Joint shape and centroid adaptive frequency selective extrapolation for the reconstruction of arbitrarily shaped loss areas

Reconstructing missing areas of arbitrary shape and size is particularly important in error-prone communication as well as in applications where motion compensation is conducted such as multi-image super-resolution or framerate up-conversion. To that end, frequency selective extrapolation is an effective image reconstruction technique. This approach was originally designed for block losses and has recently been enhanced by a centroid adaptation to improve the reconstruction quality in the case of arbitrarily shaped loss areas. In this paper, we reuse the idea of centroid adaptation and introduce a novel shape adaptation so as to better assign higher weights to more relevant pixels. Moreover, we propose to combine both the shape adaptation and the centroid adaptation into a joint solution to further improve the reconstruction quality. To evaluate the proposed method, three different loss patterns are used. Simulation results yield an average gain in luminance PSNR of up to 0.2 dB for the high quality profile and 3.4 dB for the high efficiency profile, respectively. A visual comparison confirms these results.