A Fast Algorithm of Video Super-Resolution Using Dimensionality Reduction by DCT and Example Selection

In learning-based super-resolution algorithms, there are two major problems. One is that they require a large amount of memory to store examples; the other is the high computational cost of finding the nearest neighbors in the database. We have developed a novel learning-based video super-resolution algorithm with less memory requirements and computational cost. To this end, we adopted discrete cosine transform coefficients for feature vector components. Moreover, we designed an example selection procedure to construct a compact database. We conducted evaluative experiments using MPEG test sequences and real images to synthesize a high-resolution video. Experimental results show that our method improves the effectiveness of super-resolution algorithms, while preserving the quality of synthesized images.