Image Compression Using Vector Quantization with Variable Block Size Division

Matsumoto, Hiroki; Kichikawa, Fumito; Sasazaki, K.; Maeda, Junji; Suzuki, Yusuke

doi:10.1541/ieejeiss.130.1431

Cited by 4 publications

(1 citation statement)

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“…We have been studying vector quantization for image coding (Miyamoto, 2005;Sasazaki, 2008;Matsumoto, 2010;Sakakita, 2014). For vector quantization, we have to design a code book in advance.…”

Section: Introductionmentioning

confidence: 99%

Effect of Fuzzy and Crisp Clustering Algorithms to Design Code Book for Vector Quantization in Applications

Suzuki

Sakakita

Maeda

2015

Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing

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Image coding technologies are widely studies not only to economize storage device but also to use communication channel effectively. In various image coding technologies, we have been studied vector quantization. Vector quantization technology does not cause deterioration image quality in a high compression region and also has a negligible computational cost for image decoding. It is therefore useful technology for communication terminals with small payloads and small computational costs. Furthermore, it is also useful for biomedical signal processing: medical imaging and medical ultrasound image compression. Encoded and/or decoded image quality depends on a code book that is constructed in advance. In vector quantization, a code book determines the performance. Various clustering algorithms were proposed to design a code book. In this paper, we examined effect of typical clustering (crisp clustering and fuzzy clustering) algorithms in terms of applications of vector quantization. Two sets of experiments were carried out for examination. In the first set of experiments, the learning image to construct a code book was the same as the test image. In practical vector quantization, learning images are different from test images. Therefore, learning images that were different from test images were used in the second set of experiments. The first set of experiments showed that selection of a clustering algorithm is important for vector quantization. However, the second set of experiments showed that there is no notable difference in performance of the clustering algorithms. For practical applications of vector quantization, the choice of clustering algorithms to design a code book is not important.

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Section: Introductionmentioning

confidence: 99%