Multi-reference neighborhood search for vector quantization by self-organized featured map
Research output: Journal Publications and Reviews › RGC 22 - Publication in policy or professional journal
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 579-583 |
Journal / Publication | IEE Conference Publication |
Issue number | 410 |
Publication status | Published - 1995 |
Conference
Title | Proceedings of the 5th International Conference on Image Processing and its Applications |
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City | Edinburgh, UK |
Period | 4 - 6 July 1995 |
Link(s)
Abstract
Reference Neighbor Search (RNS) is a new technique for fast searching of vector quantization(VQ), while maintaining near-optimal visual quality. However, the performance is greatly affected by the selection of reference point. In this research, we employed Self-Organized Feature Map to generate a topological preserving codebook and adaptively predicted the reference by a simple scheme. The predicted reference is close to the input, together with searching across multiple queues, the searching cost was significantly reduced while maintaining optimal visual quality.
Citation Format(s)
Multi-reference neighborhood search for vector quantization by self-organized featured map. / Chan, K. W.; Chan, K. L.
In: IEE Conference Publication, No. 410, 1995, p. 579-583.
In: IEE Conference Publication, No. 410, 1995, p. 579-583.
Research output: Journal Publications and Reviews › RGC 22 - Publication in policy or professional journal