Polyphase interpretation of empirical image interpolation

We observe several characteristics of empirical image interpolating algorithms and contribute four novel concepts and claims. First, we interpret well-known classification-based filtering algorithms in terms of their polyphase components. We examine the underlying principles behind the various fixed-scale linear interpolating kernels. Second, we conceptually extend the properties of the multiple filters to two dimensions to analyze frequency domain characteristics common to all empirically-designed interpolating filters. Third, we propose a general linear filter for image interpolation, which uses a universal magnitude response and zero-phase. Finally, the proposed filter is further generalized to support arbitrary scaling factors. We claim that at any scaling factor, the proposed algorithm yields lowcomplexity at a minimal loss of high image-quality with the ability to interpolate diverse image content.

• Type: Conference
• Year: 2009
• Where: ICASSP
• Authors: Karl S. Ni, Truong Q. Nguyen
• Keywords: ICASSP 2009, Image Interpolating Algorithms, Interpolating, Linear Interpolating Kernels, Signal Processing