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Optimizing images on the web


Images are a massive part of the Internet. On the median web page, images account for 51% of the bytes loaded, so any improvement made to their speed or their size has a significant impact on performance.

Today, we are excited to announce Cloudflare’s Image Optimization Testing Tool. Simply enter your website’s URL, and we’ll run a series of automated tests to determine if there are any possible improvements you could make in delivering optimal images to visitors.

Example screenshot of Cloudflare's Image Optimization Test Results

How users experience speed

Everyone who has ever browsed the web has experienced a website that was slow to load. Often, this is a result of poorly optimized images on that webpage that are either too large for purpose or that were embedded on the page with insufficient information.

Images on a page might take painfully long to load as pixels agonizingly fill in from top-to-bottom; or worse still, they might cause massive shifts of the page layout as the browser learns about their dimensions. These problems are a serious annoyance to users and as of August 2021, search engines punish pages accordingly.

Understandably, slow page loads have an adverse effect on a page's “bounce rate” which is the percentage of visitors which quickly move off of the page. On e-commerce sites in particular, the bounce rate typically has a direct monetary impact and pages are usually very image-heavy. It is critically important to optimize all the images on your webpages to reduce load on and egress from your origin, to improve your performance in search engine rankings and, ultimately, to provide a great experience for your users.

Measuring speed

Since the end of August 2021, Google has used the Core Web Vitals to quantify page performance when considering search results rankings. These metrics are three numbers: Largest Contentful Paint (LCP), First Input Delay (FID), and Cumulative Layout Shift (CLS). They approximate the experience of loading, interactivity and visual stability respectively.

CLS and LCP are the two metrics we can improve by optimizing images. When CLS is high, this indicates that large amounts of the page layout is shifting as it loads. LCP measures the time it takes for the single largest image or text block in the viewport to render.

These can both be measured “in the field” with Real User Monitoring (RUM) analytics such as Cloudflare's Web Analytics, or in a “lab environment” using Cloudflare’s Image Optimization Testing Tool.

How to optimize for speed


One of the most impactful performance improvements a website author can make is ensuring they deliver images with appropriate dimensions. Images taken on a modern camera can be truly massive, and some recent flagship phones have gigantic sensors. The Samsung Galaxy S21 Ultra, for example, has a 108 MP sensor which captures a 12,000 by 9,000 pixel image. That same phone has a screen width of only 1440 pixels. It is physically impossible to show every pixel of the photo on that device: for a landscape photo, only 12% of pixel columns can be displayed.

Embedding this image on a webpage presents the same problem, but this time, that image and all of its unused pixels are sent over the Internet. Ultimately, this creates unnecessary load on the server, higher egress costs, and longer loading times for visitors.. This is exacerbated even further for visitors on mobile since they are often using a slower connection and have limits on their data usage. On a fast 3G connection, that 108 MP photo might consume 26 MB of both the visitor’s data plan and the website’s egress bandwidth, and take more than two minutes to load!

It might be tempting to always deliver images with the highest possible resolution to avoid “blocky” or pixelated images, but when resizing is done correctly, this is not a problem. “Blocky” artifacts typically occur when an image is processed multiple times (for example, an image is repeatedly uploaded and downloaded by users on a platform which compresses that image). Pixelated images occur when an image has been shrunk to a size smaller than the screen it is rendered on.

So, how can website authors avoid these pitfalls and ensure a correctly sized image is delivered to visitors’ devices? There are two main approaches:

  • Media conditions with srcset and sizes

When embedding an image on a webpage, traditionally the author would simply pass a src attribute on an img tag:

<img src="hello_world_12000.jpg" alt="Hello, world!" />

Since 2017, all modern browsers have supported the more dynamic srcset attribute. This allows authors to set multiple image sources, depending on the matching media condition of the visitor’s browser:

<img srcset="hello_world_1500.jpg 1500w,
             hello_world_2000.jpg 2000w,
             hello_world_12000.jpg 12000w"
     sizes="(max-width: 1500px) 1500px,
            (max-width: 2000px) 2000px,
     alt="Hello, world!" />

Here, with the srcset attribute, we're informing the browser that there are three variants of the image, each with a different intrinsic width: 1,500 pixels, 2,000 pixels and the original 12,000 pixels. The browser then evaluates the media conditions in the sizes attribute ( (max-width: 1500px) and (max-width: 2000px)) in order to select the appropriate image variant from the srcset attribute. If the browser's viewport width is less than 1500px, the hello_world_1500.jpg image variant will be loaded; if the browser's viewport width is between 1500px and 2000px, the hello_world_2000.jpg image variant will be loaded; and finally, if the browser's viewport width is larger than 2000px, the browser will fallback to loading the hello_world_12000.jpg image variant.

Similar behavior is also possible with a picture element, using the source child element which supports a variety of other selectors.

  • Client Hints

Client Hints are a standard that some browsers are choosing to implement, and some not. They are a set of HTTP request headers which tell the server about the client's device. For example, the browser can attach a Viewport-Width header when requesting an image which informs the server of the width of that particular browser's viewport (note this header is currently in the process of being renamed to Sec-CH-Viewport-Width).

This simplifies the markup in the previous example greatly — in fact, no changes are required from the original simple HTML:

<img src="hello_world_12000.jpg" alt="Hello, world!" />

If Client Hints are supported, when the browser makes a request for hello_world_12000.jpg, it might attach the following header:

Viewport-Width: 1440

The server could then automatically serve a smaller image variant (e.g. hello_world_1500.jpg), despite the request originally asking for hello_world_12000.jpg image.

By enabling browsers to request an image with appropriate dimensions, we save bandwidth and time for both your server and for your visitors.


JPEG, PNG, GIF, WebP, and now, AVIF. AVIF is the latest image format with widespread industry support, and it often outperforms its preceding formats. AVIF supports transparency with an alpha channel, it supports animations, and it is typically 50% smaller than comparable JPEGs (vs. WebP's reduction of only 30%).

We added the AVIF format to Cloudflare's Image Resizing product last year as soon as Google Chrome added support. Firefox 93 (scheduled for release on October 5, 2021) will be Firefox's first stable release, and with both Microsoft and Apple as members of AVIF's Alliance for Open Media, we hope to see support in Edge and Safari soon. Before these modern formats, we also saw innovative approaches to improving how an image loads on a webpage. BlurHash is a technique of embedding a very small representation of the image inside the HTML markup which can be immediately rendered and acts as a placeholder until the final image loads. This small representation (hash) produced a blurry mix of colors similar to that of the final image and so eased the loading experience for users.

Progressive JPEGs are similar in effect, but are a built-in feature of the image format itself. Instead of encoding the image bytes from top-to-bottom, bytes are ordered in increasing levels of image detail. This again produces a more subtle loading experience, where the user first sees a low quality image which progressively “enhances” as more bytes are loaded.

At the top, a normal JPEG loading from top-to-bottom at three points (13% complete, 30% complete and 50% complete).

At the bottom, a progressive JPEG loading with incrementally more detail at the same three points (13% complete, 30% complete and 50% complete).


The newer image formats (WebP and AVIF) support lossless compression, unlike their predecessor, JPEG. For some uses, lossless compression might be appropriate, but for the majority of websites, speed is prioritized and this minor loss in quality is worth the time and bytes saved.

Optimizing where to set the quality is a balancing act: too aggressive and artifacts become visible on your image; too little and the image is unnecessarily large. Butteraugli and SSIM are examples of algorithms which approximate our perception of image quality, but this is currently difficult to automate and is therefore best set manually. In general, however, we find that around 85% in most compression libraries is a sensible default.


All of the previous techniques reduce the number of bytes an image uses. This is great for improving the loading speed of those images and the Largest Contentful Paint (LCP) metric. However, to improve the Cumulative Layout Shift (CLS) metric, we must minimize changes to the page layout. This can be done by informing the browser of the image size ahead of time.

On a poorly optimized webpage, images will be embedded without their dimensions in the markup. The browser fetches those images, and only once it has received the header bytes of the image can it know about the dimensions of that image. The effect is that the browser first renders the page where the image takes up zero pixels, and then suddenly redraws with the dimensions of that image before actually loading the image pixels themselves. This is jarring to users and has a serious impact on usability.

It is important to include dimensions of the image inside HTML markup to allow the browser to allocate space for that image before it even begins loading. This prevents unnecessary redraws and reduces layout shift. It is even possible to set dimensions when dynamically loading responsive images: by informing the browser of the height and width of the original image, assuming the aspect ratio is constant, it will automatically calculate the correct height, even when using a width selector.

<img height="9000"
     srcset="hello_world_1500.jpg 1500w,
             hello_world_2000.jpg 2000w,
             hello_world_12000.jpg 12000w"
     sizes="(max-width: 1500px) 1500px,
            (max-width: 2000px) 2000px,
     alt="Hello, world!" />

Finally, lazy-loading is a technique which reduces the work that the browser has to perform right at the onset of page loading. By deferring image loads to only just before they're needed, the browser can prioritize more critical assets such as fonts, styles and JavaScript. By setting the loading property on an image to lazy, you instruct the browser to only load the image as it enters the viewport. For example, on an e-commerce site which renders a grid of products, this would mean that the page loads faster for visitors, and seamlessly fetches images below the fold, as a user scrolls down. This is supported by all major browsers except Safari which currently has this feature hidden behind an experimental flag.

<img loading="lazy" … />


Finally, you can improve image loading by hosting all of a page's images together on the same first-party domain. If each image was hosted on a different domain, the browser would have to perform a DNS lookup, create a TCP connection and perform the TLS handshake for every single image. When they are all co-located on a single domain (especially so if that is the same domain as the page itself), the browser can re-use the connection which improves the speed it can load those images.

Test your website

Today, we’re excited to announce the launch of Cloudflare’s Image Optimization Testing Tool. Simply enter your website URL, and we’ll run a series of automated tests to determine if there are any possible improvements you could make in delivering optimal images to visitors.

We use WebPageTest and Lighthouse to calculate the Core Web Vitals on two versions of your page: one as the original, and one with Cloudflare's best-effort automatic optimizations. These optimizations are performed using a Cloudflare Worker in combination with our Image Resizing product, and will transform an image's format, quality, and dimensions.

We report key summary metrics about your webpage's performance, including the aforementioned Cumulative Layout Shift (CLS) and Largest Contentful Page (LCP), as well as a detailed breakdown of each image on your page and the optimizations that can be made.

Cloudflare Images

Cloudflare Images can help you to solve a number of the problems outlined in this post. By storing your images with Cloudflare and configuring a set of variants, we can deliver optimized images from our edge to your website or app. We automatically set the optimal image format and allow you to customize the dimensions and fit for your use-cases.

We're excited to see what you build with Cloudflare Images, and you can expect additional features and integrations in the near future. Get started with Images today from $6/month.

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Greg Brimble |@GregBrimble
Cloudflare |Cloudflare

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