add badge and example codes to README

README.md

@@ -1,19 +1,43 @@
 # ImageQualityIndexes
 
+[![Build Status](https://travis-ci.org/JuliaImages/ImageQualityIndexes.jl.svg?branch=master)](https://travis-ci.org/JuliaImages/ImageQualityIndexes.jl)
+[![Codecov](https://codecov.io/gh/JuliaImages/ImageQualityIndexes.jl/badge.svg?branch=master)](https://codecov.io/gh/JuliaImages/ImageQualityIndexes.jl)
+
 ImageQualityIndexes provides the basic image quality assessment methods.
 
 ## Supported indexes
 
-* `PSNR` -- Peak signal-to-noise ratio
-* `SSIM` -- Structural similarity
+### Full reference indexes
+
+* `PSNR`/`psnr` -- Peak signal-to-noise ratio
+* `SSIM`/`ssim` -- Structural similarity
 
 
 ## Basic usage
 
-In this package, each index corresponds to a `ImageQualityIndex` type, there are three ways to assess the image quality:
+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, 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
+
+```julia
+using Images, TestImages
+using ImageQualityIndexes
+
+img = testimage("lena_gray_256") .|> float64
+noisy_img = img .+ 0.1 .* randn(size(img))
+ssim(noisy_img, img) # 0.3577
+psnr(noisy_img, img) # 19.9941
+
+kernel = ones(3, 3)./9 # mean filter
+denoised_img = imfilter(noisy_img, kernel)
+ssim(denoised_img, img) # 0.6529
+psnr(denoised_img, img) # 26.0350
+```