Figure 6 clearly shows that the proposed reliability indices are correlated with the standard deviation of displacement vector, meaning that they are an effective tool for measuring the accuracy of motion vectors. The motion-compensation prediction error does not have such correlation with the standard deviation, making it unsuitable as an indicator of accuracy. We classified motion images by the distribution of gradient vectors using the reliability indices proposed. Figure 7 is a flowchart of the classification process. First, spherical reliability index S s and linear reliability index sl are calculated from the eigenvalue of the covariance matrix G of the gradient vectors inside a given image region.
Every pixel is classified using the image region surrounding the each pixel.
Through the above process, all the pixels inside the motion image are classified into four categories. These areas have the following characteristics. The gradient vectors are distributed spherically and the eigenvalues are small. It is difficult to estimate the movement, because there is no typical texture in this image region, as shown in Fig. The gradient vectors are distributed spherically and the eigenvalues are large.