Computer implemented assessment method for creating a training data set, wherein the training data set is in particular designed to serve as an input and ground truth for a computer implemented artificial intelligence method for evaluation of image visibility degradation, and wherein the assessment method comprises following steps: - Gathering a main set of image data units (P1, P2, P3, P4) concerning the environment of a vehicle (1, 1'), wherein at least a share of the image data units (PI, P2, P3, P4) contains image information which is subject to a visibility degradation based on at least one vision degrading influence (5, 6, 7, 8); - Defining multiple subsets (S1, S2, S3, S4) of image data units (P1, P2, P3, P4), and gathering comparison results (C1, C2, C3) for a relative comparison of the image data units (P1,P2; P3,P4; P3,P2) in a respective subset (S1, S2, S3), wherein the comparison result (C1, C2, C3) is an input parameter provided by a human labeler, and wherein the comparison result (C1, C2, C3) is associated to the respective subset (S1, S2, S3) and indicates a relative order of the visibility or visibility degradation among the image data units (P1,P2; P3,P4; P3,P2) in the subset (S1, S2, S3); - Processing multiple subsets (S1, S2, S3) and the respective comparison results (C1, C2, C3), wherein an absolute visibility rank order is established for the image data units (P1, P2, P3, P4) in the processed subsets (S1, S2, S3), wherein the absolute visibility rank order provides quantitative evaluation on the visibility degradation on a single scale and independently from a specific vision degrading influence; - Associating a rank parameter (R1, R2, R3, R4) to an image data unit (P1, P2, P3, P4), wherein the rank parameter (R1, R2, R3, R4) is calculated based on multiple comparison results (C1, C2, C3) of the processed subsets (S1, S2, S3), and wherein the rank parameter (R1, R2, R3, R4) defines a rank within the absolute visibility rank order; - Outputting a training data set comprising the image data units (P1, P2, P3, P4) of the processed subsets (S1, S2, S3) and the associated rank parameters (R1, R2, R3, R4). - Gathering a main set of image data units (P1, P2, P3, P4) concerning the environment of a vehicle (1, 1'), wherein at least a share of the image data units (PI, P2, P3, P4) contains image information which is subject to a visibility degradation based on at least one vision degrading influence (5, 6, 7, 8); - Defining multiple subsets (S1, S2, S3, S4) of image data units (P1, P2, P3, P4), and gathering comparison results (C1, C2, C3) for a relative comparison of the image data units (P1,P2; P3,P4; P3,P2) in a respective subset (S1, S2, S3), wherein the comparison result (C1, C2, C3) is an input parameter provided by a human labeler, and wherein the comparison result (C1, C2, C3) is associated to the respective subset (S1, S2, S3) and indicates a relative order of the visibility or visibility degradation among the image data units (P1,P2; P3,P4; P3,P2) in the subset (S1, S2, S3); - Processing multiple subsets (S1, S2, S3) and the respective comparison results (C1, C2, C3), wherein an absolute visibility rank order is established for the image data units (P1, P2, P3, P4) in the processed subsets (S1, S2, S3), wherein the absolute visibility rank order provides quantitative evaluation on the visibility degradation on a single scale and independently from a specific vision degrading influence; - Associating a rank parameter (R1, R2, R3, R4) to an image data unit (P1, P2, P3, P4), wherein the rank parameter (R1, R2, R3, R4) is calculated based on multiple comparison results (C1, C2, C3) of the processed subsets (S1, S2, S3), and wherein the rank parameter (R1, R2, R3, R4) defines a rank within the absolute visibility rank order; - Outputting a training data set comprising the image data units (P1, P2, P3, P4) of the processed subsets (S1, S2, S3) and the associated rank parameters (R1, R2, R3, R4).