https://scholars.lib.ntu.edu.tw/handle/123456789/607200
標題: | K-Means Clustering Based Adaptive Context Assignment Method for Image AC Term Encoding | 作者: | Lu Y.-S Hua S.-C. JIAN-JIUN DING |
關鍵字: | Adaptive arithmetic coding;Context model;K-means clustering;Encoding (symbols);Image coding;Image enhancement;Image segmentation;Signal encoding;Context models;Encodings;Feature domain;Input datas;K-means clustering method;K-means++ clustering;Model method;Over segmentation and merging;Probability modelling | 公開日期: | 2021 | 來源出版物: | ISPACS 2021 - International Symposium on Intelligent Signal Processing and Communication Systems: 5G Dream to Reality, Proceeding | 摘要: | Context modeling is to classify the input data into several classes and apply different probability models for each class. It plays a critical role in data compression. Conventionally, when constructing the context, one often uses several criterions to separate each axis in the feature domain into several parts independently. However, it does not consider the correlation among different features. In this work, we propose an advanced context modeling method based on over-segmentation and merging in the feature domain using the k-means clustering method. It considers the relation among different features and the number of training data corresponding to each context is more balanced. The experiments in image AC term encoding show that, with the proposed context assignment method, the coding efficiency can be much improved. ? 2021 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124163023&doi=10.1109%2fISPACS51563.2021.9650990&partnerID=40&md5=e092595fadf40082d634e7eb4b126129 https://scholars.lib.ntu.edu.tw/handle/123456789/607200 |
DOI: | 10.1109/ISPACS51563.2021.9650990 |
顯示於: | 電機工程學系 |
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