Image Color Summarizer

rgb, hsl & lch image color statistics — simple and easy
NEW — histogram and pixel data is no longer reported by default (details)

the image color summarizer

The colour summarizer will produce descriptive colour statistics for an image. Reported will be the average, standard deviation, median or mode, minimum and maximum of each component of RGB, HSV and LCH. Some of the questions the summarizer will answer are


LCH is the perceptually uniform equivalent of HSV, and defines colors using intuitive and perceptually-based luminance (perceived brightness), chroma (richness) and hue. If you are doing any kind of image analysis, it's likely that LCH will be much more useful to you than HSV or RGB.

To learn about LCH, see my presentation about color spaces and perceptual uniformity.


If you are a data geek, you'll be happy to know that XML or plain-text API output of the image statistics now includes RGB and HSV histograms, as well as individual pixel values. Munge away!

The purpose of this utility is to generate metadata that summarizes an image's colour characteristics for inclusion in an image database, such as Flickr. In particular this tool is being used to generate metadata for Flickr's Color Fields group.

get started already

If you are curious, see the summarizer run. You may want to see see canned examples. Of course, why not provide your own image for analysis? You should read the FAQ to see how things work.

You can use the summarizer as a simple web service. Learn how to get image statistics in XML or plain-text format, or see a live XML example of this analysis).

an example

(A) RGB histograms

(B) HLS histograms

(C) independent statistics for H/L/S

(D) representative HLS triplet

(E) independent statistics for R/G/B

(F) representative RGB value


Image statistics are computed using every pixel in the image. Therefore, analysis of images in which the background is dominant will be skewed by the background color. Specifically, statistics for product images (e.g. an items photographed on a white background) can be difficult to interpret. The answer to this is to mask areas of the image before carrying out statistics, but this is not implemented.