Real-Time Algorithms for High Dynamic Range Video

A recurring problem in capturing video is the scene having a range of brightness values that exceeds the capabilities of the capturing device. An example would be a video camera in a bright outside area, directed at the entrance of a building. Because of the potentially big brightness difference, it may not be possible to capture details of the inside of the building and the outside simultaneously using just one shutter speed setting. This results in under- and overexposed pixels in the video footage. The approach we follow in this thesis to overcome this problem is temporal exposure bracketing, i.e., using a set of images captured in quick sequence at different shutter settings. Each image then captures one facet of the scene's brightness range. When fused together, a high dynamic range (HDR) video frame is created that reveals details in dark and bright regions simultaneously. The process of creating a frame in an HDR video can be thought of as a pipeline where the output of each step is the input to the subsequent one. It begins by capturing a set of regular images using varying shutter speeds. Next, the images are aligned with respect to each other to compensate for camera and scene motion during capture. The aligned images are then merged together to create a single HDR frame containing accurate brightness values of the entire scene. As a last step, the HDR frame is tone mapped in order to be displayable on a regular screen with a lower dynamic range. This thesis covers algorithms for these steps that allow the creation of HDR video in real-time. When creating videos instead of still images, the focus lies on high capturing and processing speed and on assuring temporal consistency between the video frames. In order to achieve this goal, we take advantage of the knowledge gained from the processing of previous frames in the video. This work addresses the following aspects in particular. The image size parameters for the set of base images are chosen such that only as little image data as possible is captured. We make use of the fact that it is not always necessary to capture full size images when only small portions of the scene require HDR. Avoiding redundancy in the image material is an obvious approach to reducing the overall time taken to generate a frame. With the aid of the previous frames, we calculate brightness statistics of the scene. The exposure values are chosen in a way, such that frequently occurring brightness values are well-exposed in at least one of the images in the sequence. The base images from which the HDR frame is created are captured in quick succession. The effects of intermediate camera motion are thus less intense than in the still image case, and a comparably simpler camera motion model can be used. At the same time, however, there is much less time available to estimate motion. For this reason, we use a fast heuristic that makes use of the motion information obtained in previous frames. It is robust to the large brightness difference between the images of an exposure sequence. The range of luminance values of an HDR frame must be tone mapped to the displayable range of the output device. Most available tone mapping operators are designed for still images and scale the dynamic range of each frame independently. In situations where the scene's brightness statistics change quickly, these operators produce visible image flicker. We have developed an algorithm that detects such situations in an HDR video. Based on this detection, a temporal stability criterion for the tone mapping parameters then prevents image flicker. All methods for capture, creation and display of HDR video introduced in this work have been fully implemented, tested and integrated into a running HDR video system. The algorithms were analyzed for parallelizability and, if applicable, adjusted and implemented on a high-performance graphics chip