Linear row and column predictors for the analysis of resized images

This paper adds a new perspective to the analysis and detection of periodic interpolation artifacts in resized digital images. Instead of relying on a single, global predictor, we discuss how the specific structure of resized images can be explicitly modeled by a series of linear predictors. Characteristic periodic correlations between neighboring pixels are then measured in the estimated predictor coefficients itself. Experimental results on a large database of images suggest a superior detection performance compared to state-of-the-art methods.