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

Stereoscopic Seam Carving With Temporal Consistency

In this paper, we present a novel technique for seam carving of stereoscopic video. It removes seams of pixels in areas that are most likely not noticed by the viewer. When applying seam carving to stereoscopic video rather than monoscopic still images, new challenges arise. The detected seams must be consistent between the left and the right view, so that no depth information is destroyed. When removing seams in two consecutive frames, temporal consistency between the removed seams must be established to avoid flicker in the resulting video. By making certain assumptions, the available depth information can be harnessed to improve the quality achieved by seam carving. Assuming that closer pixels are more important, the algorithm can focus on removing distant pixels first. Furthermore, we assume that coherent pixels belonging to the same object have similar depth. By avoiding to cut through edges in the depth map, we can thus avoid cutting through object boundaries

Parallel Implementation of a Real-Time High Dynamic Range Video System

Abstract. This article describes the use of the parallel processing capabilities of a graphics chip to increase the processing speed of a high dynamic range (HDR) video system. The basis is an existing HDR video system that produces each frame from a sequence of regular images taken in quick succession under varying exposure settings. The image sequence is processed in a pipeline consisting of: shutter speeds selection, capturing, color space conversion, image registration, HDR stitching, and tone mapping. This article identifies bottlenecks in the pipeline and describes modifications to the algorithms that are necessary to enable parallel processing. Time-critical steps are processed on a graphics processing unit (GPU). The resulting processing time is evaluated and compared to the original sequential code. The creation of an HDR video frame is sped up by a factor of 15 on the average

Trau, SCHAU, wem? - V-IDS oder eine andere Sicht der Dinge

Die ständig wachsende Flut der in einem Netzwerk anfallenden sicherheitsrelevanten Daten macht in zunehmendem Maße neue Darstellungsformen notwendig. Nur so können diese Daten ausreichend schnell und in angemessenem Umfang erfassbar und beherrschbar bleiben. Wesentlich schneller und intuitiver als reinen Text können wir den Inhalt von Bildern erfassen, grafische Darstellungen machen Geschehnisse in der Regel leichter erfassbar. Informationen können zusätzlich stärker verdichtet dargestellt werden, ohne dass der transportierte Inhalt darunter leidet. Die Darstellung von Sicherheitsdaten in grafischer Form steht derzeit noch sehr am Anfang, es gibt wenig Erfahrung, welche Darstellungen mehr und welche weniger geeignet sind. V-IDS soll Grundlagen legen für eine dynamische, dreidimensionale Darstellung solcher Daten. Es soll ein einfaches Experimentieren mit verschiedenen und neuartigen Darstellungen ermöglichen. Damit können dann vorhandene und zukünftige Ideen einfach und ohne längere Entwicklungszeit prototypisch umgesetzt und bewertet werden

Analysis of Disparity Maps for Detecting Saliency in Stereoscopic Video

We present a system for automatically detecting salient image regions in stereoscopic videos. This report extends our previous system and provides additional details about its implementation. Our proposed algorithm considers information based on three dimensions: salient colors in individual frames, salient information derived from camera and object motion, and depth saliency. These three components are dynamically combined into one final saliency map based on the reliability of the individual saliency detectors. Such a combination allows using more efficient algorithms even if the quality of one detector degrades. For example, we use a computationally efficient stereo correspondence algorithm that might cause noisy disparity maps for certain scenarios. In this case, however, a more reliable saliency detection algorithm such as the image saliency is preferred. To evaluate the quality of the saliency detection, we created modified versions of stereoscopic videos with the non-salient regions blurred. Having users rate the quality of these videos, the results show that most users do not detect the blurred regions and that the automatic saliency detection is very reliable

Histogram-based image registration for real-time high dynamic range videos

We introduce a novel approach for image registration for high dynamic range (HDR) videos. We estimate a translation vector between two low dynamic range (LDR) frames cap-tured at different exposure settings. By using row and column histograms, counting the number of dark and bright pixels in a row or column, and maximizing the correlation between the histograms of two consecutive frames, we reduce the two-dimensional problem to two one-dimensional searches. This saves computation time, which is critical in recording HDR videos in real-time. The robustness of our estimation is increased through application of a Kalman filter. A novel certainty criterium controls whether the estimated translation is used directly or discarded and extrapolated from previous frames. Our experiments show that our proposed approach performs registration more robustly on videos and is 1.4 to 3 times faster than comparable algorithms. Index Terms — Image Registration, HDR Video 1