PSNR vs SSIM — Which Metric Should You Use?

7 min read  ·  Quality metrics  ·  June 2026

You've compressed a video, re-encoded an image, or run a batch conversion. Now you want to know: is the quality acceptable? Two metrics will show up everywhere you look — PSNR and SSIM. They're both standard. They're both fast to compute. And they frequently give contradictory answers. Here's how to make sense of them.

What PSNR measures

PSNR stands for Peak Signal-to-Noise Ratio. It's expressed in decibels (dB) and calculated from Mean Squared Error (MSE) — the average of the squared difference between each pair of corresponding pixels.

The formula is: PSNR = 10 × log₁₀(MAX² / MSE), where MAX is the maximum possible pixel value (255 for 8-bit images).

What this means in practice: every 6 dB of PSNR roughly doubles the signal-to-noise ratio. PSNR is logarithmic, so small differences at the top of the scale (40 vs 42 dB) are less meaningful than the same gap near the bottom (26 vs 28 dB).

What SSIM measures

SSIM (Structural Similarity Index Measure) takes a different approach. Rather than summing pixel errors, it models three aspects of how humans perceive image quality: luminance (local brightness), contrast (local variance), and structure (correlation between local pixel patterns).

SSIM is computed in a sliding 11×11-pixel window across both images and averaged. The result is a number from 0 to 1 (often expressed as 0–100%). A score of 1.0 means the two images are perceptually identical at the structural level.

Where they disagree — and why it matters

Case 1: A small region has a large error

Imagine one image has a single bright artefact in the top-left corner. PSNR will be significantly lower because MSE adds up the squared error — and squaring amplifies large differences. SSIM will be close to 1.0 because 99% of the image's local windows are unaffected. In this case, SSIM is probably giving you the more useful signal: the image looks fine except for one small area.

Case 2: A global blur

Aggressive noise reduction or over-compression produces a uniform, mild blurring. Every pixel changes a little. PSNR might still be acceptable (35 dB), because no individual pixel error is large. But SSIM will fall noticeably — local contrast and structure are reduced everywhere. Here, SSIM catches something PSNR misses.

Case 3: A colour cast

If one image is 10% brighter than the other (a global exposure shift), MSE will be large, so PSNR will be low. But the structure — the edges, textures, and shapes — is identical. SSIM will be high. Which metric is right? It depends on the application. For colour-graded photography, both matter. For medical imaging where diagnostic information lives in structure, SSIM is more appropriate.

What about VMAF?

For video specifically, Netflix's VMAF (Video Multi-Method Assessment Fusion) is increasingly the industry benchmark. VMAF trains a machine learning model on a dataset of human quality ratings, combining features from multiple metrics including SSIM, VIF (Visual Information Fidelity), and motion. For streaming and OTT, VMAF scores above 93 are generally considered "transparent" to viewers.

VMAF requires more compute than PSNR or SSIM and is harder to set up locally (it needs libvmaf or the ffmpeg build that includes it). DiffALL currently reports SSIM and PSNR for video; VMAF support is on the roadmap.

Practical recommendation

For most image and video comparison workflows: report both, lead with SSIM. SSIM correlates better with human perception in the cases that matter most (blurring, compression, noise), while PSNR is useful as a secondary check and for compatibility with existing specifications.

When the two metrics disagree significantly — high SSIM but low PSNR, or vice versa — examine the heatmap. The spatial distribution of errors will tell you whether you're looking at a localised problem (PSNR understates quality), a global blur (SSIM understates it), or a colour shift (look at both).