ABSTRACT H.264 as a new-generation video coding algorithm is becoming increasingly important for international
broadcasting standards such as DVB-H and DMB. In comparison to its predecessors MPEG-2 and
MEPG-4 SP/ASP, H.264 achieves improved compression effciency at the cost of increased computational complexity.
Real-time execution of the H.264 decoding process poses a large challenge on mobile devices due to
low processing capabilities. Multi-core systems provide an elegant and power-effcient solution to overcome this
performance limitation. However, effciently distributing the video algorithm among multiple processing units
is a non-trivial task. It requires detailed knowledge about the algorithmic complexity, dynamic variations and
inter-dependencies between functional blocks. The objective of this paper is an investigation on the dynamic
behavior of the H.264 decoding process and on the interaction between the main decoding tasks in the context
of multi-core environments. We use an H.264 decoder model to investigate the effciency of a decoding system
under various conditions (e.g. different FIFO buffer sizes, bitstreams, coding features and bitrates). The gained
insights are finally used to optimize the runtime behavior of a multi-core decoding system and to find a good
trade-off between core usage and buffer sizes.
This paper describes a novel stereo matching algorithm for epipolar rectified images. The method applies colour segmentation on the reference image. The use of segmentation makes the algorithm capable of handling large untextured regions, estimating precise depth boundaries and propagating disparity information to occluded regions, which are challenging tasks for conventional stereo methods. We model disparity inside a segment by a planar equation. Initial disparity segments are clustered to form a set of disparity layers, which are planar surfaces that are likely to occur in the scene. Assignments of segments to disparity layers are then derived by minimization of a global cost function via a robust optimization technique that employs graph cuts. The cost function is defined on the pixel level, as well as on the segment level. While the pixel level measures the data similarity based on the current disparity map and detects occlusions symmetrically in both views, the segment level propagates the segmentation information and incorporates a smoothness term. New planar models are then generated based on the disparity layers' spatial extents. Results obtained for benchmark and self-recorded image pairs indicate that the proposed method is able to compete with the best-performing state-of-the-art algorithms.