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Dot Diffusion Halftone (Knuth Halftoning, Mese Vaidyanathan Halftoning)

For introduction to Digital Halftoning refer to the following 

 http://imageprocessing-sankarsrin.blogspot.tw/2016/12/digital-half-toning-ordered-dithering.html
http://imageprocessing-sankarsrin.blogspot.tw/2017/04/digital-half-toning-error-diffusion.html


Dot Diffusion techniques is an improvisation of error diffusion with parallel processing feature of Ordered dithering. Basically it consist of two matrix such as Class and Diffusion Matrix.

Class Matrix (CM) - Determines the order in which the pixels are processed in a block. (in other techniques raster or serpentine scan order is adopted). Note: All the blocks will be processed in parallel. 



For example in Knuth Class matrix, '0' indicates the index of the first pixel to be processed and in further, pixels are processed with respect to positions of 1,2..63.

Distribution Matrix - Provides the weight-age of error that has to be distributed to neighborhood pixels



 Abstract 
Digital halftones by dot diffusion - Knuth

This paper describes a technique for approximating real-valued pixels by two-valued pixels. The new method, called dot diffusion, appears to avoid some deficiencies of other commonly used techniques. It requires approximately the same total number of arithmetic operations as the Floyd-Steinberg method of adaptive grayscale, and it is well suited to parallel computation; but it requires more buffers and more complex program logic than other methods when implemented sequentially. A “smooth” variant of the method may prove to be useful in high-resolution printing.

Optimized Dot Diffusion - Mese

The dot diffusion method for digital halftoning has the advantage of parallelism unlike the error diffusion method However  image quality offered by error diffusion is still regarded as superior to other known methods In this paper we show how the dot diffusion method can be improved by optimization of the so called class matrix By taking the human visual characteristics into account we show that such optimization consistently results in images comparable to error diffusion without sacrificing the parallelism 


Cheers..!!

MATLAB Code:


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