Hybrid DCT-Wiener-based interpolation via learnt Wiener filter

The hybrid DCT-Wiener-based (DCT-WB) interpolation scheme provides a powerful framework to interpolate an image by utilizing the information in both spatial and DCT domain. In this paper, we investigate the bottleneck of this hybrid scheme and propose a 2D non-separable block-based Wiener filter for the hybrid scheme. The Wiener filter is learnt using training image pairs through the minimum mean squares error estimation. The proposed Wiener filter resolves the quarter-pixel shift issue and provides much better performance over the original 1D 6-tap pixel-based Wiener filter. Experimental results show that incorporating the proposed Wiener filter into the hybrid scheme improves the PSNR (0.44 dB), SSIM and subjective quality for our extensive experimental work on testing images with various contents.