mlh-fellowship / imagequalityindexes.jl

Indexes for image quality assessment

ImageQualityIndexes

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

Supported indexes

Full reference indexes

• PSNR/assess_psnr -- Peak signal-to-noise ratio
• SSIM/assess_ssim -- Structural similarity

No-reference indexes

• HASLER_AND_SUSSTRUNK_M3/hasler_and_susstrunk_m3 -- Colorfulness

Basic usage

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)

Examples

using Images, TestImages
using ImageQualityIndexes

img = testimage("lena_gray_256") .|> float64
noisy_img = img .+ 0.1 .* randn(size(img))
assess_ssim(noisy_img, img) # 0.3577
assess_psnr(noisy_img, img) # 19.9941

kernel = ones(3, 3)./9 # mean filter
denoised_img = imfilter(noisy_img, kernel)
assess_ssim(denoised_img, img) # 0.6529
assess_psnr(denoised_img, img) # 26.0350

img = testimage("lena_color_256");
colorfulness(img) # 64.1495