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27 February 1996 Detection and encoding of model failures in very low bit rate video coding
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Proceedings Volume 2727, Visual Communications and Image Processing '96; (1996) https://doi.org/10.1117/12.233297
Event: Visual Communications and Image Processing '96, 1996, Orlando, FL, United States
Abstract
One of the challenging problems for most existing video codecs is the detection and encoding of the information pertaining to model failure areas, i.e., areas where the compensation of the motion is insufficient. The insufficient motion may result from several different reasons, such as uncovered background by moving objects, complex motion, etc. The existing approaches to detection and encoding of model failures are closely tied to the encoding scheme they are built in, particularly to the specific motion estimation algorithm used; therefore, generalization of these algorithms to other coding techniques is not possible. On the other hand, the efficient encoding of the position and the intensity field information in these areas is also very crucial to the performance of the very low bit rate codecs. The existing approaches fail to meet the target bit rates and satisfactory image quality. In this paper, a new method to detect the model failure areas is described. In this method, the model failure areas are detected based on a motion compensated prediction of the current frame independently of the motion estimation algorithm. Thus the proposed method can be used with any type of coding scheme. In addition efficient and robust encoding of the boundary and the intensity information is described. The simulation results demonstrate that with the described method, the requirements of very low bit rate coding can be satisfactorily met.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Taner Ozcelik and Aggelos K. Katsaggelos "Detection and encoding of model failures in very low bit rate video coding", Proc. SPIE 2727, Visual Communications and Image Processing '96, (27 February 1996); https://doi.org/10.1117/12.233297
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