KEYWORDS: Quantization, Wavelets, Wavelet transforms, 3D modeling, Computer programming, Associative arrays, Signal processing, Digital signal processing
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, Multiple Data (SIMD) computing, addressing hence greater implementation challenges. Transformation and Quantization (TQ) is considered the second highest workload part of the wavelet-based mesh coding. Therefore, its acceleration will further improve the overall processing speed of the coding. In this paper, an OpenCL (Open Computing Language) acceleration of TQ is proposed. The Butterfly Wavelet Transform (BWT) based on the unlifted scheme is adopted in the transformation method while the embedded deadzone quantization is employed for the wavelet quantization. A chunk rearrangement process is applied for the computation of the neighborhood information needed for the Butterfly subdivision stencils. Accordingly, every chunk proceeds independently the prediction of the wavelet coefficients and their quantization. The key insights behind the proposed TQ method on GPU are a smart memory management and an efficient memory data mapping. Extensive experimental assessments demonstrate the effectiveness of our GPU implementation in terms of memory and runtime costs while preserving the rate distortion performance of the state-ofthe-art Bitplane coder.
KEYWORDS: 3D modeling, Computer programming, Data processing, Computing systems, Visualization, Wavelets, Data acquisition, Computer programming languages, 3D applications, Data modeling
3D multiresolution mesh compression systems are still widely addressed in many domains. These systems are more and more requiring volumetric data to be processed in real-time. Therefore, the performance is becoming constrained by material resources usage and an overall reduction in the computational time. In this paper, our contribution entirely lies on computing, in real-time, triangles neighborhood of 3D progressive meshes for a robust compression algorithm based on the scan-based wavelet transform(WT) technique. The originality of this latter algorithm is to compute the WT with minimum memory usage by processing data as they are acquired. However, with large data, this technique is considered poor in term of computational complexity. For that, this work exploits the GPU to accelerate the computation using OpenCL as a heterogeneous programming language. Experiments demonstrate that, aside from the portability across various platforms and the flexibility guaranteed by the OpenCL-based implementation, this method can improve performance gain in speedup factor of 5 compared to the sequential CPU implementation.
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