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Perceptual Hash

Perceptual hashing is based on four steps:

  • (1) Convert the image to grayscale and resize it to n_size_side_preprocess=32
  • (2) Compute the DCT of the 32x32 image.
  • (3) Get the top n_hash=n_size_side*n_size_side bits of the previous 32x32 array (usually ignoring the first value).
  • (4) Create a hash comparing each value of the previous array with the median.

Example

Let us visualize the original image and hash and mathash of an image.

using TestImages
img = testimage("fabio_color_256.png");
img

A "perceptual mathash" (matrix hash) can be created using perceptual_mathash(image, n_size_side), as follows:

using TestImages, Images, ImageHashes
img = testimage("fabio_color_256.png");
mat_hash = perceptual_mathash(img, 8)
mat_hash
8×8 BitMatrix:
 1  1  0  1  1  0  0  0
 0  1  0  0  0  1  1  0
 0  0  1  1  0  1  1  1
 0  1  0  0  0  0  0  0
 1  0  0  0  0  0  0  0
 0  0  0  1  0  0  0  0
 0  0  0  0  1  0  0  0
 0  0  0  0  0  0  0  0

We can visualize the hash using Gray..

using TestImages, Images, ImageHashes
img = testimage("fabio_color_256.png");
mat_hash = perceptual_mathash(img, 8)
Gray.(mat_hash)

The bigger the matrix hash is, the higher quality it can achive.

using TestImages, Images, ImageHashes
img = testimage("fabio_color_256.png");
mat_hash = perceptual_mathash(img, 28)
Gray.(mat_hash)

A hash (vector hash) for an image can be created with perceptual_hash function.

using TestImages, ImageHashes
img = testimage("fabio_color_256.png");
img_hash = perceptual_hash(img, 8)
img_hash
0x88d020a482606020

Execution time and allocations

using TestImages, ImageHashes, BenchmarkTools
img = testimage("fabio_color_256.png");
benchmark = @benchmark perceptual_hash($img, 8)
benchmark