What does PSNR measure in image comparison?

PSNR measures pixel-level error relative to the original image. Higher values usually mean less pixel error, but PSNR does not always match human perception.

Is the SSIM value on this page a full reference implementation?

No. This benchmark uses a transparent global luminance SSIM-style approximation for repeatable site data. It is useful for relative comparison, but full SSIM implementations may produce different values.

Original Data

Image Compression Quality Benchmark

This DualView benchmark compares local image variants against the same reference test pattern. It reports file size, mean squared error, PSNR, and a transparent global luminance SSIM-style approximation so the results are reproducible and useful for visual quality discussions.

Quick Answer

In this local benchmark, higher JPEG quality preserves more pixel fidelity than lower JPEG quality, while blur, noise, brightness, and contrast changes show why a single metric is not enough. PSNR is useful for pixel error, but visual review and SSIM-style structure checks are still needed for real image comparison decisions.

Benchmark Results

Variant	File size KB	PSNR dB	SSIM approx.	MSE
JPEG quality 95	182.1	48.59	1	0.9
JPEG quality 60	64.89	36.11	0.9993	15.92
JPEG quality 30	45.28	33.2	0.9987	31.09
Blurred PNG	398.25	28.94	0.9969	83.02
Noisy PNG	3875.42	26.52	0.9938	144.96
Sharpened PNG	274.91	33.8	0.9983	27.12
Brightened PNG	215.47	21.58	0.9862	452.21
High-contrast PNG	224.97	26.91	0.9992	132.39

Download JSON or download CSV. Every result includes a public source asset and SHA-256 checksum so the calculation can be reproduced against the exact committed input.

Methodology

The benchmark uses the public test pattern reference and compares its committed variants. Each file is decoded with Sharp, converted to RGB pixels, resized to the reference dimensions when needed, and compared channel-by-channel. PSNR is computed from mean squared error across RGB channels. The SSIM-style value is a global luminance approximation, not a windowed full SSIM implementation. Read the full comparison methodology for evidence, fairness, review, and correction rules.

Limitation: this page is a transparent benchmark for DualView's own test assets. It should not be treated as a universal ranking of formats, encoders, or image-processing methods.

How To Use These Numbers

Use PSNR to spot large pixel-error differences, use the SSIM-style value to understand broad structural similarity, and use visual inspection to catch artifacts that metrics can hide. For production decisions, compare your own source files in DualView quality metrics and inspect the visible image, not only the score.