Statistical search range adaptation solution for effective frame rate up-conversion

Abstract

The recent development of advanced television systems has demonstrated a need for an efficient video conversion technique. In this scenario, frame rate up conversion (FRUC) solutions play an important role due to their benefits in both increasing the viewing quality experience and reducing the cost of video transmission. However, with the recent increase in video resolution, notably from standard definition to high definition (HD) and ultra HD, FRUC now requires not only better interpolated frame quality but also lower FRUC time processing. Considering this problem, this study proposes a novel statistical learning based adaptive search range (SR) solution to enable an effective FRUC mechanism. In the proposed adaptive SR solution, a set of spatial-temporal features are carefully defined and exploited to adaptively assign an appropriate SR value to each considered block, notably by formulating the SR adaptation as a classification problem and using the well-known support vector machine framework for the classification task. Experimental results conducted on a rich set of common video test sequences show the advantages of the proposed adaptive SR solution, notably in both interpolated frame quality improvement and time processing reduction.