Hybrid DCT-Wiener-Based interpolation using dual MMSE estimator scheme

Hybrid DCT-Wiener-Based interpolation scheme using the learnt Wiener filter can significantly improve both objective and subjective performance by learning a suitable Wiener filter to fit the hybrid scheme with a good mix of spatial and transform domain process. Using the adaptive k-NN MMSE estimation for each block achieves extraordinary up-sampling results. However, it needs a large database and relatively long processing time. In this paper, we investigate using multiple learnt Wiener filters and combine the information from both the external training images and the original low-resolution image. The proposed dual MMSE estimators adaptively resolve the problem of one general learnt Wiener filter and use less computation time compared with that of the k-NN MMSE estimation. Experimental results show that the proposed dual MMSE estimators achieve around 1dB PSNR improvement compared to the original hybrid DCT-Wiener-Based scheme and provide more natural visual quality.