https://scholars.lib.ntu.edu.tw/handle/123456789/607203
標題: | Distribution-Adaptive Contexts and Probability Model Adjusting for Lossless Image Compression | 作者: | Chang J.-M JIAN-JIUN DING |
關鍵字: | adaptive arithmetic coding;adaptive context modeling;lossless image compression;probability model;Digital arithmetic;Image coding;Image compression;Probability distributions;Adaptive arithmetic coding;Adaptive context modeling;Adjusting mechanism;Coding methods;Context models;Entropy coding;Frequency Tables;Lossless image compression;Performance;Probability modelling;Image enhancement | 公開日期: | 2021 | 起(迄)頁: | 585-586 | 來源出版物: | 2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021 | 摘要: | Adaptive arithmetic coding (AAC) is an advanced entropy coding method. However, its performance is highly dependent on the context assignment method and the frequency table adjusting mechanisms. In this work, two techniques to improve the performance of AAC on lossless image compression are proposed. First, a method to adaptively vary the thresholds for context assignment according to the histogram is proposed. Moreover, a hyper-Laplacian probability model is applied to construct the frequency table and its parameters are adjusted adaptively according to the local weighted variance. With these techniques, lossless image compression can be performed in a much more effective way. ? 2021 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123501286&doi=10.1109%2fGCCE53005.2021.9621797&partnerID=40&md5=a3ea66c54851ffee6ee910d6d251fc31 https://scholars.lib.ntu.edu.tw/handle/123456789/607203 |
DOI: | 10.1109/GCCE53005.2021.9621797 |
顯示於: | 電機工程學系 |
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