Adaptive General Scale Interpolation Based on Weighted Autoregressive Models

The autoregressive (AR) model has been widely used in signal processing for its effective estimation, especially in image processing. Many dedicated
$2\times $
interpolation algorithms adopt the AR model to describe the strong correlation between low-resolution (LR) pixels and high-resolution (HR) pixels. However, these AR model-based methods closely depend on the fixed relative position between LR pixels and HR pixels that are nonexistent in the general scale interpolation. In this paper, we present an adaptive general scale interpolation algorithm that is capable of arbitrary scaling factors considering the nonstationarity of natural images. Different from other dedicated
$2\times $
interpolation methods, the proposed AR terms are modeled by pixels with their adjacent unknown HR neighbors. To compensate for the information loss caused by mismatches of AR models, we consider a weighting scheme suitable for general scale situations based on the pixel similarity to increase accuracy of the estimation. Comprehensive experiments demonstrate the effectiveness of the proposed method on general scaling factors. The maximum gain of peak signal-to-noise ratio is 2.07 dB compared with segment adaptive gradient angle in
$1.5 \times $
enlargements. To evaluate the performance in resolution adaptive video coding, we have also tested our method on Joint Scalable Video Model codec and obtained better subjective quality and rate-distortion performance.