Image processing applications may add special effects such as blurring to video frame data in order to simulate the natural blur process of a camera lens or the depth of field effect associated with the human visual system. One approach to image blurring may involve the application of a filter to the pixels to the image to be blurred. High precision blurring, however, may be difficult to achieve due to limitations placed on conventional filtering solutions by the input and/or output pixel density of the image to be blurred. For example, sub-pixel (e.g., 2.5 pixel) blurring may involve the proportional mixing of multiple filters (e.g., one filter blurring at a two pixel density and another filter blurring at a three pixel density), which may lead to more complexity, increased power consumption and/or reduced battery life. Indeed, fractional (e.g., less than one pixel) sub-pixel blurring may not even be achievable under conventional approaches.
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