Image Compression of MRI Image using Planar Coding

In this paper a hierarchical coding technique for variable bit rate service is developed using embedded zero block coding approach. The suggested approach enhances the variable rate coding by zero tree based block-coding architecture with Context Modeling for low complexity and high performance. The proposed algorithm utilizes the significance state-table forming the context modeling to control the coding passes with low memory requirement and low implementation complexity with the nearly same performance as compared to the existing coding techniques. Keyword- image coding; embedded block coding; context modeling; multi rate services. I. INTRODUCTION With rapid development of heterogeneous services in image application the future digital medical images and video coding applications finds various limitations with available resource. The traditional multi-bit stream approach to the heterogeneity issue is very constrained and inefficient under multi bit rate applications. The multi bit stream coding techniques allow partial decoding at a various resolution and quality levels. Several scalable coding algorithms have been proposed in the international standards over the past decade, but these former methods can only accommodate relatively limited decoding properties. The rapid growth of digital imaging technology in conjunction with the ever-expanding array of access technologies has led to a new set of requirements for image compression algorithms. Not only are high quality reconstructed medical images required at medium-low bitrates, but also as the bit rate decreases, the quality of the reconstructed MRI image should degrade gracefully. The traditional multi-bit stream solution to the issue of widely varying user resources is both inefficient and rapidly becoming impractical. The bit level scalable codes developed for this system allow optimum reconstruction of a medical image from an arbitrary truncation point within a single bit stream. For progressive transmission, image browsing, medical image analysis, multimedia applications, and compatible trans coding, in a digital hierarchy of multiple bit rates, the problem of obtaining the best MRI image quality and accomplishing it in an embedded fashion i.e. all the encoded bits making compatible to the target bit rate is a bottleneck task for today"s engineer. As medical images are of huge data set and encoding it for a lower bit rate results in loss of data, which intern results in very low image quality under compression. Coming to the transmission over a noisy channel this problem becomes more effective due to narrow bandwidth effect. Various algorithms were proposed for encoding and compressing the MRI image data before transmission. These algorithms show high-end results under high bandwidth systems but show poor result under low data rate systems. The problem of transmission of MRI images over a low bit rate bandwidth can be overcome if the medical image data bits are such encoded and compressed that the data bit rate is made compatible to the provided low bit rate. Embedded zero tree wavelet algorithm is a proposed image compression algorithm which encode the bit in the bit stream in the order of importance which embed the bit stream in hierarchical fashion.