Better Fixed-Point Filtering with Averaging Trees

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dc.contributor.authorAdams, Andrewen_US
dc.contributor.authorSharlet, Dillonen_US
dc.contributor.editorJosef Spjuten_US
dc.contributor.editorMarc Stammingeren_US
dc.contributor.editorVictor Zordanen_US
dc.date.accessioned2023-01-23T10:23:46Z
dc.date.available2023-01-23T10:23:46Z
dc.date.issued2022
dc.description.abstractProduction imaging pipelines commonly operate using fixed-point arithmetic, and within these pipelines a core primitive is convolution by small filters - taking convex combinations of fixed-point values in order to resample, interpolate, or denoise. We describe a new way to compute unbiased convex combinations of fixedpoint values using sequences of averaging instructions, which exist on all popular CPU and DSP architectures but are seldom used. For a variety of popular kernels, our averaging trees have higher performance and higher quality than existing standard practice.en_US
dc.description.number3
dc.description.sectionheadersAcceleration Structures
dc.description.seriesinformationProceedings of the ACM on Computer Graphics and Interactive Techniques
dc.description.volume5
dc.identifier.doi10.1145/3543869
dc.identifier.issn2577-6193
dc.identifier.urihttps://doi.org/10.1145/3543869
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1145/3543869
dc.publisherACM Association for Computing Machineryen_US
dc.subjectCCS Concepts: Computing methodologies -> Image processing Additional Key Words and Phrases: fixed-point arithmetic, image filtering
dc.subjectComputing methodologies
dc.subjectImage processing Additional Key Words and Phrases
dc.subjectfixed
dc.subjectpoint arithmetic
dc.subjectimage filtering
dc.titleBetter Fixed-Point Filtering with Averaging Treesen_US
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High-Performance Graphics 2022