Chang J.-MJIAN-JIUN DING2022-04-252022-04-252021https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123501286&doi=10.1109%2fGCCE53005.2021.9621797&partnerID=40&md5=a3ea66c54851ffee6ee910d6d251fc31https://scholars.lib.ntu.edu.tw/handle/123456789/607203Adaptive 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.adaptive arithmetic codingadaptive context modelinglossless image compressionprobability modelDigital arithmeticImage codingImage compressionProbability distributionsAdaptive arithmetic codingAdaptive context modelingAdjusting mechanismCoding methodsContext modelsEntropy codingFrequency TablesLossless image compressionPerformanceProbability modellingImage enhancementconference paper10.1109/GCCE53005.2021.96217972-s2.0-85123501286