| Bayer image parallel decoding based on GPU | |
| Hu, Rihui1,2; Xu, Zhiyong1; Wei, Yuxing1; Sun, Shaohua3; Hu, R. (ustc_hui@126.com) | |
| Volume | 8558 |
| Pages | 85581T |
| 2012 | |
| Language | 英语 |
| ISSN | 0277786X |
| DOI | 10.1117/12.999431 |
| Indexed By | Ei |
| Subtype | 会议论文 |
| 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, 2Kx2Kx16bit. 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 1Kx1Kx16bit 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. © Copyright SPIE.; 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, 2Kx2Kx16bit. 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 1Kx1Kx16bit 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. © Copyright SPIE. |
| Conference Name | Proceedings of SPIE: Optoelectronic Imaging and Multimedia Technology II |
| Conference Date | 2012 |
| Citation statistics | |
| Document Type | 会议论文 |
| Identifier | http://ir.ioe.ac.cn/handle/181551/7695 |
| Collection | 光电探测与信号处理研究室（五室） |
| Corresponding Author | Hu, R. (ustc_hui@126.com) |
| Affiliation | 1. Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan, 610209, China 2. Graduate School, Chinese Academy of Sciences, Beijing, 100039, China 3. 750 Test Field of China Shipbuilding Industry Corporation, Kunming, Yunnan, 650051, China |
| Recommended Citation GB/T 7714 | Hu, Rihui,Xu, Zhiyong,Wei, Yuxing,et al. Bayer image parallel decoding based on GPU[C],2012:85581T. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| 2012-2168.pdf（224KB） | 会议论文 | 开放获取 | CC BY-NC-SA | Application Full Text | ||
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