Fast mesh compression is becoming a requisite in several applications such as medical imaging and video games.Graphics Processing Units (GPUs) are recently becoming massively parallel devices for Single Instruction, MultipleData (SIMD) computing, addressing hence greater implementation challenges. Transformation and Quantization (TQ) isconsidered the second highest workload part of the wavelet-based mesh coding. Therefore, its acceleration will furtherimprove the overall processing speed of the coding. In this paper, an OpenCL (Open Computing Language) accelerationof TQ is proposed. The Butterfly Wavelet Transform (BWT) based on the unlifted scheme is adopted in thetransformation method while the embedded deadzone quantization is employed for the wavelet quantization. A chunkrearrangement process is applied for the computation of the neighborhood information needed for the Butterflysubdivision stencils. Accordingly, every chunk proceeds independently the prediction of the wavelet coefficients andtheir quantization. The key insights behind the proposed TQ method on GPU are a smart memory management and anefficient memory data mapping. Extensive experimental assessments demonstrate the effectiveness of our GPUimplementation in terms of memory and runtime costs while preserving the rate distortion performance of the state-ofthe-art Bitplane coder.
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