professional monitors). The extremely low complexity of our method allows for real-time software implementation, without any need to modify the hardware. It can also be used off-line, in professional settings, to automatize format conversion processes and produce content with optimal appearance.

Figure 1, a) conventional non-linear transform of a frame from a video sequence captured by an ARRI-Alexa professional digital cinema camera, b) manual output by a skilled colorist, c) our automatic result. Notice visibility of details in background. 

Perceptual dynamic range method for generating images of more natural appearance