1. A method for evaluating the quality of a sequence of test images in relation to a sequence of reference images, wherein the sequence of test images comprises a sequence of a number of neighbor images of a certain test image, wherein the sequence of reference images comprises a sequence of the same number of neighbor images of a certain reference image, the method comprising the steps of: for each location of the certain test image and for each neighbor image of the certain test image, estimating a disparity between the certain test image and the neighbor images of the certain test image to generate a test image disparity vector, and for each location of the certain reference image and for each neighbor image of the certain reference image, estimating a disparity between the certain reference image and the neighbor images of the certain reference image to generate a reference image disparity vector, wherein the location comprises at least one of a pixel, a block, or a region of the corresponding image; for each location of the certain test image and for each neighbor image of the certain test image, computing a consistency function that depends on the test image disparity vector and the reference image disparity vector; and computing a metric based on a weighted combination of the consistency function over the locations of the certain test image and over the neighbor images of the certain test image to evaluate the quality of the sequence of test images at the certain test image, wherein the metric is computed: by weighting the consistency function at each location of each test image with a local weighting parameter, configurable for each location of each test image, wherein the local weighting parameter for a location of a test image represents the probability that the disparity vectors in the reference and test sequences will be consistent given the consistency of neighboring disparity vectors in the reference sequence, and by weighting the consistency function of each test image with a global weighting parameter, configurable image by image, wherein the global weighting parameter for each test image depends on the temporal distance between the test image and its neighbor image, as well as on whether the content of the test image is detected to contain an event which will affect the accuracy of the disparity estimation.