Best color difference or distance approximation?

asked 2015-07-09 20:45:57 -0500

updated 2015-07-09 20:57:47 -0500

Currently, a standard way of comparing colors is using "Delta E" metric in CIELab [Color-difference] which is based on Euclidean distance in CIELab color space.

However, for certain applications using the distance metric intensively "Delta E" metric could be a bit slow (e.g. RGB2Lab conversion is necessary, floating point operations can be costly, etc.).

Is there a "good enough approximation" of color difference or distance?


  • Weighted Manhattan distance (L1 distance) (in RGB) (as suggested here)

  • Hue Manhattan distance (L1 distance) (in HSV) (as suggested here)

  • Any other suggestions?

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How about cosine distance (i.e. the angle between two color vectors)?

Guanta gravatar imageGuanta ( 2015-07-10 09:17:37 -0500 )edit

@Guanta Thanks for the comment. Can you elaborate why you think cosine distance might be a "good enough approximation" of color difference or distance? It does have the speed potential as reported in EVALUATION OF SIMILARITY MEASUREMENT FOR IMAGE RETRIEVAL...

mkc gravatar imagemkc ( 2015-07-12 07:13:47 -0500 )edit