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Optimization of Packetization Masks for Image Coding Based on an Objective Cost Function for Desired Packet Spreading

机译:基于目标成本函数的期望分组扩散的图像编码分组掩模的优化

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In image communication over lossy packet networks (e.g., cell phone communication), packet loss errors lead to damaged images. Damaged images can be repaired with passive error concealment methods, which use neighboring coefficient or pixel values to estimate the missing ones. Neighboring image data should, thus, be spread over different packets. This paper presents a novel robust packetization method for the transmission of image content in lossy packet networks. We first define novel criteria for a good packetization. Based on these properties, we propose a cost function for packetization masks. We then use stochastic optimization to calculate optimal packetization masks. We test our packetization technique on both wavelet coding and DCT coding. Compared to other packetization techniques, we are able to achieve the same or better mean quality of the reconstructed images but with less fluctuation in quality, which is important for the viewer experience. In this way, we significantly increase the worst case quality, especially for high packet loss rates. This leads to visually more pleasing images in case of a passive reconstruction.
机译:在有损分组网络上的图像通信(例如,蜂窝电话通信)中,分组丢失错误导致图像损坏。可以使用被动错误隐藏方法修复损坏的图像,该方法使用相邻系数或像素值来估计丢失的图像。因此,相邻的图像数据应分布在不同的数据包上。本文提出了一种新颖的鲁棒打包方法,用于有损分组网络中图像内容的传输。我们首先定义一个好的打包标准。基于这些属性,我们提出了打包掩码的成本函数。然后,我们使用随机优化来计算最佳打包掩码。我们在小波编码和DCT编码上测试我们的分组技术。与其他打包技术相比,我们能够获得相同或更好的重建图像平均质量,但质量波动较小,这对于观看者的体验至关重要。这样,我们显着提高了最坏情况的质量,尤其是对于高丢包率的情况。在被动重建的情况下,这会导致视觉上更令人愉悦的图像。

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