Scalar-vector quantization of medical images

N. Mohsenian, H. Shahri, N. Nasrabadi
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引用次数: 2

Abstract

A new coding scheme based on the scalar-vector quantizer (SVQ) is developed for compression of medical images. SVQ is a fixed-rate encoder and its rate-distortion performance is close to that of optimal entropy-constrained scalar quantizers (ECSQ's) for memoryless sources. For a set of magnetic resonance (MR) images, coding results obtained from SVQ and ECSQ at low bit-rates are indistinguishable. Furthermore, the authors' encoded images are perceptually indistinguishable from the original, when displayed on a monitor. This makes the authors' SVQ based coder an attractive compression scheme for picture archiving and communication systems (PACS), currently under consideration for an all digital radiology environment in hospitals, where reliable transmission, storage, and high fidelity reconstruction of images are desired.
医学图像的标量矢量量化
提出了一种新的基于标量矢量量化器(SVQ)的医学图像压缩编码方案。SVQ是一种固定速率编码器,其速率失真性能接近无记忆源的最优熵约束标量量化器(ECSQ)。对于一组磁共振(MR)图像,低比特率下SVQ和ECSQ的编码结果是无法区分的。此外,当显示在显示器上时,作者编码的图像在感知上与原始图像无法区分。这使得作者基于SVQ的编码器成为图像存档和通信系统(PACS)的一种有吸引力的压缩方案,目前正在考虑用于医院的全数字放射学环境,其中需要可靠的传输,存储和高保真图像重建。
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