IDL GUI for Digital Halftoning Final Project for SIMG-726 Computing For Imaging Science Changmeng Liu

Outline Objective Digital Halftoning Background Halftoning algorithm Resolution of the display device and Human Vision MTF and CSF GUI design and demo Conclusion and Future Work

Objective A IDL GUI is implemented to display the halftone image created by different Digital Halftoning algorithms. The GUI can simulate the Halftone image in different resolution Dispersed-dot, Clustered-dot with different orientation, and Random dithering algorithms are implemented

What is Halftone and Why Halftone A process converts a gray scale image into a bi-level image or more generally to fewer gray levels. Applications: a. Printing images b. Display images with low-end display unit c. compressing images & video clips

Continuous Tone image vs. Halftone image Continuous tone image: every pixel has its continuous valued gray level, for example from 0 to 255 Halftone image: every pixel only has two gray levels: 0 and 1 Example of continuous tone and halftone image

Halftone cell --- Resolution vs. Represent gray level There is a trade off in halftone technique, the resolution vs. represent gray level Larger halftone cell can represent more gray level, but larger halftone cell will reduce the resolution of the halftone image. Note: Error diffusion has no such limitation

Halftone Algorithm The Ordered Dithering Algorithm flow chart image screen halftone compare pixel-by-pixel 1 if image > screen 0 otherwise 8 bpp ready for printing on a binary printer 8 bpp 1 bpp

Cluster-dot Dithering In its simplest form, a digital version of analog methods Used in higher resolution printing devices where system response is nonlinear Clustering the black pixels yields repeatable, low noise images with acceptable tone reproduction characteristics

Screen Cell The screen is usually constructed from small rectangular bricks, called cells This screen is constructed from 3x6 cells; the cells are offset by 3 pixels. Single Cell Construction. The screen on the left with this cell form a 45 degree screen

Halftone Cell Orientation Human Vision have lowest contrast sensitivity at 45° For monochrome screens (e.g., B&W), cells are typically nx2n in dimension and offset so the lowest frequency component is at 45°, where the visual MTF is lowest. m m 0°0° 2n2n n 45°

Dispersed-Dot Dithering Dispersed-dot produces finer image details, and its halftone texture pattern contains much higher frequency and therefore is less visible Bayer’s mask is used as the dispersed-dot dithering mask in this project

Dispersed-Dot Mask Design Recursively Define mask M (k), for K>0 M (k) has dimensions 2 k * 2 k

Random Dithering White noise dithering use an uniformly distributed uncorrelated noise as threshold function. Random dithering has visible low frequency noise

Resolution of the display device DPI refers to the number of dots (pixels) per inch on a screen. Macintosh computer (or MacOS compatible), have a 72 dpi screen. Windows PC have a 96 dpi screen. Because of this, objects that are displayed under Windows will appear to be 133% of their printed size (at default 72 dpi). Printer have 72 dpi (default), 300dpi, 600dpi or even higher resolution.

Question Can I Simulate the High Resolution Halftone Image (300 dpi) with a Low Resolution CRT(96 dpi)? - No, you can’t display it by just down sampling the high resolution halftone image directly because of the Aliasing. - But, you can simulate the appearance of the high resolution ht image in CRT.

Human Visual MTF Human Visual MTF is a kind of low pass filter

Human Visual CSF Contrast Sensitivity Function of Human Visual system is a band pass filter From the MTF and CSF, the high spatial frequency in high resolution printer can’t be “seen” by human eye

Simulate different resolution halftone image on CRT Up-sample (or scale) the image to high resolution size. Applying halftone algorithm Convolve the halftone image with the low pass filter to simulate what our eye sees Down-sample the low-passed image to real size

GUI design, flow chart Dithering Image Input Halftone Algorithm Select Display Resolution Select Halftone Image Display Save the Halftone Image Input Image Display

Output 1, 0° Cluster-dot at 96 dpi

Output 2, 0° Cluster-dot at 300 dpi

Output 3, 45° Cluster-dot at 96 dpi

Output 4, 45° Cluster-dot at 300 dpi

Output 5, Dispersed-dot at 96 dpi

Output 6, Dispersed-dot at 300 dpi

Output 7, Random dithering at 96 dpi

Output 8, Random dithering at 300 dpi

Conclusion and Future Work This GUI can simulate the different resolution halftone image display Cluster-dot ( 45° and 0° ), disperse-dot, and random dot is implemented. The convolution kernel can be changed to fit the human visual MTF and CSF better. More halftone algorithms can be implemented.

Question?