Adaptive Variable Degree-k Zero-Trees for Re-Encoding of Perceptually Quantized Wavelet-Packet Transformed Audio and High Quality Speech

TL;DR

This paper introduces an adaptive zero-tree algorithm for efficient re-encoding of perceptually quantized wavelet-packet coefficients in audio and speech, outperforming existing methods in quality and speed.

Contribution

The paper presents a novel adaptive variable degree-k zero-tree algorithm specifically designed for audio and speech, improving upon modified EZW and SPIHT schemes.

Findings

AVDZ outperforms modified EZW and SPIHT in bit-rate and computation time.

Modifications to EZW and SPIHT improve their performance by 15-25%.

AVDZ achieves better subjective quality with low complexity.

Abstract

A fast, efficient and scalable algorithm is proposed, in this paper, for re-encoding of perceptually quantized wavelet-packet transform (WPT) coefficients of audio and high quality speech and is called "adaptive variable degree-k zero-trees" (AVDZ). The quantization process is carried out by taking into account some basic perceptual considerations, and achieves good subjective quality with low complexity. The performance of the proposed AVDZ algorithm is compared with two other zero-tree-based schemes comprising: 1- Embedded Zero-tree Wavelet (EZW) and 2- The set partitioning in hierarchical trees (SPIHT). Since EZW and SPIHT are designed for image compression, some modifications are incorporated in these schemes for their better matching to audio signals. It is shown that the proposed modifications can improve their performance by about 15-25%. Furthermore, it is concluded that the proposed AVDZ algorithm outperforms these modified versions in terms of both output average bit-rates and computation times.