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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.

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

Ex.

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

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

• Any suggestions?

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.

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

Ex.

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

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

• Any suggestions?

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.slow (e.g. RGB2Lab conversion is necessary, floating point operations an be costly, etc.).

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

Ex.

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

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

• Any suggestions?

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 an can be costly, etc.).

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

Ex.

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

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

• Any other suggestions?

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?

Ex.

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

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

• Any other suggestions?