What is Image Optimization?

Image optimization relates to delivering images in the shortest feasible file size while maintaining the highest possible quality in terms of format, dimensions, resolution, and quality. Large, unoptimized images and videos can cause web pages to load slowly and provide a poor user experience. As a result, website owners, developers, and product design teams go to considerable lengths to lower page load times by employing plugins, scripts, and other file-saving tactics. One of the most effective characteristics of image optimization is the ability to change the size and quality of your photographs to a level that still provides good visual output. You can provide images less than half the original size with almost no noticeable change.

Beyond traditional methodologies, the industry is now exploring different machine learning and artificial intelligence (ML/AI)-based solutions for image and video optimization. Applying machine learning to image optimization makes perfect sense because computers have learned to recognize, optimize, compress, and change image formats, thanks to advancements in both machine learning and deep learning. Before knowing more about AI in Image Optimization, let us first know the traditional method of Image Optimization.

Image Optimization with traditional methods:

Must compress image and video files to load swiftly and efficiently. Network and storage limits can affect the overall delivery time and quality of the file during file transmissions and streaming. But, when a file is too large to load, it can take a long time. Furthermore, large files eat up a lot of storage and network resources. Compression techniques minimize the size of a file while preserving the image or video’s quality. The simplest basic compression method removes unnecessary data from a file, such as duplicated pixels and spatial redundancies. The file’s overall size is reduced when this information is eliminated. Machine-learning algorithms based on expert programming of specified rules have been the primary way of developing AI in the past. Several compression approaches were utilized to decrease statistical redundancies using entropy coding techniques. Later, a spatial frequency encoding method known as Discrete Cosine Transform (DCT) was created to convert images from 24-bit to 16-bit, resulting in a considerable reduction in file size. This method is still utilized in compression standards like JPEG today. DCT can detect minute features in an image that the human eye cannot see.

Image Optimization with AI:

While traditional image optimization techniques have been extremely successful, machine learning has much potential for improving compression and optimization processes. They’ve given rise to superior algorithms that learn to “recognize” possibly questionable facts through direct data navigation. These deep learning algorithms are useful because they function with little or no human intervention. Instead of collecting and processing data in its raw form using artificial neural networks. Some of the ways machine learning is improving image optimization are following:

Image Compression and Image Resolution:

RAISR is a Google algorithm that was developed and released. RAISR combines classical upsampling and deep learning (DL) to convert low-resolution images into high-resolution files.

Image Manipulation and Generation:

Using generative adversarial networks (GAN) to produce new features in existing images is possible. Two neural networks are a challenge against each other in GAN. One seeks to make fake versions of an image, while the other tries to distinguish between fake and real images. The network eventually improves its ability to create convincing fake images.

GAN use to change images in unexpected ways, such as eliminating rain, turning a daytime view to a nighttime setting, and adding or deleting people or other things from the image. This can provide interesting effects, such as converting night into day or removing rain from images. The capacity of networks to learn from one another and generate new data is extremely useful.

Enhancement of Images:

The Deep Bilateral Learning framework, another Google innovation, employs neural networks to enhance images in full HD in real-time. Machine learning may also learn subjective effects from human retouching, allowing it to execute human-like photo retouching in real-time on your phone. The most remarkable aspect of this technology is that it can perform real-time inference on a mobile device. One possible application is allowing phone users to preview an enhanced image with various changes or filters applied, even before taking a shot on their phone. Rather than relying on Instagram filters, this model displays the final result of your images as you frame up your shot, complete with professional-level enhancements.

Wrap up:

Keeping up with the competitive Internet climate necessitates optimizing our website images. Machine learning algorithms and techniques are included in the top image optimization. Digital Asset Management software assists automate otherwise repetitive processes. Beyond the adequate performance of classical algorithms, AI has huge potential to optimize photos and video. This will enable better online experiences as the demand for rich media streaming on mobile devices grows.

AI will support the next move to new sorts of content, such as augmented reality and 360-degree video, in the same way, that the Internet transitioned from text-based content to image-rich web pages to dynamic and video content. Incredibly, new and inventive technologies are improving and transforming how we make and edit images. Deep learning and machine learning techniques will only enhance the future of image optimization.