scholarly journals Linear color-separable human visual system models for vector error diffusion halftoning

2003 ◽  
Vol 10 (4) ◽  
pp. 93-97 ◽  
Author(s):  
V. Monga ◽  
W.S. Geisler ◽  
B.L. Evans
Keyword(s):  
Visual System ◽  
Human Visual System ◽  
Error Diffusion ◽  
System Models ◽  
Vector Error

scholarly journals Computational experiment of error diffusion dithering for depth reduction in images

2020 ◽  
Vol 1 (2) ◽  
pp. 9-13
Author(s):  
Leonardo Rezende Costa
Keyword(s):  
Visual System ◽  
Human Visual System ◽  
Computational Experiment ◽  
White Paper ◽  
Error Diffusion ◽  
Visual Effect ◽  
Black And White ◽  
Halftone Images ◽  
Depth Reduction ◽  
As If

The halftone technique is a process that employs patterns formed by black and white dots to reduce the number of gray levels in an image. Due to the tendency of the human visual system to soften the distinction between points with different shades, the patterns of black and white dots produce a visual effect as if the image were composed of shades of gray and dark. This technique is quite old and is widely used in printing images in newspapers and magazines, in which only black (ink) and white (paper) levels are needed. There are several methods for generating halftone images. In this article we explore dithering with error diffusion and an analysis of different halftone techniques is presented using error diffusion to change the depth of the image. The results showed that the depth of the image changes 1/8 per channel, this halftone technique can be used to reduce an image weight, losing information but achieving good results, depending on the context. ontext.

Human visual system models in Digital Watermarking

2015 ◽  
Author(s):  
Hirak Mazumdar ◽  
Piyush Anand ◽  
Saraswati Jee Soni ◽  
Mridul Joshi ◽  
Kumar Rajeev ◽  
...  
Keyword(s):  
Visual System ◽  
Digital Watermarking ◽  
Human Visual System ◽  
System Models

Chorus of k prototypes or discord of contradictory representations?

1998 ◽  
Vol 21 (4) ◽  
pp. 467-468
Author(s):  
David R. Andresen ◽  
Chad J. Marsolek
Keyword(s):  
Visual System ◽  
Human Visual System ◽  
Shape Recognition ◽  
Visual Representation ◽  
Representation Space ◽  
Single System ◽  
System Models

The human visual system is capable of learning both abstract and specific mappings to underlie shape recognition. How could dissimilar shapes be mapped to the same location in visual representation space, yet similar shapes be mapped to different locations? Without fundamental changes, Chorus, like other single-system models, could not accomplish both mappings in a manner that accounts for recent evidence.

Alpha stable human visual system models for digital halftoning

2006 ◽  
Author(s):  
A. J. González ◽  
J. Bacca ◽  
G. R. Arce ◽  
D. L. Lau
Keyword(s):  
Visual System ◽  
Human Visual System ◽  
Digital Halftoning ◽  
System Models
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