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Adaptive and efficient colour quantisation based on a growing self-organising map

机译：基于不断增长的自组织图的自适应高效色彩量化

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Studies on colour quantisation have indicated that its applications range from the relaxation of displaying hardware constraints in early years to a modern usage of facilitating content-based image retrieval tasks. Among many alternatives, approaches based on neural network models are generally accepted to be able to produce quality results in colour quantisation. However, these approaches using n quantised neurons require O(n) for a full search strategy, which is inefficient when n becomes large. In view of this, we propose to incorporate a growing quadtree structure into a self-organising map (GQSOM) which reaches a search time O(logn). Specifically, the strategy of inheriting from parent neurons hierarchically facilitates a much more efficient and flexible learning process. Both theoretical and empirical studies have shown that our approach is adaptive in determining an appropriate number of quantised colours, and the performance is significantly improved without compromise of the quantisation quality.
机译：有关色彩量化的研究表明，其应用范围从早期放宽显示硬件的限制到促进基于内容的图像检索任务的现代使用。在许多替代方案中，基于神经网络模型的方法通常被接受为能够在颜色量化中产生高质量的结果。但是，这些使用n个量化神经元的方法需要O（n）进行完整搜索，这在n变大时效率很低。鉴于此，我们建议将增长的四叉树结构合并到达到搜索时间O（logn）的自组织映射（GQSOM）中。具体而言，从父级神经元分层继承的策略可促进更高效和灵活的学习过程。理论和经验研究均表明，我们的方法可自适应地确定适当数量的量化颜色，并且在不影响量化质量的前提下，性能得到了显着提高。

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