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Image Optimization Best Practices for Device Breakpoints - Part 1

Ari Weil

An effective breakpoint strategy helps deliver sharp, properly sized images, which are some of the most compelling pieces of content on a web page. Lack of such a strategy can lead to jagged images or ones that take too long to render due to excessive size, potentially reducing the overall effectiveness of web pages — and driving down the quality of the user experience.

Creation of derivative images at properly determined breakpoints is critical but often challenging for web developers and designers to achieve. Generating the right number of variants — and at the correct widths and spacings — for their users' critical devices can provide a great balance between byte savings and cache dilution.

While it's true that having too few breakpoints can improve offload, it can also deliver many unused bytes and place an unnecessarily high rendering burden on the device and browser. At the same time, when there are too many breakpoints, it may result in the opposite effect. Finding the right balance mandates some time and resource commitment, but the benefits are truly worth the exercise.

In this 2-part blog, we will explore just how significant image breakpoints are to businesses, and some important device-related factors to consider in image breakpoint decisions — from screen resolution market share and user base, to device characteristics to pixel density — to deliver the optimally-sized web image every time.

The Byte Loss Breakdown

Getting image breakpoints right is especially important at larger image widths. To illustrate, let's look at this example in which a web page is displaying two images that need to be resized by the browser 50 pixels in both height and width:


As you can see, delivering a 200 X 200 pixel (px) image for a 150 X 150 px use case (50 extra pixels in width and height) results in 70,000 wasted bytes required in memory for the browser to display the image than if the image were delivered at 150 X 150 px.

When looking at the larger image example, delivering a 600 X 600 px image instead of a 550 X 550 px use case (still 50 extra pixels in height and width) resulted in 230,000 wasted bytes, or 3.3-times more wasted bytes. This illustrates why it's recommended to have more breakpoints at larger image sizes (in this case, images larger than 700 px in width).

Another example to consider involves displaying images on a typical smartphone, in this case, the Samsung Galaxy S5. This model has a screen resolution of 1080 X 1920 px. Take, for example, a server sending an image of 1200 X 2000 px dimensions that must be resized to 1080 X 1920 px. This would result in more than 1,305,600 bytes — that's 1.3 MB — waste, not to mention a significant degradation in user experience.

Read Image Optimization Best Practices for Device Breakpoints - Part 2, covering 4 tips for getting image breakpoints right.

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Image Optimization Best Practices for Device Breakpoints - Part 1

Ari Weil

An effective breakpoint strategy helps deliver sharp, properly sized images, which are some of the most compelling pieces of content on a web page. Lack of such a strategy can lead to jagged images or ones that take too long to render due to excessive size, potentially reducing the overall effectiveness of web pages — and driving down the quality of the user experience.

Creation of derivative images at properly determined breakpoints is critical but often challenging for web developers and designers to achieve. Generating the right number of variants — and at the correct widths and spacings — for their users' critical devices can provide a great balance between byte savings and cache dilution.

While it's true that having too few breakpoints can improve offload, it can also deliver many unused bytes and place an unnecessarily high rendering burden on the device and browser. At the same time, when there are too many breakpoints, it may result in the opposite effect. Finding the right balance mandates some time and resource commitment, but the benefits are truly worth the exercise.

In this 2-part blog, we will explore just how significant image breakpoints are to businesses, and some important device-related factors to consider in image breakpoint decisions — from screen resolution market share and user base, to device characteristics to pixel density — to deliver the optimally-sized web image every time.

The Byte Loss Breakdown

Getting image breakpoints right is especially important at larger image widths. To illustrate, let's look at this example in which a web page is displaying two images that need to be resized by the browser 50 pixels in both height and width:


As you can see, delivering a 200 X 200 pixel (px) image for a 150 X 150 px use case (50 extra pixels in width and height) results in 70,000 wasted bytes required in memory for the browser to display the image than if the image were delivered at 150 X 150 px.

When looking at the larger image example, delivering a 600 X 600 px image instead of a 550 X 550 px use case (still 50 extra pixels in height and width) resulted in 230,000 wasted bytes, or 3.3-times more wasted bytes. This illustrates why it's recommended to have more breakpoints at larger image sizes (in this case, images larger than 700 px in width).

Another example to consider involves displaying images on a typical smartphone, in this case, the Samsung Galaxy S5. This model has a screen resolution of 1080 X 1920 px. Take, for example, a server sending an image of 1200 X 2000 px dimensions that must be resized to 1080 X 1920 px. This would result in more than 1,305,600 bytes — that's 1.3 MB — waste, not to mention a significant degradation in user experience.

Read Image Optimization Best Practices for Device Breakpoints - Part 2, covering 4 tips for getting image breakpoints right.

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77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...

We just surveyed 300 frontend and mobile engineers across 16 countries, and the finding that keeps sticking with me isn't the one about AI. It's this: 74% of engineering teams rate themselves in the "middle" of the observability maturity scale. Not reactive, not strategic. Stuck in the middle. They have dashboards, they have tracing, they have alerts. And yet when something goes wrong, they still can't tell you why ...

In MEAN TIME TO INSIGHT Episode 25, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses  AI's impact on the Wide Area Network (WAN) ... 

Application performance monitoring (APM) dashboards are only as useful as what they are configured to measure. The default setup covers obvious failure modes such as downtime, error spikes, and latency breaches, but it does not cover everything. Some failures produce no alerts or anomalies. The dashboard stays green while users experience a broken product. Here are six signs that is happening ...

The race to deploy AI is largely over. Most enterprises have entered it. The question now is not whether artificial intelligence is running inside the organization. The question is whether anyone is genuinely responsible for what it does. That is not a technical question. It is a leadership one. And most organizations are not yet structured to answer it honestly ...

A new analysis of 250 real-world queries across common retail tasks, such as product pricing, availability, ratings, shipping and specifications, reveals systemic inefficiency at the heart of web-based AI agents. On average, 97.9% of the data retrieved by agents from live web pages is irrelevant to the query being answered. Specifically, the average page ingested ran nearly 9,000 characters, while the average answer was just 32 characters, resulting in a noise-to-signal ratio of 278:1. Price queries were the most extreme outlier, with noise rates approaching 99.5%. That's not a rounding error. That's a structural problem ...

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...