Speeding-up Fractal Audio Compression Using Selective Search Based on Proposed Affain Invariant Descriptors
Author: Zahraa A. Hasan and Loay E. George
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Abstract:
Fractal coding has been used for years in the field of compression. Many researchers and programmers considered this algorithm as a promising base of their work, but they noticed that it suffers from the long "encoding time", that is due to the exhaustive search performed to encode each range block, in which the whole blocks of domain pool is searched with a purpose to find the closest domain block. The aim of this research is to reduce the long encoding process by utilizing a selective based search method, so instead of making exhaustive search on the entire domain pool, only a subset (class) of this pool will be searched to encode each range block. The proposed method is based on an indexing scheme, this scheme relies upon the usage of moment descriptors as indexing parameters for the classes obtained; this approach does not affect the performance of the affain transform equation during the matching process. In the last few years, the moment concept was used by some researchers to achieve enhanced compression performance. In this article, a selective scheme that based on new set of weakly correlated moments is introduced. It retains as much as possible of their class discriminatory information that leads to find the best approximations for the range blocks during the matching stage, Moreover it leads to a high reduction in the encoding time compared to traditional method without making significant degradation in the fidelity of compressed audio file.
Keywords: Fractal audio compression, Moment descriptor, Selective search, Indexing scheme