KEYWORDS: Image compression, Video compression, 3D modeling, Video, Cameras, Performance modeling, 3D video compression, Algorithm development, 3D image processing, Systems modeling
Disparity estimation is a very important operation in stereo image and video compression. However, existing disparity estimation algorithms require too large computation power. In this paper, we propose a fast disparity estimation algorithm for mesh-based stereo image and video compression with two-stage hybrid approach, which is names as Two Stage Iterative Block and Octagonal Matching algorithm (TS-IBOM). The first stage of this algorithm is Iterative Block Matching algorithm, which can give a good initial guess of the disparity vectors with a little computation. In the second stage, Iterative Octagonal Matching algorithm is employed to refine the disparity vectors. A local updating scheme is also proposed to further accelerate the process by skipping nodes whose disparity vectors have been derived already. Experimental results show that the proposed algorithm can give good results and is 18 times faster than Octagonal Matching. This algorithm is suitable to be employed in mesh-based stereo image and video compression systems.
The emerging video coding standard, H.264/MPEG-4 AVC, allows to use multiple reference frames for motion estimation to enhance temporal prediction. Exhaustive search of each frame requires a lot of computation which is propotional to the number of searched frames. However, the reduction of prediction residues is highly dependent on the characteristics of video sequences. In many cases, searching more reference frames contributes to nothing but only waste of computation. In this paper, we proposed a novel algorithm to accelerate the motion estimation by using reference frame skipping criteria. We adopted selected macroblock mode, intra/inter frame prediction residues, compactness of motion vectors, and scene changes in the criteria. By using these criteria, each macroblock can determine whether it is necessary to keep on searching more reference frams after the block matching process in the first reference frame. Simulation results show that the proposed algorithm can save up to 80% of computation without noticeable degradation of video quality.
Background registration technique is useful to solve still object problem and uncovered background problem for video segmentation. However, it is hard to automatically decide the threshold of the frame difference for background registration to make it more feasible for real-time applications. Many previous works made efforts on automatic threshold decision for change detection. In this paper, we propose a new method of automatic threshold decision algorithm in a totally different viewpoint. Not only change detection but also the quantization effect in discrete domain is concerned. A Gaussianity test is first applied to find the standard deviation of Gaussian noise from the camera. Then, the quantization effect in discrete domain is taken into consideration to derive the relation between the standard deviation and the optimal threshold value. A couple MPEG-4 sequences and experimental sequences are tested as examples. Simulation results show that the calculated threshold values are suitable for background registration to give good segmentation results.
Video segmentation is one of the fundamental technologies for content-based real-time MPEG-4 camera system. For real- time requirement, the segmentation algorithm should be fast and accurate. Almost all existing algorithms are computationally intensive and not suitable for real-time applications. In this paper, based on change detection and background registration, a new segmentation algorithm is proposed. The algorithm includes two modes: first, the baseline mode that can deal with general sequences; second, the shadow cancellation mode, which is based on gradient operation and block-based changed detection, can deal with sequences affected by shadow and light change. Experimental results show that this algorithm can be applied on different kinds of sequences, and can quickly give accurate segmentation results.
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