A low complex context adaptive image interpolation algorithm for real-time applications

Recently a lot of interpolation algorithms are proposed, but these interpolation algorithms are highly computationally expensive. Hence these algorithms cannot be implemented and used in real time applications. In view of real time applications we have proposed a computationally simple interpolation algorithm. In our proposed algorithm the unknown pixels are categorized into various bins depending upon the characteristic of the neighboring pixels (activity level) and for each bin fixed prediction parameters are used for prediction. We have presented different set of fixed predictors for both smooth type and edgy type of images. We have also proposed a modified algorithm in which selection of prediction parameter is done on block by block basis instead of image basis. Our proposed algorithm gives much better qualitative and quantitative performance as compared to other computationally simple interpolation algorithms.