Overview: Wavelet Transform Along With SPIHT Algorithm for Image Compression

Abstract: Image compression is one of the important technologies in multimedia communications, that has been much attention in the past decades, where the two techniques Discrete Wavelet Transform (DWT) and Set-Partitioning In Hierarchical Trees (SPIHT) have great influence on its performance. Due to the properties of fast computation, low memory requirement, adaptive Lifting DWT has been adopted as a new technique for still image compression. Furthermore, the traditional DWT and SPIHT have the drawback of long bits output and time consuming. In this paper we produce a new technique named adaptive Lifting DWT. An Adaptive Lifting DWT that locally adapts the filtering directions to image content based on directional lifting. This technique using the new algorithm that detects all the blocks in a given image to decide whether the block is homogenous or heterogeneous block. For homogeneous block, the simple Discrete Wavelet Transform (DWT) is used. And for the heterogeneous block Lifting Wavelet Transform is used. In this technique image quality is measured objectively, using peak signal noise ratio or picture quality scale, and subjectively, using perceived image quality.

Keywords: SPIHT, Entropy coding, Lifting Scheme, Wavelet, Compression.