High dynamic range (HDR) video processing for the exploitation of high bit-depth sensors in human-monitored surveillance

High bit-depth video data is becoming more common in imaging and remote sensing because higher bit-depth cameras are becoming more affordable. Displays often represent images in lower bit-depths, and human vision is not able to completely exploit this additional information in its native form. These problems are addressed with High Dynamic Range (HDR) tone mapping, which nonlinearly maps lightness levels from a high bit-depth image into a lower bit-depth representation in a way that attempts to retain and accentuate the maximum amount of useful information therein. We have adapted the well-known Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm into the application of HDR video tone mapping by using time-adaptive local histogram transformations. In addition to lightness contrast, we use the transformations in the L*a*b* color space to amplify color contrast in the video stream. The transformed HDR video data maintains important details in local contrast while maintaining relative lightness levels locally through time. Our results show that time-adapted HDR tone mapping methods can be used in real-time video processing to store and display HDR data in low bit-depth formats with less loss of useful information compared to simple truncation.