Analysis of colour constancy algorithms using the knowledge of variation of correlated colour temperature of daylight with solar elevation

EURASIP Journal on Image and Video Processing

Table 3 Performance of the model-based and projection-based algorithms when applying the sensor responses generated by 80-nm FWHM evenly spread sensors

	Phase of day	Solar elevation	Projection-based algorithm			Model-based algorithm
	Phase of day	Solar elevation	Without solar elevation and phase of day	With solar elevation and phase of day	Improvement (%)	Without solar elevation and phase of data	With solar elevation and phase of day	Improvement (%)
Quarter 1	Morning	−10 to 20	13.9	12.4	−10.7	13.9	11.8	−15.1
	Midday	20 to 90	17.4	20.0	+14.9	17.1	20.1	+17.5
	Evening	−10 to 20	12.0	11.9	−0.8	12.2	11.9	−2.4
Quarter 2	Morning	−10 to 20	15.3	15.5	+1.3	15.5	15.8	+1.9
	Midday	20 to 90	16.2	20.1	+24.1	16.0	20.4	+27.5
	Evening	−10 to 20	14.2	14.0	−1.4	13.9	14.0	+0.7
Quarter 3	Morning	−10 to 20	13.8	14.1	+2.2	13.5	14.0	+3.7
	Midday	20 to 90	15.8	20.1	+27.2	15.7	20.4	+29.9
	Evening	−10 to 20	14.3	15.0	+4.9	14.0	15.1	+7.8
Quarter 4	Morning	−10 to 20	14.6	14.9	+2.1	14.4	14.7	+2.1
	Midday	20 to 90	15.0	19.4	+29.3	14.8	19.5	+31.8
	Evening	−10 to 20	13.4	15.1	+12.7	13.3	15.3	+15.0

In this test, Munsell samples separated by 3-CIELab units and measured daylight spectra were applied. The sensor responses were multiplied by normally distributed hundred random numbers with a mean value of 1 and standard deviation 3% and the resulting linear responses were quantised to 10 bits. The Mahalanobis distance boundary settings were applied in three phases of the day (morning, midday and evening) with elevation threshold of 20°.