Computational complexity reduction methods for multiscale recurrent pattern algorithms

The Multidimensional Multiscale Parser algorithm was originally proposed as a generic lossy data compression algorithm. An high degree of adaptivity and versatility allowed it to outperform state-of-the-art transform-based compression methods for a wide range of applications, from still images, comp...

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Veröffentlicht in:2011 IEEE Eurocon S. 1 - 4
Hauptverfasser: Francisco, Nelson C., Rodrigues, Nuno M. M., da Silva, Eduardo A. B., de Carvalho, Murilo B., de Faria, Sergio M. M.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.04.2011
Schlagworte:
Computational complexity
Costs
Data Compression
Decoding
Dictionaries
Distortion
Encoding
Image Coding
Image segmentation
Optimization
Pattern Matching
Symbols
ISBN:1424474868, 9781424474868
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:The Multidimensional Multiscale Parser algorithm was originally proposed as a generic lossy data compression algorithm. An high degree of adaptivity and versatility allowed it to outperform state-of-the-art transform-based compression methods for a wide range of applications, from still images, compound documents, or even ECG's, just to name a few. However, as other pattern matching algorithms, it presents a high computational complexity. In this paper, we investigated several techniques that allowed to considerably reduce both the encoder's and the decoder's computational complexity, with marginal R-D performance losses. The most important reduction was achieved on the decoder, that reduced up to 95% the time required by the previous method. These improvements contribute to affirm MMP as an alternative to traditional transform-based encoders, approaching its computational complexity with that of transform-based algorithms.
ISBN:1424474868
9781424474868
DOI:10.1109/EUROCON.2011.5929396