The document discusses the representation of graphics and images, highlighting the use of pixels to form images and the concepts of bitmap and vector graphics. Bitmap images handle each pixel individually, allowing for high-quality details but with varying file sizes due to different compression methods, whereas vector images use mathematical representations resulting in smaller file sizes and scalable quality. Additionally, the document explains storage calculations based on pixel count and bit depth, showcasing differences in color representation between formats.

1. Multimedia Systems
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2. Graphics and Images Representation
 Images are commonly used to capture and represent a static visual
snapshot of the world around us. The world, however, is not static,
but continuously changes in time.
 To record this change, we need to capture the time evolution of
changing images (video) and sound (audio).
 The digital media forms need to be represented and stored so that
they can be viewed, exchanged, edited, and transmitted in a standard
manner.

3. Graphics and Images Representation
 An image on a screen is made up of dots called pixels.
 A pixel is the smallest part of the screen that can be controlled by
the computer or other device.
 The total number of pixels on a screen is called its resolution. (ie
New iPad has Retina display, 2048 x 1536 resolution).

4. Graphics and Images Representation
 An image can be represented in two different ways. Either a
Bitmap or a Vector.
 Typical file formats for a bitmap can be: JPEG, GIF, PNG and
BMP.
 Vector images can be SVG, WMF and EMF.

5. Bitmap Image
Bitmap images treat each dot in an image separately. These dots or pixels
can be different colors with each color being represented as a binary
number.
Bitmaps produce good quality images where shading and detail are needed.
Bitmap images are often compressed to reduce their file size for
storage.
Some of these compression formats will not alter the image
quality. This is called lossless compression however others will
remove some parts of the image. This is known as lossy
compression.

6. Bitmap Image
 To calculate the uncompressed storage needed for a bitmap you
will need to calculate the total number of pixels and multiply
by the color or bit depth.
 Color depth refers to the maximum number of colors an image
can contain.
 bit depth of an image is the number of binary bits that define the
shade or color of each pixel in a bitmap.
 Color depth is determined by the bit depth.

7. Bitmap Image
 For example, a pixel with a bit depth of 1 can have two values:
black and white.
 The greater the bit depth, the more colors an image can contain,
and the more accurate the color representation is.
 For example, an 8-bit GIF image can contain up to 256 colors,
but a 24-bit JPEG image can contain approximately 16 million
colors.

8. Bitmap Image
 Example:
If an image is 1200 by 800 pixels, the total number of pixels will
be 960,000.
If the bit depth is 24 then each pixel needs 3 bytes of storage
therefore the total file size will be
960,000 X 3 =2,880,000.
The size of this image in kilobytes will be 2,880,000 divided by
1024 = 2812.5 kB.
Divide this by 1024 to convert to megabytes. The image would
require 2.74MB of storage.

9. Vector Images
 Vector images or graphics are made up of objects such as
straight lines, curves or shapes.
 Each portion of the image is represented mathematically.
 Each object is defined by its characteristics such as positions,
width of the lines and patterns.
 The total size of the data required to represent a vector image is
usually less than that of an equivalent Bitmap image.
 Vector images can be resized to any required resolution without
loosing clarity.