A new, fast, and efficient image codec based on set partitioning in hierarchical trees

TLDR

Embedded zerotree wavelet (EZW) coding is an effective, computationally simple image compression technique. The authors aim to clarify EZW principles and present a new SPIHT implementation that outperforms prior extensions. The approach relies on magnitude‑based partial ordering, set‑partitioning sorting, ordered bit‑plane transmission, and cross‑scale self‑similarity, all realized in the SPIHT algorithm. The resulting codec matches or exceeds the performance of more sophisticated methods while being extremely fast, and can be further accelerated with negligible loss by omitting arithmetic coding.

Abstract

Embedded zerotree wavelet (EZW) coding, introduced by Shapiro (see IEEE Trans. Signal Processing, vol.41, no.12, p.3445, 1993), is a very effective and computationally simple technique for image compression. We offer an alternative explanation of the principles of its operation, so that the reasons for its excellent performance can be better understood. These principles are partial ordering by magnitude with a set partitioning sorting algorithm, ordered bit plane transmission, and exploitation of self-similarity across different scales of an image wavelet transform. Moreover, we present a new and different implementation based on set partitioning in hierarchical trees (SPIHT), which provides even better performance than our previously reported extension of EZW that surpassed the performance of the original EZW. The image coding results, calculated from actual file sizes and images reconstructed by the decoding algorithm, are either comparable to or surpass previous results obtained through much more sophisticated and computationally complex methods. In addition, the new coding and decoding procedures are extremely fast, and they can be made even faster, with only small loss in performance, by omitting entropy coding of the bit stream by the arithmetic code.