In this paper we propose a novel learning-based video super resolution algorithm with less memory requirements and computational cost. To this end, we adopt discrete cosine transform (DCT) coefficients for feature vector components. Moreover we design an example selection procedure to construct a compact database. We conducted evaluative experiments using MPEG test sequences to synthesize a high resolution video. Experimental results show that our method can improve effectiveness of super-resolution algorithm, while preserving the quality of synthesized image.