https://scholars.lib.ntu.edu.tw/handle/123456789/580939
標題: | Deep Learning Based EBCOT Source Symbol Prediction Technique for JPEG2000 Image Compression Architecture | 作者: | I-HSIANG WANG JIAN-JIUN DING |
關鍵字: | Binary codes; Digital image storage; Forecasting; Image compression; Learning systems; Regression analysis; Visual communication; Binary arithmetic coders; Embedded block coding with optimized truncation; JPEG2000 image compression; Learning architectures; Machine learning techniques; Prediction techniques; Ridge regression; Wavelet coefficients; Deep learning | 公開日期: | 2020 | 起(迄)頁: | 226-229 | 來源出版物: | 2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020 | 摘要: | In this work, an efficient and robust learning-based JPEG2000 architecture is proposed. It uses machine learning techniques for predicting and encoding the decision bit in the embedded block coding with optimized truncation (EBCOT) process. First, we apply non-locally weighted ridge regression to predict the quantized wavelet coefficients in the LL subband. Then, during the EBCOT process, we perform inter/intra subband prediction and inter/intra bit plane symbol prediction to estimate the activity of the decision bit using the deep learning architecture. Then, the binary prediction result is treated as an additional context and the decision bit is eventually coded using an advanced context-based adaptive binary arithmetic coder. Simulations show that the proposed framework provides the same visual quality as conventional codecs with as much as 30% bitrate savings. ? 2020 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099456146&doi=10.1109%2fVCIP49819.2020.9301822&partnerID=40&md5=6ba3b3df41eb1e0ea0c65d27e2df8633 https://scholars.lib.ntu.edu.tw/handle/123456789/580939 |
DOI: | 10.1109/VCIP49819.2020.9301822 |
顯示於: | 電機工程學系 |
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