Representing Images
Learn how Images are represented through 0s and 1s.
Last updated
Learn how Images are represented through 0s and 1s.
Last updated
As you already know, computers often have to store and process real life data. However, for it to be able to process the data, the data must be in binary format, as computers can only understand 0s and 1s. Computers use discrete values (in binary) in order to represent the images, and the data is processed in discrete steps. This is called digital data.
On the other hand, natural data is called analogue data. This is because the data is represented in continuous manner and there are no discrete values.
Humans are able to see images because of the light which is reflected by the objects they are looking at. Light is transmitted as waves, and because of that, changing wavelengths produces different colours which can then be detected by the eyes.
There are infinite number of possible colours due to them being analogue, however humans cannot detect them as human vision is limited. When a photograph is taken via a digital camera, the computer needs to capture and approximate the colours as discrete values.
When a digital camera captures an image, it breaks up what it sees through its lens into a grid of pixels. The light sensor of the camera measures the intensity of the colour in each pixel. Then each of the measurement (intensity of colour) is converted into binary using an electric component called analogue-to-digital convertor.
Bitmapped graphics are created using a grid of pixels, where each pixel is assigned a binary value which determines the colour of the pixel.
Common Bitmap filetypes: BMP, JPG, GIF, PNG and TIF.
Resolution refers to the number of pixels used to represent the bitmap image. Often, resolution is expressed as the number of dots per inch squared, where a dot is a pixel.
Resolution is defined as width × height
A typical bitmap resolution might be 1000 × 1000
This would mean that a total of 1,000,000 pixels would be used
Typically, this would be referred to as 1 megapixel image
Resolution doesn’t define the actual physical size of an image.
If an image is enlarged, the size of each pixel grows in order to maintain the required resolution.
This is why there is a deterioration in quality (pixelation) when they are resized.
The image above shows the concept of creating bitmapped images. Each value represents a different colour, using one bit per pixel only allows two possible colours, as there can only be two values (0 and 1). 0=BLACK, 1=WHITE
This new image shows the idea that having more bits per pixel increases the number of possible colour combinations. The number of bits per pixel is called the image's colour depth.
A higher bit depth gives a greater range of colour and a better quality of image. As a consequence, increasing the bit depth also increases the file size.
Colour values are usually expressed as denary RGB values and in hexadecimal, and not in binary. This is done to make it easier for graphics designers to memorise different colour codes, and also it takes less space on the screen when writing.
RGB values range from 0-255, this tells us that there are 256 variation of the colours, this tells us that 8 bits are used to represent each colour in binary. There are three sets of 8, because of (Red, Green, Blue) -> 8 * 3 = 24 so 24 bits are used to represent RGB colours.
In some cases, you will see 32-bits being used to represent colours. In that case, the last 8 bits are used to represent the transparency of the colours.
Image file size is determined by the number of pixels used and the colour depth. Therefore, the formula to calculate the minimum file size is: N of pixels * colour depth
The primary reason why we call this a minimum value is due to the fact that files also have metadata which also adds to the file size.
This is when geometric objects and shapes such as rectangles, circles and lines are used to create images. The properties (e.g. dimensions and colours) of each object or shape in the image are stored in a list.
Can be rescaled without losing quality
Can become blurry and pixelated when enlarged
Well suited to simple images that use shapes, but not good for photographers
Used for storing photos
Frequently use less space
Often use more space than vector graphics
Metadata is simply data about data and it is stored in the same file as the image data.
Metadata includes things like:
Data creation date
Width and height of the image
Colour depth
Camera details
GPS coordinates of where the image was takes
[Please note that metadata is not exclusive to image files]
