Multi-Class Dictionary Design Algorithm Based on Iterative Class Update K-SVD for Image Compression

摘要

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.