Medical image compression using DCT-based subband decomposition and modified SPIHT data organization

Abstract

Objective The work proposed a novel bit-rate-reduced approach for reducing the memory required to store a remote diagnosis and rapidly transmission it. Method In the work, an 8 × 8 Discrete Cosine Transform (DCT) approach is adopted to perform subband decomposition. Modified set partitioning in hierarchical trees (SPIHT) is then employed to organize data and entropy coding. The translation function can store the detailed characteristics of an image. A simple transformation to obtain DCT spectrum data in a single frequency domain decomposes the original signal into various frequency domains that can further compressed by wavelet-based algorithm. In this scheme, insignificant DCT coefficients that correspond to a particular spatial location in the high-frequency subbands can be employed to reduce redundancy by applying a proposed combined function in association with the modified SPIHT. Results and conclusions Simulation results showed that the embedded DCT-CSPIHT image compression reduced the computational complexity to only a quarter of the wavelet-based subband decomposition, and improved the quality of the reconstructed medical image as given by both the peak signal-to-noise ratio (PSNR) and the perceptual results over JPEG2000 and the original SPIHT at the same bit rate. Additionally, since 8 × 8 fast DCT hardware implementation being commercially available, the proposed DCT-CSPIHT can perform well in high speed image coding and transmission.