High Synthetic Audio Compression Model Based on Fractal Audio Coding and Error-Compensation

Abstract

This study presented a model for improving audio files quality using fractal coding specifically when a high compression ratio is required. The proposed high synthetic audio compression model which can be called (HSACM) is based on conventional fractal coding and lifting wavelet transform. Various lifting wavelet transform families and levels are used and their effects on the reconstructed audio files are discussed as well. Audio files from GTZAN dataset and standard measurements for data compression are used in the evaluation of the proposed model. The results reveal that using block length 50 samples which is the worst case, PSNR is increased, on average, from 34.1 to 44.8 dB and from 34.1 to 40.5 dB using lifting wavelet transform with 3 and 2 levels, respectively. Thus, the PSNR is improved by 10 and 5 dB with slightly reducing the compression ratio by 6.2 and 12.5%, respectively. Moreover, it can be noticed that adopting lifting wavelet transform with basis Haar, db1, db4, db5, cdf1.1 and cdf2.2 provide higher audio quality while db6, db8, sym7 and sym8 give the worst audio quality. Furthermore, the performance of HSACM is compared with that of existing work to highlight its performance.