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Binary genetic algorithm-based pattern LUT for grayscale digital half-toning

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Abstract

Grayscale digital half-toning is a popular technique to reproduce grayscale images with devices that can support only two levels at output, i.e., black and white. Printers, LCD displays, etc. are some common examples of such devices. Considering 0 and 1 as black and white, respectively, this can be represented as an image-wise binary pattern generation process. The binary patterns are aimed to retain the local tonal and structural characteristics of grayscale image for a faithful illusion of the original grayscale image. Apart from tonal and structural characteristics retention, desired blue-noise characteristics also contribute significantly toward eye pleasant appearance of half-tone images. The paper presents a binary genetic algorithm-based approach to generate such binary patterns through optimizing randomly generated binary strings against a visual cost function. Paper also presents a pattern look-up-table (LUT)-based approach toward conventional clustered dot ordered dithering which is suitable for devices like laser or offset printers that cannot recognize individual pixels. The pattern LUT approach is driven toward green-noise characteristics instead of the blue-noise characteristics. The results obtained with test images are presented pictorially and evaluated through half-tone quality evaluation metrics. The evaluation results and comparison with state-of-art techniques shows the potential of presented technique for practical implementations.

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Abbreviations

f :

Radial spatial frequency in cycles/degree

f x :

Radial spatial frequency in horizontal direction

f y :

Radial spatial frequency in vertical direction

k :

Viewing distance in inch

η :

Positive integer expressing the frequency quantization

Δ:

Spacing between two individual addressable points

θ :

Angle between f x and f y

s(θ):

Measure for directional artifact sensitivity of HVS

k mn :

Viewing distance for pixel located at (m, n) in inch

l mn and s mn :

Image characteristics driving the adaptive HVS filter

g(m, n):

Original grayscale image with (m, n) as pixel position index

h(m, n):

Halftone image with (m, n) as pixel position index

dm and dn :

Image characteristics driving s mn

M × N :

Image size

D m :

Distance map used for HVS filter

μ g :

Local average intensity of HVS filtered original image

μ h :

Local average intensity of HVS filtered half-tone image

L gh :

Tonal similarity measure between original and half-tone images

S gh :

Structural closeness parameter between original and half-tone images

σ g :

Local standard deviation in HVS filtered original image

σ h :

Local standard deviation in HVS filtered half-tone image

σ gh :

Cross-correlation factor between original and half-tone images

p mn :

Minority pixel value in halftone

d p :

Principle distance between minority pixels

d mn :

Distance between minority pixel at (m, n) and its nearest minority pixel

D gh :

Distortion measure to address blue-noise characteristic

h mn :

Binary value of halftone image at (m, n)

\({\varphi}\) :

Visual cost function

w 1 , w 2 , w 3 :

Weights that can be used for prioritizing parameters in \({\varphi }\)

C p :

Crossover probability in BGA

μ :

Mutation rate in BGA

g :

Constant grayscale value

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Authors and Affiliations

  1. Department of Printing Engineering, Jadavpur University, Kolkata, 700098, India

    Arpitam Chatterjee & Kanai Ch. Paul

  2. Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata, 700098, India

    Bipan Tudu

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  1. Arpitam Chatterjee
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Correspondence to Arpitam Chatterjee.

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Chatterjee, A., Tudu, B. & Paul, K.C. Binary genetic algorithm-based pattern LUT for grayscale digital half-toning. SIViP 7, 377–388 (2013). https://doi.org/10.1007/s11760-011-0255-3

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  • DOI: https://doi.org/10.1007/s11760-011-0255-3

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