1. Capturing and optimising digital images for research Gilles Couzin

2. Introduction Who are you? What experience do you have of creating Web sites? What are your reasons for attending this course?

3. Learning objectives By the end of this course, you will be able to: Explain the digitisation process Set scanning parameters to the purpose of your scan Use Serif PhotoPlus to: Crop images and correct perspective problems Adjust tone and colours Sharpen images Scale images Use four different graphic file formats for the right purpose

4. The binary counting system From analogue to digital The digital image Spatial resolution Colour resolution (bit depth) Colour representation Introduction to digital imaging

5. The binary counting system A system that has only two variables, 0 and 1 Each digit is called a bit (binary digit) All computer input is converted into strings of binary code of variable lengths An eight-bit number such as 11011101 = 1 byte 1-bit2121 2 possible values 2-bit2 4 possible values 4-bit2424 16 possible values 8-bit2828 256 possible values 24-bit2 24 16.7 million possible values 48-bit2 48 281 trillion possible values

6. Traditional photographic images are analogue – i.e. they have continuous tones and in theory may contain an infinite number of colours and brightness. Digitisation is the process of converting the information contained in an analogue image into binary data. Scanners and digital cameras are the most common method for ‘capturing’ digital images. From analogue to digital (1)

7. From analogue to digital (2) Continuous bright- ness curve of an analogue image Same curve after digitisation into 16 discrete levels (4-bit)

8. A digital image is a rectangular grid of pixels (or picture elements) A pixel refers to the dots of light on a computer monitor and to the smallest, basic component of a digital image Each pixel is part of a mosaic of many thousand or millions of pixels that form the image The digital image

9. The quantity of pixels in a defined area (dpi or ppi). The frequency at which samples are taken during the scanning process from the analogue image (spi). The number of pixels is the only attribute that counts; physical size expressed in inches/cm is irrelevant. Increasing the number of samples improves the visual quality of the image but… …there is a point at which adding more samples has little visual benefit and some distinct disadvantages. Digital cameras: one mega pixel = one million pixels (in the whole image, not per inch). Spatial resolution (1)

10. Spatial resolution (2) 50 x 35 pixels = 1750 total; 5:1 250 x 175 pixels = 43,750 total; 1:1

11. Measures how much colour information is available to display or print each pixel in an image: A pixel with a colour depth of 1 bit has 2 (2 1 ) levels (B&W) A pixel with a colour depth of 4 bit has 16 (2 4 ) levels A pixel with a colour depth of 8 bit has 256 (2 8 ) levels Colour resolution (bit depth) Black & white (1-bit/pixel) 16 greys (4-bit/pixel) 256 greys (8-bit/pixel)

12. What are colours? the way our brain, by use of our eyes, interprets electromagnetic radiation originating from sun light (white light). Colour representation (1) the part of the electromagnetic spectrum that our eyes can actually detect ("visible light") stretches from between 380 and 780 nanometres in wavelength.

13. Two models of colour representation: Colour representation (2)

14. Colours on a computer monitor: Use the RGB model 8-bit (256 colours) per channel Any colour can be represented by a specific combination of 3 numbers comprised between 0 and 255 – for example: R255 + G255 + B0 = Yellow A 24-bit pixel (8 bit per channel) can display up to roughly 16.7 million, possible colours! Colour representation (3)

15. RGB colour workspaces: Refers to the gamut (range of colours that can be displayed or printed by a specific device sRGB: standard colour space for computer monitors, web browsers, etc. Adobe RGB (1998): recommended RGB editing space for print output, before conversion to CMYK Colour representation (3)