Block-based rotation of arbitrary-shaped images

Abstract

An apparatus, program product and method for rotating image data using a block-based approach, wherein for each of a plurality of blocks (122) of image data that define an image (120), a translation vector is applied to the block (122) to translate the block (122) a desired angle of rotation about a rotation point, e.g., to translate an anchor position for the block (122) from a source point to a destination point. In addition, the image data within the block (122) is rotated according to the desired angle of rotation, such that when the rotated image data is stored at the destination point of the block, the image data within the block (122) is effectively rotated to the desired position.

Description

technical field [0001] The present invention relates generally to manipulation of digital image data, and in particular to rotation of image data. Background technique [0002] Graphics or image data processing in computers or other data processing systems is often very computationally expensive, requiring large amounts of memory and processor resources. In many data processing systems, an image is represented by an array of pixel elements, or "pixels," each pixel having a "color depth," which defines the total number of color variations that can be expressed in each pixel. As the resolution and / or color depth of an image increases, the perceived quality of the image, which can be reflected in realism, accuracy, taxonomy, dithering, etc., also increases. Accordingly, there is a continuing need for data processing systems that support images with increased resolution and / or color depth. [0003] As a general rule, the greater the resolution and / or color depth of an image, t...

AI Technical Summary

Problems solved by technology

However, for cases where the shape of the closely related (non-background) image data in the image is substantially non-rectangular, bounding the area by a rectangle may introduce a considerable amount of white space or background data

However, most conventional image rotation algorithms cannot determine when image data is closely related to an image, or when that image data constitutes a background color, so all image data within a rectangular area is roughly treated the same way

As a result, significant processing resources may be spent on rotating image data that is not particularly closely related to the actual image being rotated

Images