Abstract
WT (Wavelet Transform) is considered as landmark for image compression because it represents a signal in terms of functions which are localized both in frequency and time domain. Wavelet sub-band coding exploits the self-similarity of pixels in images and arranges resulting coefficients in different sub-bands. A much simpler and fully embedded codec algorithm SPIHT (Set Partitioning in Hierarchical Trees) is widely used for the compression of wavelet transformed images. It encodes the transformed coefficients depending upon their significance comparative to the given threshold. Statistical analysis reveals that the output bit-stream of SPIHT comprises of long trail of zeroes that can be further compressed, therefore SPIHT is not advocated to be used as sole mean of compression. In this paper, wavelet transformed images have been initially compressed by using SPIHT technique and to attain more compression, the output bit streams of SPIHT are then fed to entropy encoders; Huffman and Arithmetic encoders, for further de-correlation. The comparison of two concatenations has been carried out by evaluating few factors like Bit Saving Capability, PSNR (Peak Signal to Noise Ratio), Compression Ratio and Elapsed Time. The experimental results of these cascading demonstrate that SPIHT combined with Arithmetic coding yields better compression ratio as compared to SPIHT cascaded with Huffman coding. Whereas, SPIHT once combined with Huffman coding is proved to be comparatively efficient.
Highlights
WT (Wavelet Transform) is considered as landmark for image compression because it represents a signal in terms of functions which are localized both in frequency and time domain
It has been observed that most of the values of wavelet coefficients are below the given threshold; output of SPIHT consists of a number of binary strings of zeros and ones that contains similarity and provides room for further compression [11]
SPIHT yields high PSNR than EZW because of a special symbol that indicates the significance of child nodes of significance parent, and separation of child nodes from second generation descendants [12,13,14]
Summary
Istorage space and reduction in transmission time. Wavelet Transform, a mathematical scheme developing rapidly in the field of image compression, is a successor of DCT (Discrete Cosine Transform) in which low frequencies are exploited efficiently and high frequencies are quantized coarsely [1]. SPIHT, an improved version of EZW, was proposed by Amir and William [6] It is WT based image compression algorithm that generates an embedded bit stream, gives better PSNR and CRs for different types of gray scale images [7]. It has been observed that most of the values of wavelet coefficients are below the given threshold; output of SPIHT consists of a number of binary strings of zeros and ones that contains similarity and provides room for further compression [11] This additional compression on the output stream of SPIHT can be achieved by using various types of entropy encodings.
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