Digital camera having subject judgment function

A digital camera, comprising:

• a storage unit which stores one or more pieces of reference image data associating each reference image data with subject information which defines a subject in an image formed by the pieces of reference image data,
• an image pickup device which picks up a subject image formed by an objective optical system and thereby successively generates subject image data,
• an image processing unit which changes image quality of an image displayed on a display device according to the subject image data by executing computation to the subject image data using proper correction values,
• a distribution data generation unit which generates pieces of two-dimensional distribution data of product-moment correlation coefficients for each of the one or more pieces of reference image data stored in the storage unit by calculating the product-moment correlation coefficients between each piece of reference image data and parts of the subject image data,
• an identification unit which identifies a piece of two-dimensional distribution data having the highest maximum value among all the two-dimensional distribution data generated for all the pieces of reference image data,
• a readout unit which reads out the subject information associated with the reference image data corresponding to the identified two-dimensional distribution data from the storage unit, and
• a setting alteration unit which alters the correction values used by the image processing unit into correction values corresponding to the subject information read out by the readout unit,
• wherein:
• the distribution data generation unit includes:
• a color space conversion module which converts color space of the subject image data into YCrCb while converting color space of all the reference image data stored in the storage unit into YCrCb,
• a coefficient calculation module which calculates product-moment correlation coefficients between Cr components of each reference image data and parts of Cr components of the subject image data and thereby generates two-dimensional distribution data of the product-moment correlation coefficients regarding the Cr components for each reference image data, and which calculates product-moment correlation coefficients between Cb components of each reference image data and parts of Cb components of the subject image data and thereby generates two-dimensional distribution data of the product-moment correlation coefficients regarding the Cb components for each reference image data, and
• an average calculation module which executes an average calculation process, for calculating each geometric average of the product-moment correlation coefficients regarding the Cr and Cb components at the same two-dimensional coordinates, for each reference image data and thereby generates two-dimensional distribution data of the geometric averages for each reference image data, and
• wherein the identification unit identifies a piece of two-dimensional distribution data of geometric averages having the highest maximum value among all the two-dimensional distribution data of geometric averages generated for all the reference image data.

A method of identifying a type of a subject image, comprising:

• preparing a plurality of pieces of reference image data respectively representing a plurality of different types of images, a number of pixels of subject image data being different from a number of pixels of each of the pieces of reference image data;
• scanning, of a piece of reference image data and subject image data, the piece of reference image data and the subject image data having a lower number of pixels, within the other of the piece of reference image data and the subject image data to evaluate similarity therebetween at every predetermined scanning position to obtain two dimensional distribution data from product-moment correlation coefficients between each of the pieces of reference image data and the subject image data at every predetermined scanning position, the scanning being repeated for each of the plurality of pieces of reference image data to obtain the two dimensional distribution data for each of the plurality of pieces of the reference image data;
• identifying one of the plurality of pieces of the reference image data most similar to the subject image in accordance with the plurality of pieces of the two dimensional distribution data corresponding to the plurality of pieces of the reference image data;
• converting color space of the subject image data into YCrCb while converting color space of all the reference image data into YCrCb;
• calculating product-moment correlation coefficients between Cr components of each reference image data and parts of Cr components of the subject image data and thereby generating two-dimensional distribution data of the product-moment correlation coefficients regarding the Cr components for each reference image data, and calculating product-moment correlation coefficients between Cb components of each reference image data and parts of Cb components of the subject image data and thereby generating two-dimensional distribution data of the product-moment correlation coefficients regarding the Cb components for each reference image data, and
• executing an average calculation process, for calculating each geometric average of the product-moment correlation coefficients regarding the Cr and Cb components at the same two-dimensional coordinates, for each reference image data and thereby generating two-dimensional distribution data of the geometric averages for each reference image data,
• wherein the identifying comprises identifying a piece of two-dimensional distribution data of geometric averages having the highest maximum value among all the two-dimensional distribution data of geometric averages generated for all the reference image data.