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Content-adaptive long-term prediction with reduced memory

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1 Author(s)
Kutka, R. ; Corp. Technol., Information & Commun., Siemens AG, Munich, Germany

This paper presents a memory-saving long-term prediction algorithm. It takes advantage of the better image quality possible with this technique, but is characterized by an extreme reduction in the amount of memory space required. In reference picture selection mode (H.263 standard, Annex N), coder and decoder use not only the previous picture for prediction, but also information from less recent pictures. Contrary to Annex N, in the method that we propose only parts, e.g. individual blocks or areas of past pictures, are stored in a reference buffer. We present two new concepts, both for selecting picture areas for storing and for deleting superfluous blocks from the long-term memory. By suitable selection of the picture areas, our algorithm achieves a similar picture quality to H.263+, Annex N, but uses only a fraction of the picture buffer. In this way it is possible to address around 50 earlier pictures with the same amount of memory required for only 5 pictures. Moreover, our approach requires no signaling and produces no delay.

Published in:

Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on  (Volume:3 )

Date of Conference:

14-17 Sept. 2003