Audio Precision, Inc., Beaverton, OR 97075, USA.
IEEE Trans Image Process. 2000;9(5):909-22. doi: 10.1109/83.841536.
Digital halftoning quantizes a graylevel image to one bit per pixel. Halftoning by error diffusion reduces local quantization error by filtering the quantization error in a feedback loop. In this paper, we linearize error diffusion algorithms by modeling the quantizer as a linear gain plus additive noise. We confirm the accuracy of the linear model in three independent ways. Using the linear model, we quantify the two primary effects of error diffusion: edge sharpening and noise shaping. For each effect, we develop an objective measure of its impact on the subjective quality of the halftone. Edge sharpening is proportional to the linear gain, and we give a formula to estimate the gain from a given error filter. In quantifying the noise, we modify the input image to compensate for the sharpening distortion and apply a perceptually weighted signal-to-noise ratio to the residual of the halftone and modified input image. We compute the correlation between the residual and the original image to show when the residual can be considered signal independent. We also compute a tonality measure similar to total harmonic distortion. We use the proposed measures for edge sharpening, noise shaping, and tonality to evaluate the quality of error diffusion algorithms.
数字半色调将灰度图像量化为每个像素 1 位。通过误差扩散进行半色调处理通过在反馈环中对量化误差进行滤波来减少局部量化误差。在本文中，我们通过将量化器建模为线性增益加加性噪声，对误差扩散算法进行线性化。我们通过三种独立的方法来验证线性模型的准确性。使用线性模型，我们量化了误差扩散的两个主要影响：边缘锐化和噪声整形。对于每种效果，我们开发了一种客观的度量方法来衡量其对半色调主观质量的影响。边缘锐化与线性增益成正比，我们给出了一个从给定的误差滤波器估计增益的公式。在量化噪声时，我们修改输入图像以补偿锐化失真，并对半色调和修改后的输入图像的残余部分应用感知加权信噪比。我们计算残余部分与原始图像之间的相关性，以显示残余部分何时可以被视为与信号无关。我们还计算了类似于总谐波失真的色调度量。我们使用所提出的边缘锐化、噪声整形和色调度量来评估误差扩散算法的质量。
