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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.

Hot Topics

The Latest

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...