Iyriboz T A, Zukoski M J, Hopper K D, Stagg P L
Department of Radiology, Penn State Geisinger Health System, Hershey 17033-1850, USA.
J Digit Imaging. 1999 May;12(2 Suppl 1):14-7. doi: 10.1007/BF03168745.
This presentation focuses on the quantitative comparison of three lossy compression methods applied to a variety of 12-bit medical images. One Joint Photographic Exports Group (JPEG) and two wavelet algorithms were used on a population of 60 images. The medical images were obtained in Digital Imaging and Communications in Medicine (DICOM) file format and ranged in matrix size from 256 x 256 (magnetic resonance [MR]) to 2,560 x 2,048 (computed radiography [CR], digital radiography [DR], etc). The algorithms were applied to each image at multiple levels of compression such that comparable compressed file sizes were obtained at each level. Each compressed image was then decompressed and quantitative analysis was performed to compare each compressed-then-decompressed image with its corresponding original image. The statistical measures computed were sum of absolute differences, sum of squared differences, and peak signal-to-noise ratio (PSNR). Our results verify other research studies which show that wavelet compression yields better compression quality at constant compressed file sizes compared with JPEG. The DICOM standard does not yet include wavelet as a recognized lossy compression standard. For implementers and users to adopt wavelet technology as part of their image management and communication installations, there has to be significant differences in quality and compressibility compared with JPEG to justify expensive software licenses and the introduction of proprietary elements in the standard. Our study shows that different wavelet implementations vary in their capacity to differentiate themselves from the old, established lossy JPEG.
本报告重点关注三种有损压缩方法应用于各种12位医学图像的定量比较。对60幅图像使用了一种联合图像专家组（JPEG）算法和两种小波算法。医学图像以医学数字成像和通信（DICOM）文件格式获取，矩阵大小从256×256（磁共振[MR]）到2560×2048（计算机X线摄影[CR]、数字X线摄影[DR]等）不等。这些算法在多个压缩级别应用于每幅图像，以便在每个级别获得可比的压缩文件大小。然后对每个压缩图像进行解压缩，并进行定量分析，以将每个压缩后再解压缩的图像与其相应的原始图像进行比较。计算的统计量包括绝对差之和、平方差之和以及峰值信噪比（PSNR）。我们的结果证实了其他研究，这些研究表明，与JPEG相比，在恒定压缩文件大小的情况下，小波压缩产生的压缩质量更好。DICOM标准尚未将小波作为公认的有损压缩标准。对于实施者和用户而言，要将小波技术作为其图像管理和通信设施的一部分采用，与JPEG相比，在质量和可压缩性方面必须存在显著差异才能证明昂贵的软件许可以及在标准中引入专有元素是合理的。我们的研究表明，不同的小波实现方式在与旧的、既定的有损JPEG区分开来的能力方面存在差异。
