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Capturing and optimising digital images for research Gilles Couzin

Capturing and optimising digital images for research Gilles Couzin

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Page 1: Capturing and optimising digital images for research Gilles Couzin

Capturing and optimising digital images for research

Gilles Couzin

Page 2: Capturing and optimising digital images for research Gilles Couzin

Introduction

• Who are you?• What experience do you have of creating

Web sites?• What are your reasons for attending this

course?

Page 3: Capturing and optimising digital images for research Gilles Couzin

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

Page 4: Capturing and optimising digital images for research Gilles Couzin

• The binary counting system

• From analogue to digital

• The digital image

• Spatial resolution

• Colour resolution (bit depth)

• Colour representation

Introduction to digital imaging

Page 5: Capturing and optimising digital images for research Gilles Couzin

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 byte1-bit 21 2 possible values

2-bit 22 4 possible values

4-bit 24 16 possible values

8-bit 28 256 possible values

24-bit 224 16.7 million possible values

48-bit 248 281 trillion possible values

Page 6: Capturing and optimising digital images for research Gilles Couzin

• 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)

Page 7: Capturing and optimising digital images for research Gilles Couzin

From analogue to digital (2)

Continuous bright- ness curve of an analogue image

Same curve after digitisation into 16 discrete levels (4-bit)

Page 8: Capturing and optimising digital images for research Gilles Couzin

• 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

Page 9: Capturing and optimising digital images for research Gilles Couzin

• 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)

Page 10: Capturing and optimising digital images for research Gilles Couzin

Spatial resolution (2)

50 x 35 pixels = 1750 total; 5:1 250 x 175 pixels = 43,750 total; 1:1

Page 11: Capturing and optimising digital images for research Gilles Couzin

• 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 (21) levels (B&W)

• A pixel with a colour depth of 4 bit has 16 (24) levels

• A pixel with a colour depth of 8 bit has 256 (28) levels

Colour resolution (bit depth)

Black & white (1-bit/pixel) 16 greys (4-bit/pixel) 256 greys (8-bit/pixel)

Page 12: Capturing and optimising digital images for research Gilles Couzin

• 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.

Page 13: Capturing and optimising digital images for research Gilles Couzin

• Two models of colour representation:

Colour representation (2)

Page 14: Capturing and optimising digital images for research Gilles Couzin

• 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)

Page 15: Capturing and optimising digital images for research Gilles Couzin

• RGB colour workspaces:• Refers to the gamut (range of

colours that can be displayedor printed by a specific device

• sRGB: standard colour spacefor computer monitors, webbrowsers, etc.

• Adobe RGB (1998):recommended RGB editingspace for print output, beforeconversion to CMYK

Colour representation (3)