A comparative study of image correlation models for directional two-dimensional sources

The non-separable Karhunen-Loève transform (KLT) has been proven to be optimal for coding a directional 2-D source in which the dominant directional information is neither horizontal nor vertical. However, the KLT depends on the image data, and it is difficult to apply it in a practical image/video coding application. In order to solve this problem, it is necessary to build an image correlation model, and this model needs to adapt to the directional information so as to facilitate the design of 2-D non-separable transforms. In this paper, we compare two models that have been used commonly in practice: the absolute-distance model and the Euclidean-distance model. To this end, theoretical analysis and experimental study are carried out based on these two models, and the results show that the Euclidean-distance model consistently performs better than the absolute-distance model.