Wavelet packet medical image coding using Space Frequency Quantization

Imaging modalities such as computerized tomography and magnetic resonance imaging produce digital images, which suffer from storage and transmission difficulties. Hence the need for high performance compression algorithms to reduce storage and transmission costs is evident. Wavelet transform is a class of subband coding of images, but it often fails to accurately capture high frequency information, especially at low bit rates where such information is lost in quantization noise. An alternate to wavelet transform is a wavelet packet that is better able to represent high-frequency information. The problem of wavelet packet image coding consists of considering all possible wavelet packet bases in the library and choosing the one that gives the best coding performance. In this work we use Space Frequency Quantization (SFQ) coder that undertaken the joint optimization of the spatial zerotree quantization and the step size of the scalar frequency quantizer. We compare the performance of wavelet packet SFQ coder with that of Vector Quantization (VQ) and Set Partitioning In Hierarchical Trees (SPIHT) coding. Extensive experimental results show that SFQ coder gives excellent coding performance for Peak Signal to Noise Ratio (PSNR) values at various bit rates.