同色异谱黑在光谱降维中的应用研究

目的研究同色异谱黑在光谱降维中的应用。方法基于同色异谱黑光谱特性提出MBPCA光谱降维法。该方法将颜色光谱分解为基本光谱和同色异谱黑光谱,基本光谱的基向量由原始光谱通过PCA法得到的前3个基向量构成,原始光谱与基本光谱之间的残余光谱作为同色异谱黑光谱,其基向量由残余光谱通过PCA法推导。结果当降维光源与实际光照光源一致时,只要基向量数目超过3个,其低维模型的色度精度皆为0,当用6个基向量重构光谱时,在4种标准光源下的平均色差接近于1。结论 MBPCA法与PCA法相比,其光谱重构精度与PCA法接近,色度精度比PCA法有明显提高,其六维模型能有效满足光谱颜色复制的需要。

Spectral Dimension Reduction Method Based on Metameric Black

Objective To study the application of metameric black in spectral dimension reduction. Methods MBPCA spectral dimension reduction method was put forward based on metamerism black spectral characteristics. This method decomposed the color spectrum into basic spectrum and metameric black spectrum. The basis vectors of basic spectrum consisted of the first three basis vectors of original spectrum by PCA. The residue spectrum between original spectrum and basic spectrum was metameric black spectrum. The basis vectors of metameric black were derived from the residue spectrum by PCA. Results When the dimension reduction illuminant was in accordance with lighting illuminant, once the number of basis vectors was above three, the color differences of MBPCA were zero. When the number of basis vectors was six, the average color differences were close to one. Conclusion Compared with PCA, the spectral accuracy of MBPCA was close to that of PCA, and the colorimetric accuracy of MBPCA was higher than that of PCA. The six-dimensional model built by MBPCA could effectively meet the needs of spectral color reproduction.