High-quality low-complexity wavelet-based compression algorithm for audio signals

Abstract

Wavelets have recently emerged as a powerful tool for signal compression, particularly in the areas of image, video, and audio compression. In this paper, we present a low-complexity wavelet-based audio compression algorithm that is capable of handling fairly arbitrary audio sources. The algorithm transforms the incoming audio data into the wavelet domain, and compresses data by exploring redundancy in the wavelet coefficients and exploiting the large runs of zeros in the transformed signal. Also there is a possibility of applying a threshold to the non-zero coefficients, thus a further increase in the number of zeros is expected. The audio signal is first preprocessed to scale down the wavelet coefficients. Then the preprocessed signal is wavelet transformed using a bi-orthogonal discrete wavelet transform (DWT) and threshold by applying energy compaction strategy. Encoding represents the threshold coefficients in compact form. A new encoding technique that is easy to implement, and that provides a reasonable compression ratio for a certain acceptable distortion level has been developed to encode the threshold DWT. So, a bit rate can be controlled such that the algorithm operates at virtually any pre-selected bit rate. The motivation of using the bi-orthogonal wavelet transform is that it permits the use of a much broader class of filters, and this class includes symmetric linear phase filters. The superior performance of this algorithm is also demonstrated by comparing it with two other popular audio compression techniques and this meets the requirements of multimedia computing.