Computer Programming: Image Editing

• FIELDS OF STUDY: Digital Media; Graphic Design; Software Engineering

ABSTRACT

Image editing software uses computing technology to change digital images. Image editing can involve altering the appearance of an image, such as showing a hot air balloon underwater, or improving the quality of a low-resolution image. Images may also be compressed, which requires less computer storage space. Editing software increasingly uses machine learning algorithms to automate tasks that once required skilled human input. Artificial intelligence (AI)-based tools can remove backgrounds, enhance image resolution, generate new visual content (generative fill), and suggest aesthetic edits. Among the numerous platforms supporting this technology are Adobe Firefly, DALL·E, and Luminar AI.

Overview of Image Editing

There are as many ways of digitally altering images as there are uses for digital art. The first step in image editing is to obtain an image in digital format. The easiest method is to use a digital camera to take a photograph and then transfer the photograph to a computer for editing. Another approach that is occasionally necessary is to scan a print photograph or film negative. This converts the photo to a digital image ready for editing. It is even possible to create an image by hand in native digital format, by using a tablet and stylus to draw and paint. Finally, rendering makes it possible for a computer to produce a digital image from a 2-D or 3-D model.

Once the digital image is available, the next step is to determine what will be done to it. Most often, the image will be enhanced (improving the image quality through interpolation or other techniques), compressed (decreasing the file size by sacrificing some image quality or clarity), or altered (made to depict something that was not originally there). These changes may be through destructive or nondestructive editing. In destructive editing, the changes are applied to the original file. By contrast, in nondestructive editing, they are saved in a separate version file.

In the early days of the Internet, image compression was an especially important type of image editing. Bandwidth was limited then, and it could take several minutes to transmit even a medium sized image file. Image compression algorithms were invented to help reduce the size of these files, with some loss of quality. Lossless compression can avoid degradation of the image, but in most cases, it does not reduce file size as much as lossy compression does.

How Compression Works

Computers store image data as sets of numeric values. Each pixel onscreen is lit in a particular way when an image is displayed, and the colors of each pixel are stored as numbers. For example, if the color black were represented by the number eight, then anywhere in a picture that has three black pixels in a row would be stored as 8, 8, 8. Because an image is composed of thousands of pixels, all of the numbers needed to describe the colors of those pixels, when combined, take up a lot of storage space. One way to store the same information in less space is to create substitutions for recurring groups of numbers. The symbol q1 could be used to represent three black pixels in a row, for instance. Thus, instead of having to store three copies of the value "8" to represent each of the three black pixels, the computer could simply store the two-letter symbol q1, thus saving one-third of the storage space that otherwise would be required. This is the basis for how digital images are compressed.

Most images are compressed using lossy compression algorithms, such as JPEG. Compression, thus, usually requires the sacrifice of some image quality. For most purposes, the reduction in quality is not noticeable and is made up for by the convenience of more easily storing and transmitting the smaller file. It is not uncommon for compression algorithms to reduce the file size of an image by 75 to 90 percent without noticeably affecting the image's quality. Modern image formats, such as WebP, HEIF, and AVIF, offer better compression with much less visual quality loss and are supported by most browsers and operating systems. These formats allow faster image loading times and reduced storage needs, which is particularly important for images in mobile applications.

A Numbers Game

Image enhancement typically relies on the mathematical adjustment of the numeric values that represent pixel hues. For instance, if an image editor were to desaturate a photograph, the software would first recognize all of the pixel values and compare them to a grayscale value. It would then interpolate new values for the pixels using a linear operation. Similarly, a filtering algorithm would find and apply a weighted average of the pixel values around a given pixel value to identify the new color codes for each pixel being adjusted. The median or the mode (most common) value could also be used. The type of filter being applied determines which mathematical operation is performed. Filters are often used to correct for noise, or unwanted signal or interference.

Image Editing Goes Mobile

Image editing became possible on mobile platforms in the early twenty-first century. Apps like Snapseed, Lightroom Mobile, and VSCO provide advanced features such as RAW editing, selective masking, and AI-based filters. These tools cater to content creators who need rapid and professional-quality edits on the go. Certain programs work only on PCs, others strictly on mobile devices, and still others on both. Besides the well-known Adobe Photoshop, modern tools such as Adobe Lightroom, Canva, and GIMP offer powerful editing features for desktop users. AI-driven tools like Runway ML and Topaz AI also provide advanced capabilities, such as background removal, video editing, and image upscaling using machine learning.

Tools like Pixlr and Adobe Express work across web, desktop, and mobile platforms, giving users flexibility between devices. AI-powered editors like Picsart and Fotor also allow for advanced photo editing, filtering, and stylization on the go. For photo collaging and social media content creation, popular options include Pic Stitch, Layout from Instagram, and BeFunky, with many offering drag-and-drop interfaces and integrated templates for reels, stories, and posts.

Apple and Google developed features like Live Photos and Motion Photos. These hybrid formats capture a few seconds of video alongside still images. Many smartphones have built-in tools for motion smoothing, background blur, and scene reconstruction, further expanding how images are captured and manipulated. The shift toward cloud-based platforms has transformed image editing into a collaborative, real-time activity. Tools like Canva, Figma, and Adobe Creative Cloud enable multiple users to work on a project simultaneously from different devices. Version control, auto-save, and integrated feedback loops streamline the editing process in professional settings.

As image editing and generation tools become increasingly powerful—especially with the rise of AI-generated imagery and deepfakes—concerns about digital misinformation and image authenticity have grown.

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