Filters with slowly decaying impulse responses have many uses in computer graphics. recursive filters are often the fastest option for such cases. In this paper, we derive closed-form formulas for computing the exact ...
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ISBN:
(纸本)9781450342797
Filters with slowly decaying impulse responses have many uses in computer graphics. recursive filters are often the fastest option for such cases. In this paper, we derive closed-form formulas for computing the exact initial feedbacks needed for recursivefiltering infinite input extensions. We provide formulas for the constant-padding (e.g. clamp-to-edge), periodic (repeat) and even-periodic (mirror or reflect) extensions. These formulas were designed for easy integration into modern block-parallel recursive filtering algorithms. Our new modified algorithms are state-of-the-art, filtering images faster even than previous methods that ignore boundary conditions.
filtering is a fundamental tool in image processing, and its acceleration affects many applications. Therefore, various algorithmic and hardware accelerations have been proposed for filtering. recursive processing usi...
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filtering is a fundamental tool in image processing, and its acceleration affects many applications. Therefore, various algorithmic and hardware accelerations have been proposed for filtering. recursive processing using infinite impulse response (IIR) filtering is an efficient algorithm, and various hardware acceleration methods have been applied to IIR filtering. In addition, a domain-specific language (DSL) of RecFilter was proposed to generate efficient IIR code for various hardware applications as an extension of image processing language, Halide. recursive filters based on sliding discrete cosine transform (SDCT) have been the most efficient approximations in recent years. For hardware acceleration, parallelization of recursive filters is challenging. One of the most efficient methods is tile-based parallelization. However, even if a function is optimized and modularized, it is not sufficiently optimized for applications where various pre/post-processing steps are coupled before and after filtering. Additionally, multiplatform deployment requires reimplementation of the code. In this study, we extended Halide for SDCT convolutions to realize efficient computing of image processing applications with filtering, named SlidingConv. The experimental results showed that SlidingConv is faster than the hand-tuned CPU code and 1/1900 of the hand-tuned code length, running more efficiently than de facto libraries like OpenCV. To verify its efficiency, we deployed the code on various hardware (x86/64 CPU with AVX2/AVX-512, ARM CPU, and GPU). In addition, we verified that the proposed method can accelerate image processing with pre/post-processing for filtering. Our code is available at https://***/SlidingConv/.
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