多尺度模式与灵活分割和字典训练的近似匹配

Nelson Francisco, Nuno M. M. Rodrigues, E. Silva, M. Carvalho, Sérgio Faria, V. Silva, M. Reis
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引用次数: 0

摘要

在本文中,我们提出了一种新的MMP(多维多尺度解析器)分割方法,结合字典训练过程。这些改进使得MMP在低到中等压缩比下,在平滑和非平滑图像上的表现都优于JPEG2000和H.264/AVC等基于变换的编码器。新方法允许MMP以更有效的方式利用图像的结构,显著提高其平滑图像的性能。此外,新技术不会影响非光滑图像的结果,其中MMP已经优于基于变换的算法。关键词:图像编码,模式匹配,图像分割,图像处理
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Casamento Aproximado de Padrões Multiescala com Segmentação Flexível e Treino do Dicionário
In this paper we present a new segmentation method for the MMP (Multidimensional Multiscale Parser) algorithm, allied with a dictionary training procedure. These improvements allowed MMP to outperform state-of-the-art transform-based encoders like JPEG2000 and H.264/AVC, for both smooth and non-smooth images, at low to medium compression ratios. The new approach allowed MMP to exploit the images’ structure in a more effective way, increasing significantly its performance for smooth images. Additionally, the new techniques do not compromise the results achieved for non-smooth images, where MMP already outperformed transform-based algorithms. Keywords— Image coding, pattern matching, image segmentation, image processing
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