<b>Multi-Class Dictionary Design Algorithm Based on Iterative Class Update K-SVD for Image Compression</b>
<b>Multi-Class Dictionary Design Algorithm Based on Iterative Class Update K-SVD for Image Compression</b>
复制标题
<b>基于迭代类更新K-SVD的图像压缩多类字典设计算法</b>
DOI：
10.11371/tievciieej.8.1_44
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Yoshiyuki Yashima
中科院分区：
文献类型：
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作者：
Ji Wang;Yukihiro Bandoh;Atsushi Shimizu;Yoshiyuki Yashima
K-SVD （k -奇异值分解）是一种学习字典的流行技术，它提供了输入数据的稀疏表示，并已应用于几个图像编码应用。众所周知，K-SVD的性能在很大程度上取决于训练图像的特征。因此，考虑到自然图像的各种特征，多类字典方法是合适的。然而，大多数已发表的多类词典研究都是基于预定分类，没有考虑分类阶段和词典训练阶段之间的关系。因此，将字典训练与分类优化相结合，仍有提高编码效率的空间。在本文中，我们提出了一种多类字典设计方法，该方法重复了以下两个阶段：所有训练向量的类更新阶段和每个类的K-SVD字典更新阶段。实验表明，该方法在固定分类任务下，bd -比特率得分为6% ~ 48%，BD-PSNR值为0.4 ~ 1.6 dB，优于传统方法。
< Summary> K-SVD (K-Singular Value Decomposition) is a popular technique for learning a dictionary that offers sparse representation of the input data, and has been applied to several image coding applications. It is known that K-SVD performance is largely dependent on the features of the training images. Therefore, a multi-class dictionary approach is appropriate for natural images given the variety of their features. However, most published investigations of multi-class dictionaries are based on predetermined classification and do not consider the relation between classification stage and dictionary training stage. Therefore, there is still room for improving coding efficiency by linking dictionary training with classification optimization. In this paper, we propose a multi-class dictionary design method that repeats the following two stages: class update stage for all training vectors and dictionary update stage for each class by K-SVD. Experiments indicate that the proposed method outperforms the conventional alternatives as it achieves, for the fixed classification task, BD-bitrate scores of 6% to 48% and the BD-PSNR value of 0.4 dB to 1.6 dB.