vector quantization

Vector Quantization (VQ) is the powerful technique in image compression. Generating a good codebook is an important part of VQ. There are various algorithms in order to generate an optimal codebook. Recently, Swarm Intelligence (SI) algorithms were adapted to obtain the near-global optimal codebook of VQ. In this paper, we proposed a new method based on a modified firefly algorithm (MFA) to construct the codebook of VQ. The proposed method merged genetic crossover operator with FA to develop the VQ. This method is called MFA model. Experimental results indicate that the reconstructed images generated by the proposed model is get higher quality than FA and it’s about one percent, but it is no significant superiority to the PSO algorithm. Furthermore, MFA is slower than FA.

The purpose of this paper is to develop a text-based speakerIdentification system by providing a new method for speaker classification. In this work, a speaker identification system has been successfully tested for a 10-person database provided by the authors in the normal room environment with a standard headset microphone.The Mel Frequency Cepstral Coefficient (MFCC) is used as a discriminating feature which is extracted from the speech of individual persons and stored with the first and second derivatives as a property vector.Using Vector Quantization (VQ), the data has been reduced and the SupportVector Machine (SVM) with the radial base functions and polynomial core to classify the speakers.The simulation results show that the proposed algorithm has acceptable detection capability in high Signal to Noise Ratio (SNR), but detection percentage decreases by decreasing the signal-to-noise ratio.