<title>High-fidelity image compression with nearly lossless arithmetic coding</title>

Abstract

ABSTRACT In this work, we investigate the nearly lossless image compression technique, which provides abetter compression ratio than purely lossless compression schemes and has a better reconstructed image quality than lossy ones. In particular, we introduce a new idea called the soft decisionquantization and integrate it with the binary arithmetic QM coder. The superior performance of the developed algorithm is demonstrated with numerical experiments. Keywords: nearly lossless, semi-lossless, image compression, soft decision quantization, QM coder. 1 INTRODUCTION Image compression methods can be categorized into two classes: lossless and lossy schemes.Entropy encoding is an example of lossless compression which removes redundancy among sym- bols without actual loss of information so that the image can be reconstructed exactly as theoriginal one. Quantization is applied in lossy compression schemes to achieve a better coding gain at the expense of information loss and, consequently, the reconstructed image cannot be thesame as the original one. In applications such as picture archiving and medical/legal image trans-mission, high compression efficiency and image fidelity are both required. We have to consider acareful balance between the desired image quality and the cost of storage and transmission. Theresulting scheme is called high fidelity image compression, which reduces the distortion of tra-ditional lossy compression schemes while providing a better rate deduction than purely losslessschemes. In this work, we propose a new approach to achieve high fidelity image compression.The basic idea is to divide the entire quantization range into 3 intervals: LOW, HIGH and Don'tCARE when performing quantization. The LOW interval is always encoded with 0, the HIGH