Improved embedded non-linear processing of video for camera surveillance

For a real time imaging in surveillance applications, image fidelity is of primary importance to ensure customer confidence. The fidelity is obtained amongst others via dynamic range expansion and video signal enhancement. The dynamic range of the signal needs adaptation, because the sensor signal has a much larger range than the usual CRT display. The signal enhancement should accommodate for the widely varying light conditions and user scenarios of the equipment. This paper proposes a new system to combine a dynamic range and enhancement processing that offers a strongly improved picture quality for surveillance applications. The key to our solution is that we use Non-Linear Processing (NLP) with a so-called Constrained Histogram Range Equalization (CHRE). The NLP transforms the digitized high-dynamic luminance sensor signal such that details of the low-luminance parts are enhanced, while avoiding losses in high-luminance areas. The CHRE technique enhances visibility of the global contrast for the camera signal without too much loss in the statistically less relevant areas. An additional advantage is that the new scheme is adaptable and allows the concatenation of further enhancement techniques without sacrificing the obtained picture quality improvement