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Real-Time Image Compression using SIMD architectures

机译:使用SIMD架构进行实时图像压缩

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Today, in the digitized satellite image domain, the needs for high dimension images increase considerably. To transmit or to stock such images (more than 6000 by 6000 pixels), we need to reduce their data volume and so we have to use image compression technics. In most cases, these operations have to be processed in Real-Time. The large amount of computations required by classical image compression algorithms prohibits the use of common sequential processors.rnTo solve this problem, CEA in collaboration with CNES has tried to define the best suited architecture for the image compression. In order to achieve this aim, we developed and evaluated a new parallel image compression algorithm for general purpose parallel computers using data-parallelism.rnThe purpose of this paper is to present this new parallel image compression algorithm. We present implementation results on several parallel computers. We also examine load balancing and data mapping problems. We end by defining optimal characteristics of the parallel machine for Real-Time image compression.
机译:如今,在数字化卫星图像领域,对高维图像的需求大大增加。要传输或存储此类图像(大于6000 x 6000像素),我们需要减少其数据量,因此我们必须使用图像压缩技术。在大多数情况下,必须实时处理这些操作。传统图像压缩算法所需的大量计算禁止使用通用的顺序处理器。为了解决此问题,CEA与CNES协作,试图为图像压缩定义最合适的体系结构。为了达到这个目的,我们开发并评估了一种使用数据并行性的通用并行计算机新并行图像压缩算法。本文的目的是提出这种新的并行图像压缩算法。我们在几台并行计算机上展示了实现结果。我们还将检查负载平衡和数据映射问题。我们首先定义用于实时图像压缩的并行机的最佳特性。

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