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...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers,
2010-12-02T20:00:26Z.
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| Subjects: | |
| Online Access: | Get fulltext |
| Summary: | 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 low-complexity at a minimal loss of high image-quality with the ability to interpolate diverse image content. NVIDIA Corporation |
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