Hewlett-Packard Co., Palo Alto, CA.
IEEE Trans Image Process. 1997;6(11):1567-79. doi: 10.1109/83.641416.
We suggest an optimization-based method for halftoning that involves looking ahead before a decision for each binary output pixel is made. We first define a mixture distortion criterion that is a combination of a frequency-weighted mean square error (MSE) and a measure depending on the distances between minority pixels in the halftone. A tree-coding approach with the ML-algorithm is used for minimizing the distortion criterion to generate a halftone. While this approach generates halftones of high quality, these halftones are not very amenable to lossless compression. We introduce an entropy constraint into the cost function of the tree-coding algorithm that optimally trades off between image quality and compression performance in the output halftones.
我们提出了一种基于优化的半色调方法，该方法在做出每个二进制输出像素的决策之前进行了前瞻性考虑。我们首先定义了一种混合失真准则，该准则是频率加权均方误差 (MSE) 和取决于半色调中少数像素之间距离的度量的组合。使用具有 ML 算法的树编码方法来最小化失真准则以生成半色调。虽然这种方法生成的半色调质量很高，但这些半色调不太适合无损压缩。我们在树编码算法的代价函数中引入了一个熵约束，以在输出半色调中在图像质量和压缩性能之间进行最佳权衡。
