Abstract
TheRGBsignals of different CISs (color image sensors) do not register the same values for the same viewing scene owing to their different spectral sensitivity and white balance mechanisms. Thus, CISs must be characterized based on CIE standard observation for colorimetric purposes. One general method for characterizing CISs is least square polynomial modeling to derive the colorimetric transfer matrix betweenRGBoutputs and CIEXYZtristimulus inputs. However, the transfer matrix that is obtained under the standard CIE illumination is unable to estimate various conditions of a CIS that is operated under various illuminations with varying chromaticity and luminance. Therefore, repeated experiments are necessary to obtain accurate colorimetric analysis results. This paper presents a scene-adaptive colorimetric analysis method using images captured by a general consumer camera under various environments.
Highlights
The RGB outputs generated by CISs are device-dependent
This means that the RGB signals do not correspond to device-independent tristimulus based on CIE CMFs [1]
This is because the spectral sensitivity of CISs is not matched to the standard CMFs, and the characteristics of color sensors that are utilized in consumer cameras usually depend on the surrounding environment when capturing a scene [2]
Summary
The RGB outputs generated by CISs (color image sensors) are device-dependent. This means that the RGB signals do not correspond to device-independent tristimulus based on CIE CMFs (color matching functions) [1] This is because the spectral sensitivity of CISs is not matched to the standard CMFs, and the characteristics of color sensors that are utilized in consumer cameras usually depend on the surrounding environment when capturing a scene [2]. It is hard to estimate the internal calibration state of the camera sensor and surrounding illumination For this reason, without the illumination estimation process, the color conversion function for various RGB levels is derived using only two images captured on the AWB (auto white balance) mode and the preset mode. This demonstrates that the proposed camera characterization method performs effectively relative to previous methods
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