The Trade‑off of Image Quality vs Performance — Real Visual Examples
Updated: 2025-10-18
Compressing images inevitably involves compromise. Lower quality settings shrink file sizes, leading to faster pages, but also introduce artifacts that can affect user perception. In this article, we examine how different quality levels affect file size and visual fidelity. We use metrics like the structural similarity index (SSIM) and provide guidance on finding the right balance.
Understanding SSIM and perceived quality
The Structural Similarity Index (SSIM) is a widely accepted metric for measuring perceived visual quality【171508693852780†L241-L290】. It compares luminance, contrast and structure between the original and compressed images. A score of 1.0 indicates perfect similarity. When tuning compression, we recommend targeting an SSIM of 0.95 or higher for critical visuals. The WebP study used SSIM as its benchmark and found that WebP delivers 25–34 % smaller files than JPEG at the same SSIM【171508693852780†L241-L290】.
Example: adjusting JPEG quality
Suppose we start with a 1 MB JPEG at quality 100. Reducing the quality to 80 might cut the file size by ~40 % with virtually no visible loss. Dropping to 60 could cut it by ~60 %, but subtle banding may appear in gradients. At quality 40, the size might be 80 % smaller, yet artifacts become obvious. The key is to inspect the results at actual size on a calibrated monitor and choose the lowest quality where details still look acceptable.
Visual examples
Below, we simulate three compression levels on a hypothetical photograph. The left column shows the compressed images; the right column lists their sizes relative to the original. (Note: the images are illustrative — you can perform similar tests with your own files.)
Quality 80
≈40 % smaller
Quality 60
≈60 % smaller
Quality 40
≈80 % smaller
Although this demonstration applies a blur filter to approximate quality loss, it illustrates how file size savings come at a cost to crispness. In practice, use actual encoders rather than CSS filters and rely on metrics like SSIM and human judgement to decide on acceptable quality thresholds.
Balancing performance and quality
- Use modern formats: AVIF and WebP offer improved compression at equivalent quality levels, meaning you can achieve smaller files without dropping quality【171508693852780†L241-L290】【221654481411971†screenshot】.
- Test multiple settings: Export a few quality levels and compare them side by side. Choose the lowest quality where differences are imperceptible.
- Consider context: Hero images may warrant higher quality, while thumbnails can be heavily compressed.
- Automate optimisation: Integrate scripts to generate multiple quality variants and choose the appropriate version for each context.
Conclusion
There’s no one‑size‑fits‑all answer when it comes to image quality versus performance. The goal is to minimise file size while delivering images that look great to your audience. By leveraging modern formats, understanding metrics like SSIM and evaluating multiple quality settings, you can find the sweet spot that maximises speed without sacrificing the user experience.