Second-generation CMOS-SEED smart pixel implementation of the error diffusion neural network

Shoop, Barry L.; Sadowski, Robert W.; Dudevoir, G.P.; Hall, Dawn L.; Sayles, Andre H.; Ressler, Eugene K.

doi:10.1109/leosst.1998.690410

Cited by 1 publication

(2 citation statements)

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“…A second architecture-based approach is to exploit the commonality between error diffusion and neural networks. [12][13][14][15][16][17][18][19] A third approach uses quad-tree structures. [20][21][22][23] With these algorithms, the degree of localization depends on the level in the tree at which the processing is being performed.…”

Section: Introductionmentioning

confidence: 99%

“…24 Finally, we can exploit instruction-level parallelism of a given processor, 25,26 or inherent parallelism in a very large scale integrated implementation. 13,14 In this paper, we introduce a pinwheel error diffusion algorithm in which the image is divided into two groups of non-overlapping blocks. The blocks in each group can be processed in parallel.…”

Section: Introductionmentioning

confidence: 99%

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Block interlaced pinwheel error diffusion

Allebach

2005

J. Electron. Imaging

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Error diffusion is a popular halftoning algorithm that in its most widely used form, is inherently serial. As a serial algorithm, error diffusion offers limited opportunity for large-scale parallelism. In some implementations, it may also result in excessive bus traffic between the on-chip processor and the off-chip memory used to store the modified continuous-tone image and the halftone image. We introduce a new error diffusion algorithm in which the image is processed in two groups of interlaced blocks. Within each group, the blocks may be processed entirely independently. In the first group, the error diffusion proceeds along an outward spiral from the center of the block. Errors along the boundaries of blocks in the first group are diffused into neighboring blocks in the second group, within which the error diffusion spirals inward. A tone-dependent error diffusion training framework is used to eliminate artifacts associated with the spiral scan paths. We demonstrate image quality that is close to that achieved by conventional line-by-line error diffusion.

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