1. Numbers in Images GCNU 1025 Numbers Save the Day

2. Announcement In-class Assignment #3 on Nov 21 (Friday) Coverage: Chapter 3 “Numbers on the Internet” 10% of final score Books, notes, other materials and discussions all allowed Help from instructor and teaching assistant Assignments submitted after class subject to penalty

3. Image storage in computers Pixel: small coloured point (smallest component of image) Colour represented by number: one digital value for each pixel How many different possible colours are there? How many bits are used to represent each colour?

4. Image storage in computers Pixel: small coloured point (smallest component of image) Colour represented by number: one digital value for each pixel How many different possible colours are there? Colour palette: a list of available colours How many bits are used to represent each colour? Example: RGB models Encoding colour by mixing red (R), green (G) and blue (B)

5. Image storage in computers Pixel: small coloured point (smallest component of image) Colour represented by number: one digital value for each pixel How many bits are used to represent each colour? Example: RGB model (colour-depth: 8-bit) Red/Green (3 bits each): from 0 to 7 0: least intense 7: most intense Blue (2 bits): from 0 to 3 0: least intense 3: most intense Total: 256 colours

6. Image storage in computers Pixel: small coloured point (smallest component of image) Colour represented by number: one digital value for each pixel How many bits are used to represent each colour? Example: RGB model (colour-depth: 24-bit) Red/Green/Blue (8 bits each): from 0 to 255 0: least intense 255: most intense Total: more than 10M colours

7. Image storage in computers Pixel: small coloured point (smallest component of image) Colour represented by number: one digital value for each pixel How many bits are used to represent each colour? Example: RGB model (colour-depth: 24-bit) Red/Green/Blue (8 bits each): from 0 to 255 0: least intense 255: most intense Total: more than 10M colours

8. Image storage in computers Pixel: small coloured point (smallest component of image) Colour represented by number: one digital value for each pixel How many bits are used to represent each colour? Example: RGB model (colour-depth: 24-bit) Red/Green/Blue (8 bits each): from 0 to 255 0: least intense 255: most intense Total: more than 10M colours

9. Resolution and file size

11. Image file format File format: method of storage Image compression: lossless/lossy

12. Image file format File format: method of storage Image compression: lossless/lossy

13. Image processing

14. Manipulation of image (image processing): operation on matrix representing the image 3-bit grey scale examples (0-7) used in this course Major types of actions: Reduction of image Rotation of image Cropping of image Darkening/brightening of image Inversion of image Blurring of image Enlargement of image Morphing of images Addition/removal of objects in image

15. Image processing Reduction of image by half

16. Image processing

18. Rotation by 90 degrees Simple rotation of matrix

19. Image processing Cropping of image Deletion of corresponding part of matrix

20. Image processing Darkening of image Multiplication by weight factor (smaller than 1)

21. Image processing Darkening of image Multiplication by weight factor (smaller than 1) Example: darkening by a factor of 0.8 Rounding off needed

22. Image processing

24. Blurring of image Averaging neighbourhood of each entry

25. Image processing

26. Enlargement of image Step 1: determine size of new matrix Step 2: duplicate existing entries Step 3: blurring Example: doubling linear size Step 3: blurring

27. Image processing Morphing of images Matching the corresponding entries of the matrices and adjusting the weighting Example: morphing of lion and tiger Matching eyes of lion with eyes of tiger Adjustment of weighting

28. Image processing Addition of objects Application of “if…then” statements to add background Example: replacing white background

29. Image processing Removal of objects Series of photos needed to provide background information Median averaging: elimination of abnormal pixels by taking median value of corresponding pixels in the images

30. Numbers in Images -End-