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Sponsored Feature: A Landmark in Image Processing: DMIP
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Sponsored Feature: A Landmark in Image Processing: DMIP

September 24, 2008 Article Start Page 1 of 2 Next

[In this Intel-sponsored feature, part of the Gamasutra Visual Computing microsite, Lightspeed Publishing's Lee Purcell lays out deferred mode image processing, a new addition to the Intel Integrated Performance Primitives Library, which speeds up complex image-processing tasks with up to 3X performance increases.]

Wherever you look, the graphical resolution of commonly used digital image formats is steadily increasing, resulting in larger file sizes and more intensive processing requirements. In several fields of image processing-digital photography, high-definition digital moviemaking, medical diagnostics, surveillance imaging, and others-frame sizes are increasing substantially.

In the case of digital video formats, such as Cinema 2K and 4K, the color space is also being expanded, further increasing the file sizes. File sizes for Cinema 4K content can be as much as one terabyte per hour of video. On the other end of the scale, even mobile handheld devices routinely capture images that can be several megapixels in size. With image sizes of this magnitude, fresh approaches are needed to maintain performance when manipulating and processing image data.

In response to a requirement from a strategic Intel customer involved in large-scale computer tomography images, Intel software engineers began conceptualizing a framework for more efficiently using the extensive library of image-processing algorithms available in Intel Integrated Performance Primitives (Intel IPP) library.

The resulting solution, which is featured in the Intel IPPP version 6.0 release, is called deferred mode image processing (DMIP). DMIP effectively handles large image data arrays that don't fit entirely within the processor L2 cache. 

DMIP, now an integral part of the Intel IPP package, performs pipelined sequences of fast functions to process image data in manageable portions, whether organized by tile, block, slice, or another element. This approach effectively combines the benefits of pipelined processing with manually optimized code of the Intel IP library.

A directed acyclic graph (DAG) defines inputs (from image data sources), outputs (destination images or data destined for memory), and operations and represents each as nodes on the graph. These nodes correspond to image-processing functions and their inputs and outputs. For operations that can be handled concurrently, parallel threads are generated to enhance performance. Using DMIP can accelerate image-processing tasks between one and a half to three times compared to a non-pipelined approach.

One key benefit: DMIP provides formula-level access to the vast library of Intel IP functions. Within the Intel IP version 6.0 release, developers can choose from among thousands of C functions that encompass a large span of data operations.

Those not familiar with full range of options in the Intel IP library can sometimes be discouraged from employing the algorithms in their applications. By removing the need to focus on the details of low-level programming, DMIP simplifies access to library functions, letting developers integrate advanced, proven routines into their code and take advantage of data alignment performance gains, particularly on Intel processors. These gains typically result in significantly faster instruction processing times for aligned data, commonly achieving speed increases of two to three times.

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