The LIP (Logarithmic Image Processing) framework is classically devoted to grayscale images. The aim of the present study is to extend this framework to color images. This new model is noted LIPC for LIP Color. It does not consist in applying the LIP Model to each channel R, G, B of a color image. We define the transmittance of color images in order to give a physical justification, on which will be based the definition of logarithmic operators like addition, subtraction and scalar multiplication, respectively noted in the LIPC : c , c and c . As for the classical LIP Model, the laws c and c define a vector space structure on the space of images which enables us to present notions requiring such a structure. For example, we define a color logarithmic interpolation by associating to a pair (F, G) of images the interval [F, G], set of barycenters of F and G. A new notion of color contrast is defined, which satisfies sub-additivity and homogeneity for scalar multiplication. This notion is proved to be efficient for edge detection. We note that the vector space structure opens the way to a lot of developments concerning the definition of metrics, norms, scalar products...and to transfer to LIPC gauges theory, duality theory... In this initial paper, we preferred insist on applications of the LIPC. For example, color prediction is presented and discussed as well as stabilization of images by dynamic range centring and enhancement of under-lighted images. Concerning the implementation of the LIPC operators and algorithms, informations are given on their execution time.