Context Modeling Based on Description Length
Funds:
The National Natural Science Foundation of China (61062005)
-
摘要: 在基于Context建模的熵編碼系統中，為了達到預期的壓縮性能，需要通過Context量化來緩解由高階Context模型所引入的Context稀釋問題。為此，該文提出一種通過最小化描述長度來實現Context量化(Minimum Description Length Context Quantization, MDLCQ)的算法。該算法使用描述長度作為評價準則，通過動態(tài)規(guī)劃算法來實現單條件的最優(yōu)Context量化，然后通過循環(huán)迭代來實現多條件的Context量化。該算法不僅可以得到多值信源的優(yōu)化Context量化器，而且可以自適應地確定各個條件的重要性從而確定模型的最佳階數。實驗結果表明：由MDLCQ算法所得到的Context量化器，可以明顯改善熵編碼系統的壓縮性能。Abstract: In entropy coding systems based on the context modeling, the context dilution problem introduced by high-order context models needs to be alleviated by the context quantization to achieve the desired compression gain. Therefore, an algorithm is proposed to implement the Context Quantization by the Minimizing Description Length (MDLCQ) in this paper. With the description length as the evaluation criterion, the Context Quantization Of Single-Condition (CQOSC) is attained by the dynamic programming algorithm. Then the context quantizer of multi-conditions can be designed by the iterated application of CQOSC. This algorithm can not only design the optimized context quantizer for multi-valued sources, but also determine adaptively the importance of every condition so as to design the best order of the model. The experimental results show that the context quantizer designed by the MDLCQ algorithm can apparently improve the compression performance of the entropy coding system.
-
周映虹, 馬爭鳴. 基于上下文建模的分類排序小波圖像編碼算法[J]. 電子與信息學報, 2006, 28(12): 2405-2408. ZHOU Yinghong and MA Zhengming. Context-based quantization and sorting in wavelet image coding[J]. Journal of Electronics Information Technology, 2006, 28(12): 2405-2408. LAKHDHAR K and LEFEDVRE R. Context-based adaptive arithmetic encoding of EAVQ indices[J]. IEEE Transactions on Audio, Speech and Language Processing, 2012, 20(5): 1473-1481. CHUAH S, DUMITRESCU S, and WU Xiaolin. optimized predictive image coding with bound[J]. IEEE Transactions on Image Processing, 2013, 22(12): 5271-5281. STRUTZ T. Entropy based merging of context models for efficient arithmetic coding[C]. IEEE International Conference on Acoustic, Speech and Signal Processing, Florence, Italy, 2014: 1991-1995. KIM S and CHO N I. Hierarchical prediction and context adaptive coding for lossless color image compression[J]. IEEE Transactions on Image Processing, 2014, 23(1): 445-449. XU Mantao, WU Xiaolin, and Pasi F. Context quantization by kernel fisher discriminant[J]. IEEE Transactions on Image Processing, 2006, 15(1): 169-177. WANG Wei, PENG Shuyan, and CHEN Jianhua. Context quantization based on the ant K-Means clustering algorithm[C]. International Conference on Systems and Informatics, Yantai, 2012: 1573-1576. CHEN Min, LIU Chen, and WANG Fuyan. Context quantization under the minimum increment of the adaptive code length[C]. International Conference on Information Technology and Applications, Chengdu, 2013: 9-12. WEINBERGER M J, RISSANEN J J, and ARPS R B. Applications of universal context modeling to lossless compression of gray-scale images[J]. IEEE Transactions on Image Processing, 1996, 5(4): 575-586. WU Xiaolin, CHOU P A, and XUE Xiaohui. Minimum conditional entropy context quantization[C]. IEEE International Symposium on Information Theory, Sorrento, Italy, 2000: 43. CHEN Jianhua. Context modeling based on context quantization with application in wavelet image coding[J]. IEEE Transactions on Image Processing, 2004, 13(1): 26-32. CAGNAZZO M, ANTONINI M, and BARLAUD M. Mutual information-based context quantization[J]. Signal Processing: Image Communication, 2010, 25(1): 64-74. FORCHHAMMER S, WU Xiaolin, and ANDERSEN J D. Optimal context quantization in lossless compression of image data sequences[J]. IEEE Transactions on Image Processing, 2004, 13(4): 509-517. ICHIRO M, HIROFUMI M, JOJI M, et al. Design and evaluation of minimum-rate predictors for lossless image coding[J]. System and Computers in Japan, 2007, 38(5): 90-98. -
計量
- 文章訪問數: 1341
- HTML全文瀏覽量: 215
- PDF下載量: 552
- 被引次數: 0
下載:
