Distribution-Adaptive Contexts and Probability Model Adjusting for Lossless Image Compression

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...

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Veröffentlicht in:2021 IEEE 10th Global Conference on Consumer Electronics (GCCE) S. 585 - 586
Hauptverfasser: Chang, Jer-Ming, Ding, Jian-Jiun
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 12.10.2021
Schlagworte:
Adaptation models
adaptive arithmetic coding
adaptive context modeling
Conferences
Current distribution
Entropy coding
Histograms
Image coding
lossless image compression
Probability distribution
probability model
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Zusammenfassung: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.
DOI:10.1109/GCCE53005.2021.9621797