{"id":158926,"date":"2010-02-01T00:00:00","date_gmt":"2010-02-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/high-quality-multi-pass-image-resampling\/"},"modified":"2018-10-16T20:00:39","modified_gmt":"2018-10-17T03:00:39","slug":"high-quality-multi-pass-image-resampling","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/high-quality-multi-pass-image-resampling\/","title":{"rendered":"High-quality multi-pass image resampling"},"content":{"rendered":"<div class=\"asset-content\">\n<p>This paper develops a family of multi-pass image resampling algorithms that use one-dimensional filtering stages to achieve high-quality results at low computational cost. Our key insight is to perform a frequency-domain analysis to ensure that very little aliasing occurs at each stage in the multi-pass transform and to insert additional stages where necessary to ensure this. Using one-dimensional resampling enables the use of small resampling kernels, thus producing highly efficient algorithms. We compare our results with other state of the art software and hardware resampling algorithms.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper develops a family of multi-pass image resampling algorithms that use one-dimensional filtering stages to achieve high-quality results at low computational cost. 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