Last year, I wrote about the challenges of picking responsive images breakpoints and how I found it a nearly unsolvable problem. It has vexed me since.
But I have a new idea on how we might be able to define responsive image breakpoints that is based on a performance budget.
Before I begin, I should note that a lot of this is a thought experiment. I don’t yet know how practical this approach would be.
Without going into all of the details about responsive image breakpoints, the short version is that most people are picking the breakpoints for responsive images based on one of two criteria:
- Based on what Scott Jehl referred to as “sensible jumps in file size to match screen dimension and/or density” OR
- Simply matching the image breakpoints to the major breakpoints being used for the design.
While the first method is more efficient and will probably result in better image sizes, my suspicion is that defining “sensible jumps in file size” is so nebulous that most web developers are going to choose to do the second, easier option.
That is unless we can find a formula to calculate what constitutes a sensible jump in file size and that’s what got me thinking about performance budgets.
I’m not sure how long the idea of a performance budget has been around, but I first became cognizant of the idea when Steve Souders talked about creating a culture of performance on the Breaking Development podcast.
Tim Kadlec expanded on the idea in a recent blog post. He cites the BBC which determined that “each page to be usable within 10 seconds on a GPRS connection and then based their goals for page weight and request count on that.”
So that’s the basic idea. Establish a performance budget and stick to it. If you add a new feature to the page and you go over budget, then you have a three options according to Steve (and transcribed by Tim):
- Optimize an existing feature or asset on the page.
- Remove an existing feature or asset from the page.
- Don’t add the new feature or asset.
Let’s apply this idea of a performance budget to responsive design. In particular, let’s treat the idea of flexible images as a feature. Because flexible images are a feature, we need a budget for that feature.
And as long as we’re making up the rules, let’s establish a few more hypotheticals:
- The page we’re working with has 10 images on it of varying formats and visual content.
- We haven’t reached our performance budget yet so we don’t have to remove other features, but we still need to make sure that flexible images do not add too much to the page weight.
- We’ve concluded that flexible images can add up to 200k to the page above what the size of the page would be if we provided fixed width images. We picked 200k because it is ~1 second at HSDPA (recent mobile) speeds. And well, 200k is a nice even number for this thought experiment.
- Because this page has 10 images on it, each image has a 20k budget for flexible images.
One thing to keep in mind, 200k isn’t the cap for the file size of all ten images combined. Instead, it is the price we’re willing to pay for using flexible images instead of images that are perfectly sized for the size they are used in the page.
For example, say you had a responsive web page with the following image on it:
That image is 500×333 pixels and 58K in file size.
Now imagine a visitor views that web page and based on the size of their viewport, the image is displayed at 300×200 pixels, but the source image is still the same. The cost of using flexible images is the difference in file size between what the image would be saved and optimized at 300×200 versus the file size of the image downloaded at 500×333.
In this case, I’ve taken that example image and resized it to 300×200 and saved it with the same compression level as the 500×333 image to see what the file size cost is of using that flexible image.
|Image matching size used in page||300||200||24K|
|Total extra download||–||–||34K|
In this example, the visitor is downloading an extra 34k of image data because they are downloading a flexible image instead of downloading one that had been resized to the exact size being used in the page.
Let’s go back to the page we want to optimize—the one with ten images on it and a total performance budget of 200k for flexible images. How do we translate that into image breakpoints?
Thinking back to the example above, the price for using flexible images is the difference between the size of the file that is downloaded and the size the file would have been if perfectly sized for its use in the page.
Our budget says that we can only download up to an extra 20K per image. Therefore, we need to make sure that we have a new image breakpoint every time the size of the image increases 20K.
We now have a methodology for picking sensible jumps in image file size that is tied to user experience instead of picking them arbitrarily.
How would this translate into a heuristic that could be used to find the breakpoints? You would need the following:
- What is the minimum size this image will be used at? (In our example, let’s say 320×213)
- What is the file size between breakpoints? In other words, what is your per image budget? (20k for our example)
- A high quality source file to use for resizing.
- Optionally, the largest size the image will be used at. (990×660 for this example).
Once you have this information, the basic logic looks like this:
- Take the source image and resize it to the smallest size the image will be used at.
- Keep the file size of that image handy.
- Start a series of tests that create new image files from the source that are gradually getting bigger.
- Check each image created. If the difference between the file size of the new image and the image file size you stored is less than your budget, discard the new image.
- When you find an image that hits your budget, save that image and replace the previous file size that you stored with the new file size.
- Repeat steps 2 through 5 until you reach either the maximum resolution of the source image or the largest size the image will be used at.
I’m tickled to say that my co-founder John Keith got excited by this idea and built a rough prototype of how this might work.
Using the script that John built, I created a demo page containing ten images. The source images were 990 by 660 pixels and all but one of them were saved as JPEGs at 50% quality. The one exception is a PNG8 image with an optimized color palette.
I tried to pick a variety of images so we can see how each image might have different breakpoints using our budget. Let’s take a look at three sample images.
This image has a lot of visual diversity. The variations in colors and textures means that JPEG’s lossy compression cannot do as much without damaging the image quality.
Because of this, there are eight image breakpoints—set at 20k intervals—between the smallest size of the image (320×213) and the largest size of the image (990×660).
|Breakpoint #||Width||Height||File Size|
Unlike the Times Square image, this image has a lot of areas with very similar colors and little variation. Because of this, JPEG can compress the image better.
On an image that can be compressed better, our 20K budget goes farther. For this image, we only need three image breakpoints to cover the full range of sizes that the image will be used at.
|Breakpoint #||Width||Height||File Size|
This is a simple PNG8 file. At its largest size (990×660), it is only 13K. Because of this, it fits into our 20K budget without any modifications.
On a recent consulting project with a company that has over 800,000 images on its site, we identified a class of images—some icons, little badges, etc.—where the size of the image on desktop retina was not much different than the size used on mobile either because the image resolution doesn’t vary much or because the image compresses well. For those images, we decided to deliver the same size image to all screen sizes.
|Breakpoint #||Width||Height||File Size|
Take a look at the other images on the sample page we created. See how the number of breakpoints vary even through all the images start with the same resolution end points.
This diversity exists despite the fact that with the exception of the Microsoft logo, all of the images start at the same size with the same compression settings. On a real site, we would see even more diversity with varying levels of JPEG quality, PNG8 with gradients going horizontally instead of vertically, and PNG32 images in the mix.
But what intrigues me about this approach to setting breakpoints is that it we wouldn’t be setting one-size-fits-all image breakpoints. Instead, we would make decisions about where the breakpoints should exist based on the our goals for user experience—the performance budget—and the unique characteristics of the image and how will it can be compressed.
The point of this thought experiment wasn’t to provide a complete methodology to set responsive image breakpoints. I started by simply asking the question about whether we might be able to use performance budgets to come up with a way to calculate what are sensible jumps in image sizes.
But the outcome of this exercise has caused me to draw some interesting conclusions as well as sparking more questions about responsive images:
- Images do contain clues that can tell us where the breakpoints should be.
Last year I wrote that “the problem is there is nothing intrinsic to the image that would guide you in deciding where you should switch from one size of the image to another.” But this experiment shows that images do have intrinsic information—how well the image compresses, what type of compression is being used, the range in size between the smallest and largest use of an image—that can be used to decide when you should switch from one source file to another.
- We can set a performance budget for flexible images.
There’s no reason why we can’t treat the use of flexible images like any other feature that we add to a page and define a performance budget for its use. In fact, setting a performance budget for flexible images could be the key to making informed decisions about where image breakpoints should be set.
- Automated image resizing and compression is a must.
I have already written about how automated image services will likely be a must for sites in the future. If a company wanted to use a technique like this to set their breakpoints, they will need an automated way to do it.
- Can we set a performance budget for flexible images across an entire site?
For our sample page, setting the performance budget to 200K for the whole page worked well. But in the real world, we often don’t know how many images are going to be on a given page. Similarly, we may not know what pages a given image is going to be added to. It seems like it would be useful to be able to say that for any given flexible image on the site, we’ve established a 20K budget. It would be less precise than a per page limit, but it may be the only practical way to translate this thought experiment into a production environment.
- An image and its breakpoints could be stored as a bundle.
The outcome of this approach to image breakpoints is that the breakpoints could be specific to the image no matter what context the image is used in. You could store the calculated breakpoints with the image and whenever the image is displayed on a page, no matter what size the image is used at within the page, the same breakpoints could be used.
- An image and breakpoint bundle would be difficult to use with the proposed picture and srcset standards
Image breakpoints calculated this way depend on knowing the size of the element in the page. Both picture and srcset make the switching of image sources contingent on the size of the viewport instead of the element. This means that you’d have to find a way to translate your image breakpoints to viewport sizes which would undermine a lot of the utility of storing breakpoints with the image.
Phew, you made it to the end. So what do you think? Is there merit in using performance budgets for flexible images to determine responsive image breakpoints?