Bayer image parallel decoding based on GPU

Rihui Hu; Zhiyong Xu; Yuxing Wei; Shaohua Sun

doi:10.1117/12.999431

30 November 2012 Bayer image parallel decoding based on GPU

Rihui Hu, Zhiyong Xu, Yuxing Wei, Shaohua Sun

Proceedings Volume 8558, Optoelectronic Imaging and Multimedia Technology II; 85581T (2012) https://doi.org/10.1117/12.999431
Event: Photonics Asia, 2012, Beijing, China

Abstract

In the photoelectrical tracking system, Bayer image is decompressed in traditional method, which is CPU-based. However, it is too slow when the images become large, for example, 2K×2K×16bit. In order to accelerate the Bayer image decoding, this paper introduces a parallel speedup method for NVIDA’s Graphics Processor Unit (GPU) which supports CUDA architecture. The decoding procedure can be divided into three parts: the first is serial part, the second is task-parallelism part, and the last is data-parallelism part including inverse quantization, inverse discrete wavelet transform (IDWT) as well as image post-processing part. For reducing the execution time, the task-parallelism part is optimized by OpenMP techniques. The data-parallelism part could advance its efficiency through executing on the GPU as CUDA parallel program. The optimization techniques include instruction optimization, shared memory access optimization, the access memory coalesced optimization and texture memory optimization. In particular, it can significantly speed up the IDWT by rewriting the 2D (Tow-dimensional) serial IDWT into 1D parallel IDWT. Through experimenting with 1K×1K×16bit Bayer image, data-parallelism part is 10 more times faster than CPU-based implementation. Finally, a CPU+GPU heterogeneous decompression system was designed. The experimental result shows that it could achieve 3 to 5 times speed increase compared to the CPU serial method.

Citation Download Citation

Rihui Hu, Zhiyong Xu, Yuxing Wei, and Shaohua Sun "Bayer image parallel decoding based on GPU", Proc. SPIE 8558, Optoelectronic Imaging and Multimedia Technology II, 85581T (30 November 2012); https://doi.org/10.1117/12.999431

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KEYWORDS

Optimization (mathematics)

Image processing

Image compression

Quantization

Image resolution

GPU based image processing

Wavelet transforms

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