3. Multimedia Representation & Compression

Computers don’t just deal with numbers and text – they also handle images, sound, and video. This chapter explains how multimedia is represented using binary. You’ll learn how bitmaps are built from pixels, how sound is captured using sampling, and how factors like resolution, colour depth, sample rate, and bit depth all influence file size and quality.

You’ll also discover why compression is essential for modern computing. By comparing lossy and lossless compression techniques, you’ll see how files can be made smaller while balancing quality and storage needs. These topics are key to understanding how media is stored, streamed, and shared efficiently in today’s digital world.

A bitmap image is made up of a grid of tiny squares called pixels. Each pixel represents a single point of colour on the screen. To store the image, the computer saves the colour of every pixel as a binary value. These binary values are stored in order, line by line, so the image can be accurately recreated when viewed.

The number of bits used for each pixel depends on the image’s colour depth. A very simple image, like a black and white icon, might use only 1 bit per pixel: 0 for white, 1 for black. A more detailed image – like a photograph – may use 24 bits per pixel, which allows over 16 million colour combinations. Each pixel’s binary value is stored in memory or a file, and when the image is opened, the computer reads the binary data and displays the corresponding colours.

Bitmap images can vary in file size depending on their resolution (number of pixels) and the colour depth. But no matter the complexity, all bitmap images are ultimately just long sequences of binary numbers representing colour values.

Two important factors that affect the appearance and file size of an image are its colour depth and resolution. Colour depth refers to how many bits are used to store the colour of each pixel. The more bits per pixel, the more colours can be represented. For example, 1-bit colour depth allows only 2 colours (typically black and white), 8-bit allows 256 colours, and 24-bit allows for over 16 million colours – suitable for high-quality photographs.

Resolution is the number of pixels that make up the image, calculated by multiplying its width and height. For instance, a resolution of 1920 × 1080 means there are over 2 million pixels in the image. Higher resolution means more detail, but also results in larger file sizes because there are more pixels to store.

Metadata is additional information stored with the image. It includes the dimensions of the image, the colour depth, the file type (like .jpg or .png), and sometimes extra details like date created or author. While metadata doesn’t affect how the image looks, it’s essential for software to understand how to open and display the file correctly.

As either the colour depth or resolution increases, the file size also increases because more binary data is needed to store all the pixel information.

To calculate how much storage space a bitmap image will use, we need to consider three things: the image’s width, height, and colour depth. The formula used is:

Width × Height × Colour depth (in bits) = File size (in bits)

This tells us the total number of bits needed to store the colours for every pixel in the image. Once you have the result in bits, you can convert it into more useful units by dividing by 8 to get bytes, and then again by 1,000 to get kilobytes (KB).

For example, a 200 × 300 image with a 24-bit colour depth is calculated as:
200 × 300 × 24 = 1,440,000 bits
Divide by 8 → 180,000 bytes
Divide by 1,000 → 180 KB

This file size represents just the pixel data. Real image files may be slightly larger due to metadata or smaller if compression is used (like JPEG). However, this formula is ideal for estimating raw uncompressed image sizes.

Unlike images, which are made up of pixels, sound is a continuous wave. To store sound digitally, computers must convert this wave into binary using a process called sampling. This involves taking measurements of the sound wave’s amplitude (volume) at regular time intervals – known as the sample rate.

Each of these samples is then converted into a binary number, using a fixed number of bits called the bit depth. For example, with 16-bit audio, each sample is stored using 16 bits, allowing for 65,536 possible volume levels. The more samples taken per second (higher sample rate) and the more bits used for each sample (higher bit depth), the better the quality of the sound – but this also leads to larger file sizes.

A standard example is CD-quality audio, which uses a sample rate of 44,100 samples per second and a bit depth of 16 bits. This means that every second of audio requires storing 44,100 binary numbers, each 16 bits long, per channel (e.g., two channels for stereo sound).

To calculate the file size of a digital audio recording, we need to consider how the sound is sampled and stored. Three main factors affect the size: the sample rate (how many samples are taken per second), the bit depth (how many bits are used for each sample), and the duration of the recording in seconds. If the audio is recorded in stereo, the number of channels (usually 2) is also included.

The formula used is:
Sample rate × Duration × Bit depth × Channels = File size (in bits)

For example, a one-minute CD-quality stereo recording uses:
44,100 samples/sec × 60 seconds × 16 bits/sample × 2 channels = 84,480,000 bits
Divide by 8 to get bytes: 10,560,000 bytes
Divide by 1,000,000 to get megabytes: ≈10.56 MB

Increasing any of these values improves the audio quality, but also increases the file size. This is why professional audio files are often very large, while compressed music files use tricks to shrink the data.

Digital files such as images, audio, and video can take up a lot of storage space. This can make them slow to upload, download, or transfer. Compression is used to reduce file sizes so that they use less memory and are easier to share. This is especially important when sending files over the internet or storing large amounts of data on a device.

Compression can be applied to many types of files, including documents, pictures, music, and programs. Some types of compression remove unnecessary detail, while others keep all the original data intact. Either way, the goal is the same: to reduce the number of bits needed to store or transmit a file.

Common examples include JPEG for images, MP3 for audio, and ZIP for compressed folders containing multiple files. Using compression not only saves space but also helps speed up website loading, streaming services, and cloud backups.

Lossy compression is a method of reducing file size by permanently removing data that is considered less important or less noticeable. It takes advantage of the way the human eye and ear work – by removing details we’re unlikely to detect. For example, in a photograph, similar colours may be merged together. In music, very quiet sounds that are masked by louder ones may be discarded.

This results in much smaller file sizes, which makes lossy formats ideal for web images, music downloads, and streaming videos. However, because some data is lost, the quality of the file is slightly reduced. In most cases, this drop in quality is so small it isn’t noticeable – but if the file is compressed too much, the loss becomes obvious.

Common lossy formats include JPEG for images, MP3 for audio, and MP4 for video. These formats are widely used because they balance good quality with small size, making them practical for everyday use.

Lossless compression reduces file size without losing any original data. It works by identifying repeated patterns within the file and storing them more efficiently. For example, if an image has a long row of white pixels, instead of storing each pixel separately, the compression can record "100 white pixels" as a single instruction. This saves space but allows the file to be reconstructed exactly as it was before compression.

Unlike lossy methods, lossless compression does not remove any detail, so there is no drop in quality. This makes it perfect for files where accuracy is essential, such as text documents, software code, or high-quality audio recordings. When the file is opened, every single bit is restored to its original place.

Common lossless formats include PNG for images, FLAC for audio, and ZIP for compressed folders and multiple files. These formats ensure that nothing is lost in the process, which is especially important when the data must remain unchanged.

When choosing between lossy and lossless compression, it’s important to understand the trade-offs between file size and quality. Lossy compression creates much smaller files by removing some data permanently, which can slightly reduce quality. It’s not reversible – once the detail is removed, it cannot be recovered. This makes lossy ideal for everyday use where small size is more important than perfect quality, such as streaming music or viewing photos online.

On the other hand, lossless compression keeps all the original data and can reconstruct the file exactly. This results in larger file sizes but preserves full quality, making it suitable for files where precision matters – such as documents, code, or high-resolution images used for editing.

The choice between lossy and lossless depends on several factors:

• The type of file (e.g. photo, audio, document)

• Whether perfect accuracy is needed

• The amount of storage space available or how quickly the file needs to be shared
  
  

Understanding when to use each method helps you manage digital media more efficiently.