Section 3.14 Digital Information and Data Compression (2026)

All data in a computer is represented in binary, using 1s and 0s. How it is represented depends on the type of data. For example, text is represented using ASCII, and images are represented using pixels.

All data is stored in bits, which are the smallest unit of data in a computer. A bit can have a value of either 0 or 1. A group of 8 bits is called a byte, which is 8 bits. A byte can represent 256 different values, from 0 to 255. For example, the letter "A" is represented as 01000001 in binary, which is 65 in decimal.

American Standard Code for Information Interchange (ASCII) is character code that includes 128 characters. It allows us to represent text in binary format. Check out this ASCII to binary table to see how each character is represented in binary. For example, the letter "A" is represented as 01000001 in binary.

ASCII is one type of character code, but 128 characters is not enough for today’s computers, which can represent Chinese, Hindi, and scripts from many other languages. Today’s computers use a system called Unicode, which has more than 100,000 different characters and covers more than 100 different scripts (languages).

A pixel (from picture element) is the smallest unit of a digital image or graphic that can be displayed and represented digitally. A pixel is a single point in a image; so it is the smallest controllable element of a picture represented on the screen. Each pixel is represented by a color value, which can be represented in binary.

In images and web pages, colors are represented using hexcode for Red, Green, and Blue values. Hexcode represents numbers in base 16. Here’s a color mixer app that lets you explore the different colors that are used in Web pages. See how the hexcode for each color is represented in binary. Check out this color mixer app to explore colors and their binary representations.

Data compression is the process of reducing the size of data to save storage space or transmission time. It is an important concept in computer science and is used in many applications, such as image and video compression.

A lossless compression algorithm is one in which no data are lost; the original data can be completely recovered. An example of lossless compression is RLE.

A lossy compression algorithm is one in which some data are lost; the original data cannot be completely restored. An example of lossy compression is JPEG. Lossy data compression algorithms can usually reduce the number of bits stored or transmitted more than lossless compression algorithms.

Images are represented in a computer as a grid of pixels. Each pixel is represented by a color value, which can be represented in binary. The more bits used to represent each pixel, the more colors can be represented, but the larger the image file will be. More bits per pixel means more information, but also more storage space and transmission time. Data compression can be used to reduce the size of image files by reducing the number of bits used to represent each pixel.

The image representation technique demonstrated in the video below is known as run-length encoding (RLE) and it is an image compression technique. Image compression is a type of data compression which can reduce the size (number of bits) of transmitted or stored data. RLE compression is an example of lossless compression and illustrates some of the ways that images and other data can be represented with binary numbers.

JPEG is an example of a lossy compression algorithm. JPEG, which uses the file extension .jpg or .jpeg, is the most common format used today to represent images.The JPEG algorithm was created by the Joint Photographic Experts Group (JPEG), hence its name. The fact that JPEG is a lossy technique means that some of the information present in the image is lost during compression and cannot be recovered. Here is a video lecture on the JPEG compression algorithm. The compression algorithm involves some math, but the video describes just enough of the math so that you can see how JPEG works. The video is a summary of an excellent, more detailed presentation by Randell Heyman -- you should really check out the Heyman video if you are interested in more of the mathematical details.

How do Snapchat filters work? If you or someone you know uses the social media app Snapchat, they have probably used one of those cool filters. But, how exactly do those filters work? Watch the video below to learn more about the algorithm and pixel data behind Snapchat filters.

How are audio files digitized? What about audio files? How are they digitized and converted to bits? Watch the following video for a summary of how audio files are converted from analog to digital format. Analog refers to data with values that change continuously, or smoothly, over time, like sound or music files. Analog data is converted to a digital forms, 0s and 1s in binary, using a sampling technique, which means measuring values of the analog signal at regular intervals (usually in time or space) called samples. The samples are measured to figure out the exact bits required to store each sample. The use of digital data to approximate real-world analog data is a great example of abstraction!

Here’s a fun activity if you have a computer or tablet with a microphone. Go to https://academo.org/demos/virtual-oscilloscope/ or a different audio recorder and see the sound waves that your voice can produce. Think about how they would be represented in 0s and 1s.

Different audio file extensions compress the audio data in different ways. WAV files are huge because they just store snapshots of digitized values at different points of time with no compression. MP3 formatting compresses audio files by removing parts of the audio signal which the human ear cannot easily hear. They save on space while preserving good audio quality.