What Is Image Optimization and Why Is It Important?

Image optimization is the process of providing high-quality images in the correct format, size, size, and resolution while bringing file size to a minimum. There are two primary benefits of optimizing images on a website: improving page load speed, which impacts user experience, and improved search engine rankings.

Improves Page Load Speed

Images are often the heaviest part of a web page and take the longest to load. If you don’t optimize images, your web pages can slow down significantly.

Page speed can dramatically affect user experience. For example, research shows that more than half of mobile users leave the site for at least 3 seconds after the site loads. This is especially important for e-commerce websites, because a poor user experience can mean loss of revenue. 

Boosts Search Engine Rankings

Optimized images are an essential part of SEO on your page and help complement other SEO efforts. Page load speed, especially on mobile, is a ranking factor in the Google algorithm. Google’s PageSpeed Insights tool specifically alerts about issues that are not optimized and provides suggestions for fixing them. The first step to creating pages that perform well and can do well in Google search is to optimize images. 

Traditional Image Optimization Methods

Caching

Caching refers to the process of storing image files in the browser’s cache or a proxy server, so users can access them more quickly. The browser’s cache reduces the number of application requests and file downloads during a session.

On the other hand, if you want to use proxy caching, you need to store the image on a Point of Presence (PoP) server. An example of proxy caching is a content delivery network (CDN). Serving the image from the server physically nearest to your users will reduce page load time. 

For application services, you can use a cached image service. This method creates cached tiles to improve image performance and images available when needed. Cached image tiles serve two purposes: they allow images to load faster, by preprocessing them before caching, and provide easy access to image metadata, which enables improved querying and retrieval of image content. 

Cached image services can improve performance of static images, like banners and backgrounds, and image formats that are slow to process, such as heavily compressed images. A cached image service can pre-process such files, allowing users to access them more quickly.

Compression

This method reduces image size by reducing the number of bits required to store the information. There are two types of compression: lossy and lossless.

• Lossy compression – makes files smaller by removing redundant data. This involves merging similar, adjacent pixels. The downside is that if you compress the image using lossy methods, the lost bits cannot be recovered, and the image cannot be restored to high quality.
• Lossless compression – compresses files without loss of quality. This way you can restore the original file quality as needed. However, typically this provides lower compression ratios.

Resizing

An often overlooked aspect of image optimization is resizing. Image size and resolution must be adapted to the actual image size on the page and the user device. The higher the resolution, the larger the file size. 

For websites, high-resolution images are not recommended. Images must be reduced to the minimal resolution that will allow them to display with good quality, at the required physical image size. For example, if an image is displayed at a size of 100×400 pixels, the image file must be optimized to provide good quality at that size. This should be managed separately for different devices and screen formats.

It is never a good idea to resize images on the client side (for example, with HTML or JavaScript commands). If an image needs to be resized, this must be done in advance on the server side, so the user does not needlessly download a larger image, only to have it resized on the client.

Image Optimization with Machine Learning

Machine learning and deep learning computer vision algorithms are increasingly being used to analyze image content, identify content in images, and also to optimize and compress images. Machine learning-based approaches can potentially achieve better performance than traditional techniques, and can enable new use cases.

Image Enhancement in Live Video Streams

Google created a neural network system that can take frames from high definition video, running in real time on mobile devices, and apply image enhancements to them on the fly. The major innovation here is the speed at which these enhancements can be carried out – typically neural network processing takes a long time, especially with limited hardware available to mobile devices. This new approach makes it possible to apply retouching and filters to live video footage.

Image Compression and Resolution

A new algorithm by Google known as Rapid and Accurate Image Super Resolution (RAISR), uses deep learning together with traditional sampling techniques, to take low-resolution images and create high resolution versions. This is extremely innovative because, previously, it was not possible to upgrade the resolution of an existing low-resolution image. RAISR is trained using edge features, found in small parts of the image.

Image Manipulation and Generation

A new technology known as Generative Adversarial Networks (GAN) is used to add new features to existing images, or create new, photorealistic images. It is based on two neural networks pitted against each other, where one network tries to generate fake images, and the other tries to tell the difference between fake and real images. Eventually the system gets very good at generating “fakes” that cannot be told apart from real images.

GAN can be used to modify images in exciting ways, for example to add sunlight to a rainy scene, remove people from the image, clear the background from irrelevant objects, and so on. Like the RAISR algorithm, it can also be used to increase image resolution. These networks have many uses, from video production to medical image processing.

Conclusion

In this article I covered several ways to optimize images for the web, from traditional techniques like caching, compression and resizing, to cutting edge techniques based on machine learning algorithms, such as neural network image enhancement and generative adversarial networks (GAN). I hope this will give you a few useful options for reducing image size, providing a better experience to users, and making your website more successful.