Fast Text/Graphics Resolution Improvement Using Wavelet Based Denoising and Chain-

1. * Onur G. Guleryuz and Anoop Bhattacharjya Fast Text/Graphics Resolution Improvement Using Wavelet Based Denoising and Chain- Code Table Lookup oguleryuz@erd.epson.com, anoop@erd.epson.com Please view in full screen presentation mode to see the animations. Epson Palo Alto Laboratory 3145 Porter Drive, Suite 104 Palo Alto, CA 94304 * Presenting Author

2. Summary PROBLEM: In many cases, text/graphics images that have been created at a particular resolution have to be printed/displayed at higher resolutions offered by printers and other devices. AIM OF THIS WORK: Given a labeled text/graphics image at a particular resolution, produce the same image at a higher resolution with better quality using signal processing algorithms. (Higher resolutions can be higher grid/dpi resolutions and/or same grid/dpi resolution but more color planes, sub-dpi resolution, etc. ) Please see some examples first

3. Resolution Improvement for Text/Graphics - Examples (a) Original: 300dpi (b) Processed : 300 dpi plus grayscale (Obvious aliasing artifacts) (Resolution improved)

4. Examples - Text (a) Original: 300dpi (b) Processed: 300 dpi plus grayscale

5. Examples - Text (a) Original: 300dpi (b) Processed : 300 dpi plus grayscale

6. Examples - Graphics (a) Original: 300dpi (b) Processed : 300 dpi plus grayscale

7. Examples - Upsampling (a) Original: 12 pt at 300dpi (b) Processed: 300 dpi plus grayscale (c) Processed: 600 dpi plus grayscale

8. Examples - Upsampling (a) Original: 300dpi (b) Processed: 600 dpi plus grayscale

9. (c) Nearest neighbor (d) Bilinear (Compare to earlier slide for reference.)

10. Examples - Upsampling (a) Original: 300dpi (b) Processed: 600 dpi plus grayscale

11. (c) Nearest neighbor (d) Bilinear (Compare to earlier slide for reference.)

12. Examples – Sub-dpi resolutions dots per inch (a) Original: 300dpi (b) Processed: 300 dpi plus Pulse Width Modulation (Modern laser printers can put a single pulse of varying width inside each dot.)

13. Main Algorithm For each text/graphics pixel that is on the boundary of a text graphics object: • Determine a local boundary segment. • Parameterize boundary segment in terms of x-y coordinates two 1-D signals. • Denoise/smooth each coordinate smoothed boundary segment. • Render current pixel to “new position” using smoothed boundary/coordinates.

14. Algorithm Flow Denoise using wavelets Render (Repeated for each boundary pixel.) (Coordinate parameterization is on an upsampled grid, please ask the presenter why.)

15. Algorithm Flow with LookUp Tables LUT Denoise using wavelets Render (Access to LUT is via the “differential” chain code of the boundary segment.) (Several types of LUTs possible.)

16. Rendering (detail) : occupancy at pixel p, 0-100% 0-1 : input color at pixel p : output color at pixel p (Please ask the presenter for an explanation.)

17. Implementation Huge data sizes on embedded devices Tight implementation 7 pixel length boundary segments (Performance on near horizontal/vertical boundaries improves as segment length increases.) Only 4 directions searched in boundary trace out of the 7 possible. (Reduces LUT size without a big imapct on performance.) Daubechies 7-9 bank for wavelets.

18. FAQ • Why denoising?A: We consider the jagged boundary as a noisy version of an underlying smooth boundary. • Why wavelets?A: Dealing with 1-D signals with localized singularities (e.g., our coordinate parameterization) is a strength of wavelets. Also, as the upsampling amount increases there are benefits to using multiresolutional denoising. Please ask the presenter how DCTs, Fourier transforms, etc., perform on these two issues. • Aren’t text/graphics scalable, why do this?A: There are many applications involving hand generated data, scanned data, bitmap data, data on PDAs and client/server scenarios, etc., where scalable text/graphics is not available. Furthermore, even for scalable text/graphics, computationally easy conversion to sub-dpi resolutions (e.g., printers) or increased color planes (e.g., computer monitors) may be required. • Is it possible to have more than one boundary segment for a pixel?A: Yes, these are handled in turn and correctly accounted for in the rendering stage.

19. FAQ (contd.) • I see some rendering mistakes, can you improve rendering?A: Yes, in the shown examples rendering uses box filters. More sophisticated filters like Gaussians provide better performance but increase complexity a little bit. Please ask the presenter about further details. • What does the computational complexity involve?A: The boundary trace and the accumulations during rendering. • Do you compute wavelet transforms online?A: No, all serious computation is done offline and the results are stored in lookup tables. • Can you upsample by other amounts?A: Yes, the algorithm can do 4X, 1.5X, etc.

20. Conclusion • Fast, computationally simple algorithm. • Computationally complex steps delegated to lookup tables. • Direct access to lookup tables without intermediate steps (as opposed to pattern matching based approaches which have to find a match). • Resolution conversion by integer and non-integer factors. • Resolution conversion to same grid resolution but increased color planes or sub-dpi resolutions. • Easy incorporation of application specific constraints like serif/corner preservation for text, selective denoising based on input boundary segment classification, etc.