Wavelet Video Coding Algorithm Based on Energy Weighted Significance Probability Balancing Tree

TL;DR

This paper introduces a novel 3-D wavelet video coding algorithm that weights wavelet subbands by basis energy and employs a significance probability balancing tree, resulting in improved reconstruction quality and lower computational cost.

Contribution

It proposes a new energy-weighted significance probability balancing tree structure for 3-D wavelet video coding, enhancing compression efficiency and reducing computation compared to existing methods.

Findings

Achieves higher PSNR than asymmetric 3-D orientation tree.

Outperforms temporal-spatial orientation tree in PSNR.

Requires lower computational cost.

Abstract

This work presents a 3-D wavelet video coding algorithm. By analyzing the contribution of each biorthogonal wavelet basis to reconstructed signal's energy, we weight each wavelet subband according to its basis energy. Based on distribution of weighted coefficients, we further discuss a 3-D wavelet tree structure named \textbf{significance probability balancing tree}, which places the coefficients with similar probabilities of being significant on the same layer. It is implemented by using hybrid spatial orientation tree and temporal-domain block tree. Subsequently, a novel 3-D wavelet video coding algorithm is proposed based on the energy-weighted significance probability balancing tree. Experimental results illustrate that our algorithm always achieves good reconstruction quality for different classes of video sequences. Compared with asymmetric 3-D orientation tree, the average peak signal-to-noise ratio (PSNR) gain of our algorithm are 1.24dB, 2.54dB and 2.57dB for luminance (Y) and chrominance (U,V) components, respectively. Compared with temporal-spatial orientation tree algorithm, our algorithm gains 0.38dB, 2.92dB and 2.39dB higher PSNR separately for Y, U, and V components. In addition, the proposed algorithm requires lower computation cost than those of the above two algorithms.