For real-time imaging in surveillance applications, visibility of details is of primary importance to ensure customer
confidence. If we display High Dynamic-Range (HDR) scenes whose contrast spans four or more orders of magnitude on
a conventional monitor without additional processing, results are unacceptable. Compression of the dynamic range is
therefore a compulsory part of any high-end video processing chain because standard monitors are inherently Low-
Dynamic Range (LDR) devices with maximally two orders of display dynamic range. In real-time camera processing,
many complex scenes are improved with local contrast enhancements, bringing details to the best possible visibility. In
this paper, we show how a multi-scale high-frequency enhancement scheme, in which gain is a non-linear function of the
detail energy, can be used for the dynamic range compression of HDR real-time video camera signals. We also show the
connection of our enhancement scheme to the processing way of the Human Visual System (HVS). Our algorithm
simultaneously controls perceived sharpness, ringing ("halo") artifacts (contrast) and noise, resulting in a good balance
between visibility of details and non-disturbance of artifacts. The overall quality enhancement, suitable for both HDR
and LDR scenes, is based on a careful selection of the filter types for the multi-band decomposition and a detailed
analysis of the signal per frequency band.
KEYWORDS: Video, Signal to noise ratio, High dynamic range imaging, Sensors, Cameras, Visualization, Visibility, Image compression, Video processing, Video surveillance
For real-time imaging with digital video cameras, good tonal rendition of video is important to ensure high visual comfort for the user. Except local contrast improvements, High Dynamic Range (HDR) scenes require adaptive gradation correction (tone-mapping function) which should enable good visualization of details at lower brightness. We discuss how to construct and control optimal tone-mapping functions, which enhance visibility of image details in the dark regions while not excessively compressing the image in the bright image parts. The result of this method is a 21-dB expansion of the dynamic range. The new algorithm was successfully evaluated in HW and although suited for any video system, it is particularly beneficial for those processing HDR video.
For real-time imaging in surveillance applications, visibility of details is of primary importance to ensure customer
confidence. Usually, image quality is improved by enhancing contrast and sharpness. Many complex scenes require local
contrast improvements that should bring details to the best possible visibility. However, local enhancement methods
mainly suffer from ringing artifacts and noise over-enhancement. In this paper, we present a new multi-window real-time
high-frequency enhancement scheme, in which gain is a non-linear function of the detail energy. Our algorithm
simultaneously controls perceived sharpness, ringing artifacts (contrast) and noise, resulting in a good balance between
visibility of details and non-disturbance of artifacts. The overall quality enhancement is based on a careful selection of
the filter types for the multi-band decomposition and a detailed analysis of the signal per frequency band. The advantage
of the proposed technique is that detail gains can be set much higher than usual and the algorithm will reduce them only
at places where it is really needed.
For real-time imaging in surveillance applications, image fidelity is of primary importance to ensure customer confidence. The obtained image fidelity is a result from amongst others 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 standard CRT display. The signal enhancement should accommodate for the widely varying light and scene conditions and user
scenarios of the equipment. This paper proposes a new system to combine dynamic range and enhancement processing, offering 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 detail losses in the high-luminance areas. The CHRE technique enhances visibility of the global contrast for the camera signal
without significant information loss in the statistically less relevant areas. Evaluations of this proposal have shown clear
improvements of the perceptual image quality. 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.
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