首页> 外文会议>International Joint Conference on Computer Vision and Computer Graphics Theory and Applications >PARALLEL LOSSY COMPRESSION FOR HD IMAGES A New Fast Image Magnification Algorithm for Lossy HD Video Decompression Over Commodity GPU
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PARALLEL LOSSY COMPRESSION FOR HD IMAGES A New Fast Image Magnification Algorithm for Lossy HD Video Decompression Over Commodity GPU

机译:用于高清图像的并行损耗压缩新型快速图像放大算法,用于商品GPU的损耗高清视频解压缩

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Today High Definition (HD) for video contents is one of the biggest challenges in computer vision. The 1080i standard defines the minimum image resolution required to be classified as HD mode. At the same time bandwidth constraints and latency don't allow the transmission of uncompressed, high resolution images. Often lossy compression algorithms are involved in the process of providing HD video streams, because of their high compression rate capabilities. The main issue concerned to these methods, while processing frames, is that high frequencies components in the image are neither conserved nor reconstructed. Our approach uses a simple downsampling algorithm for compression, but a new, very accurate method for decompression which is capable of high frequencies restoration. Our solution Is also highly parallelizable and can be efficiently implemented on a commodity parallel computing architecture, such as GPU, obtaining extremely fast performances.
机译:今天的视频内容的高清(HD)是计算机愿景中最大的挑战之一。 1080i标准定义了所需的最小图像分辨率作为HD模式。在相同的时间带宽约束和延迟不允许传输未压缩的高分辨率图像。由于其高压缩速率能力,通常涉及提供高清视频流的过程中的损失的压缩算法。涉及这些方法的主要问题,同时处理帧,是图像中的高频分量既不保守也不重建。我们的方法使用简单的下采样算法进行压缩,但是一种能够高频率恢复的新的,非常准确的解压缩方法。我们的解决方案也非常平行化,可以在商品并行计算架构(如GPU)上有效地实现,获得极快的性能。

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