A fast encoding algorithm for vector quantization based on weighted variance inequality and Hadamard transform

Vector quantization is one of high performance and popular methods for data compression. But it is extremely time consuming during the encoding process. In this paper, a fast encoding algorithm for vector quantization is proposed to save the computation time. This algorithm uses two characteristics...

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Veröffentlicht in:2011 3rd International Conference on Computer Research and Development Jg. 4; S. 324 - 327
Hauptverfasser: Linbo Xie, Bang Huang
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.03.2011
Schlagworte:
Algorithm design and analysis
Channel coding
codeword search
High temperature superconductors
HTS
Image coding
Signal processing algorithms
variance
Vector quantization
ISBN:1612848397, 9781612848396
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Zusammenfassung:Vector quantization is one of high performance and popular methods for data compression. But it is extremely time consuming during the encoding process. In this paper, a fast encoding algorithm for vector quantization is proposed to save the computation time. This algorithm uses two characteristics of a vector, Hadamard transform (HT) and variance. The methods using one of these features was already proposed, they handles these features separately. Here, the proposed algorithm put forward a new inequality which utilizes these features simultaneously to rejects more codewords which are impossible to be the nearest codeword in the distortion computations stage. This method produces the same output as conventional full search algorithm. The simulation results show that the effectiveness of the proposed algorithm is outstanding.
ISBN:1612848397
9781612848396
DOI:10.1109/ICCRD.2011.5763885