Color error-diffusion halftoning what differentiates it from grayscale error diffusion?

Abstract

What differentiates it from grayscale error diffusion? G rayscale halftoning converts a continuous-tone image (e.g., 8 bits per pixel) to a lower resolution (e.g., 1 bit per pixel) for printing or display. Grayscale halftoning by error diffusion uses feedback to shape the quantization noise into high frequencies where the human visual system (HVS) is least sensitive. In color halftoning, the application of grayscale error-diffusion methods to the individual colorant planes fails to exploit the HVS response to color noise. Ideally the quantization error must be diffused to frequencies and colors, to which the HVS is least sensitive. Further it is desirable for the color quantization to take place in a perceptual space so that the colorant vector selected as the output color is perceptually closest to the color vector being quantized. This article discusses the design principles of color error diffusion that differentiate it from grayscale error diffusion, focusing on color error diffusion halftoning systems using the red, green, and blue (RGB) space for convenience.

Three-dimensional interconnection scheme for color error diffusion

“…Our 3D filter and Shaked et al's 11 MBVC method yielded similar low noise in luminosity error across all frequencies. Both the Damera-Venkata et al 7 and Kolpatzik-Bouman 6 methods produced the lowest luminosity errors in the low frequency region, which is the frequency most sensitive to human eyes. The Damera-Venkata method yielded a much smoother curve indicating a more uniformly distributed color patterns than the KolpatzikBouman method.…”

“…The MBVC uses the FloydSteinberg filter with an added postprocessing that lengthens the computing time. The Kolpatzik-Bouman 6 and DameraVenkata et al 7 methods perform error diffusions similar to Floyd-Steinberg's method except they both have 36 matrix elements in RGB space including in-plane and cross-plane interconnections and they both take a few times longer than Floyd-Steinberg. However, in all cases, the computation can be finished in a couple of seconds.…”

“…Various vector error diffusion algorithms were also proposed to perform the quantization step jointly for the entire color vector to better control the output colors. [7][8][9] Shaked et al 10,11 developed an algorithm that incorporated the minimal brightness variation criterion ͑MBVC͒ design rule to limit the range of brightness in local areas of a halftone. These methods generally yield better color halftone textures than do the scalar versions.…”

Multi-Class Error-Diffusion with Blue-noise Property

“…It traditionally exists in the area of color printing, where a limited number of colorants are used to reproduce a continuous-tone color image (Baqai et al, 2005). Many studies focus on solving this problem using ED technique, which is called vector error-diffusion in some literature, because they quantized the input signals simultaneously by treating them as a vector (Haneishi et al, 1996;Kang, 1999;Damera-Venkata et al, 2003). For example, the vector ED algorithm proposed in (Damera-Venkata and Evans, 2001) uses an optimum matrix-valued error filter to take into account the correlation among color planes.…”

Blue noise digital color halftoning with multiscale error diffusion

“…[1][2][3] To a certain extent, it can be considered as an evolution of digital monochrome halftoning in which a continuous-tone monochrome image is rendered with a binary image. [1][2][3] To a certain extent, it can be considered as an evolution of digital monochrome halftoning in which a continuous-tone monochrome image is rendered with a binary image.…”