@blazediff/gmsd

High-performance GMSD (Gradient Magnitude Similarity Deviation) perceptual image quality metric. Structure-aware similarity scoring using Prewitt gradients on luma channel.

Installation

npm install @blazediff/gmsd

API

gmsd(image1, image2, output, width, height, options)

Compare two images using GMSD perceptual similarity metric and return a similarity score.

Returns: GMSD score where 0 = identical, higher values = more differences (typically 0-0.35 range)

Usage

import { gmsd } from '@blazediff/gmsd';

// Basic comparison
const score = gmsd(
  image1.data,
  image2.data,
  undefined,
  width,
  height,
  {
    downsample: 0,
    c: 170,
  }
);

// Lower score = better quality (0 = perfect match)
console.log(`GMSD score: ${score.toFixed(4)}`);

// With GMS map output for visualization
const output = new Uint8ClampedArray(width * height * 4);
const score = gmsd(
  image1.data,
  image2.data,
  output, // Will be filled with grayscale similarity map
  width,
  height,
  {}
);

// output now contains:
// - White pixels (255): identical gradient structure
// - Black pixels (0): different gradient structure
// - Gray shades: partial similarity

References

Based on the paper:

Xue, W., Zhang, L., Mou, X., & Bovik, A. C. (2013). "Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index." IEEE Transactions on Image Processing, 22(2), 684-695.

• Original MATLAB implementation
• Mathematical formula verification