Image PSNR Calculator
Upload an original and a compressed image to calculate the Peak Signal-to-Noise Ratio (PSNR) — the industry-standard metric for measuring image compression quality. Shows per-channel results, MSE values, and a quality scale reference. Runs entirely in your browser with no signup required.
Upload an original and a compressed image to calculate the Peak Signal-to-Noise Ratio (PSNR) — the industry-standard metric for measuring image compression quality. Runs entirely in your browser. Your images never leave your device.
Original Image
Click to upload original
JPEG, PNG, WebP, etc.
Compressed Image
Click to upload compressed
Must be same dimensions
Why Use Our Image PSNR Calculator?
Instant PSNR Calculation
Upload two images and calculate PSNR instantly in your browser — per-channel (R, G, B) results with MSE values and a quality scale reference.
Secure Image PSNR Calculator Online
All pixel comparison runs entirely in your browser using the Canvas API. Your images never leave your device — safe for proprietary or confidential image content.
Quality Scale Reference
See where your PSNR score falls on the industry-standard quality scale — from Excellent (≥50 dB) to Very Poor (<25 dB) — with your result highlighted.
100% Free Forever
Calculate PSNR for as many image pairs as you need, completely free. No account, no subscription, no file size limits, and no ads.
Common Use Cases for Image PSNR Calculator
Image Compression Quality Audit
Measure the quality loss introduced by JPEG, WebP, or AVIF compression — compare the PSNR score against the original to verify that compression settings meet your quality threshold.
Codec Comparison & Benchmarking
Compare PSNR scores across different codecs (JPEG vs WebP vs AVIF) at the same file size — use PSNR as an objective metric to identify which codec delivers the best quality.
Image Processing Pipeline Validation
Validate that image processing steps (resize, sharpen, denoise) do not introduce unexpected quality degradation — PSNR provides an objective before/after comparison.
Video Frame Quality Analysis
Extract frames from original and compressed video and calculate PSNR to measure video codec quality — a standard technique in video compression research and QA.
Compression Parameter Tuning
Find the optimal JPEG quality setting or WebP compression level by comparing PSNR scores — identify the quality/size tradeoff point that meets your requirements.
Lossless Compression Verification
Verify that a compression algorithm is truly lossless — a PSNR of infinity (MSE = 0) confirms that the compressed image is pixel-perfect identical to the original.
Understanding PSNR for Image Quality
What is Image PSNR?
Peak Signal-to-Noise Ratio (PSNR) is the industry-standard metric for measuring image compression quality. It compares an original image to a compressed version by calculating the mean squared error (MSE) between corresponding pixels, then expressing the result on a logarithmic decibel scale. Higher PSNR means less distortion — a PSNR of infinity means the images are pixel-perfect identical. Our free image PSNR calculator computes PSNR per channel (R, G, B) entirely in your browser.
How Our Image PSNR Calculator Works
- Upload Both Images: Upload the original image and the compressed version. Both must have identical dimensions.
- Browser-Based Pixel Comparison:Click "Calculate PSNR" — the tool draws both images to an offscreen Canvas, reads the pixel data, and computes MSE and PSNR per channel. Your images never leave your device.
- Review Results: See the overall PSNR, per-channel values, MSE, quality verdict, and where your score falls on the industry-standard quality scale.
PSNR Formula
- MSE: Mean of (original_pixel − compressed_pixel)² across all pixels and channels.
- PSNR: 20 × log₁₀(255) − 10 × log₁₀(MSE) = 48.13 − 10 × log₁₀(MSE)
- Infinite PSNR: When MSE = 0 (identical images), PSNR is mathematically infinite — the images are losslessly identical.
- Per-channel: Calculated separately for R, G, B channels — the overall PSNR uses the average MSE across all three channels.
PSNR vs SSIM
PSNR measures pixel-level differences but does not always correlate with human perception. SSIM (Structural Similarity Index) is often preferred for perceptual quality assessment because it considers luminance, contrast, and structure. However, PSNR remains the most widely used metric in compression research and standards because it is simple, fast, and reproducible. Use PSNR for objective codec comparison and SSIM for perceptual quality assessment.
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Frequently Asked Questions About Image PSNR Calculator
PSNR is a logarithmic metric that measures the ratio between the maximum possible signal power and the power of distortion noise introduced by compression. It is expressed in decibels (dB) — higher values indicate better quality. Our free online image PSNR calculator computes it from two images entirely in your browser.
PSNR ≥ 40 dB is considered very good quality — differences are barely visible. 35–40 dB is good quality with minor artifacts. 30–35 dB is acceptable. Below 30 dB indicates significant compression artifacts. For lossless compression, PSNR is infinite (MSE = 0).
Yes, complete privacy is guaranteed. All pixel comparison runs entirely client-side in your browser using the Canvas API. Your images never leave your device and are never uploaded to any server.
Yes. The image PSNR calculator is 100% free with no signup, no subscription, no file size limits, and no ads. You can calculate PSNR for as many image pairs as you need.
PSNR is calculated by comparing corresponding pixels at the same position in both images. If the dimensions differ, there is no meaningful pixel-to-pixel correspondence. If your compressed image was resized, you need to resize the original to match before calculating PSNR.
MSE (Mean Squared Error) is the average of the squared differences between corresponding pixels. PSNR is derived from MSE: PSNR = 20 × log10(255) − 10 × log10(MSE). Lower MSE means higher PSNR and better quality. MSE = 0 means the images are identical (PSNR = ∞).
PSNR measures pixel-level differences but does not always correlate with human perception. Two images with the same PSNR can look very different to the human eye. For perceptual quality assessment, SSIM (Structural Similarity Index) is often preferred. PSNR is most reliable for comparing the same codec at different quality settings.
Different color channels can have different compression artifacts — JPEG compression often introduces more artifacts in the blue channel. Per-channel PSNR helps identify which channel is most affected by compression. The overall PSNR is the average of the three channel MSE values.