ImageQualityIndexes

ImageQualityIndexes provides the basic image quality assessment methods. Check the reasoning behind the code design here if you're interested in.

• PSNR/assess_psnr -- Peak signal-to-noise ratio
• SSIM/assess_ssim -- Structural similarity
• MSSSIM/assess_msssim -- Multi-scale SSIM

• HASLER_AND_SUSSTRUNK_M3/hasler_and_susstrunk_m3 -- Colorfulness

The root type is ImageQualityIndex, each concrete index is supposed to be one of FullReferenceIQI, ReducedReferenceIQI and NoReferenceIQI.

There are three ways to assess the image quality:

• use the general protocol, e.g., assess(PSNR(), x, ref). This reads as "assess the image quality of x using method PSNR with information ref"
• each index instance is itself a function, e.g., PSNR()(x, ref)
• for well-known indexes, there are also convenient name for it for benchmark purpose.

For detailed usage of particular index, please check the docstring (e.g., ?PSNR)

using Images, TestImages
using ImageQualityIndexes

img = testimage("cameraman") .|> float64
noisy_img = img .+ 0.1 .* randn(size(img))
assess_ssim(noisy_img, img) # 0.24112
assess_psnr(noisy_img, img) # 19.9697

kernel = ones(3, 3)./9 # mean filter
denoised_img = imfilter(noisy_img, kernel)
assess_psnr(denoised_img, img) # 28.4249
assess_ssim(denoised_img, img) # 0.6390
assess_msssim(denoised_img, img) # 0.8460

img = testimage("fabio");
colorfulness(img) # 68.5530