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一种快速的纹理预测和混合哥伦布的无损压缩算法

罗瑜 张珍珍

罗瑜, 张珍珍. 一种快速的纹理预测和混合哥伦布的无损压缩算法[J]. 电子与信息学报, 2018, 40(1): 137-142. doi: 10.11999/JEIT170305
引用本文: 罗瑜, 张珍珍. 一种快速的纹理预测和混合哥伦布的无损压缩算法[J]. 电子与信息学报, 2018, 40(1): 137-142. doi: 10.11999/JEIT170305
LUO Yu, ZHANG Zhenzhen. A Fast-lossless Compression Using Texture Prediction and Mixed Golomb Coding[J]. Journal of Electronics & Information Technology, 2018, 40(1): 137-142. doi: 10.11999/JEIT170305
Citation: LUO Yu, ZHANG Zhenzhen. A Fast-lossless Compression Using Texture Prediction and Mixed Golomb Coding[J]. Journal of Electronics & Information Technology, 2018, 40(1): 137-142. doi: 10.11999/JEIT170305

一种快速的纹理预测和混合哥伦布的无损压缩算法

doi: 10.11999/JEIT170305
基金项目: 

国家863计划项目(2015M16903),陕西省自然科学基金(2014K14-02-02)

A Fast-lossless Compression Using Texture Prediction and Mixed Golomb Coding

Funds: 

The National 863 Program of China (2015M16903), The Natural Science Foundation of Shaanxi Province (2014k14-02-02)

  • 摘要: 为了进一步降低芯片内无损压缩的运算复杂度和编码时间,该文在保持高压缩率的基础上,提出一种基于方向预测和混合熵编码的快速无损压缩算法。该算法首先采用自适应方法进行纹理方向的预测,以获得当前像素的参考像素,并计算预测残差;然后对预测残差进行混合哥伦布编码,最终大幅度地提高了无损压缩的压缩性能。实验结果显示,与基于梯度预测和变长编码的无损压缩算法相比,该算法在平均压缩率略有提升的前提下,平均编码时间减少了36.86%。
  • ITU-T Study Group 16.23008-2-2013. ITU-T recommendation h.265[S]. Geneva, 2013.
    SCHWARZ H, MARPE D, and WIEGAND T. Overview of the scalable video coding extension of the h.26/avc standard[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2007, 17(9): 1103-1120. doi: 10.1109/ TCSVT.2007. 905532.
    HNESH Allaeldien and DEMIREL Hasan. DWT-DCT-SVD based hybrid lossy image compression technique[C]. 2016 International Image Processing, Applications and Systems (IPAS), Hammamet, Tunisia, 2016, 11(5): 1150-1172. doi: 10.1109/TGRS.2016.2603527.
    LEVENIT Hrvoje, NENADI Kresimir, GALI Irena, et al. Compression parameters tuning for automatic image optimization in web applications[C]. ELMAR, 2016 International Symposium. Zadar, Groatia, 2016: 161-180. doi: 10.1109/ELMAR.2016.7731782.
    BRAHIMI T, BOUBCHIR L, FOURNIER R, et al. An improved multimodal signal-image compression scheme with application to natural images and biomedical data[J]. Multimedia Tools Applications, 2016, 9(7): 1-23. doi: 10.1007 /s11042-016-3952-7.
    XIAO Jun, TONG Miao, ZHANG Zhu, et al. A joint color image encryption and compression scheme based on hyper- chaotic system[J]. Nonlinear Dynamics, 2016, 84(4): 2333-2356. doi: 10.1007/s11071-061-2648-x.
    ZHOU N, PAN S, CHENG S, et al. Image compression encryption scheme based on hyper-chaotic system and 2D compressive sensing[J]. Optics Laser Technology, 2016, 82(2): 121-133. doi: 10.1016/j.optlastec.20.
    BUI Vy, CHING Lincheng, LI Dunling, et al. Comparison of lossless video and image compression codecs for medical computed tomography datasets[C]. 2016 IEEE International Conference on Big Data. Washington D.C., USA, 2016: 1123-1145. doi: 10.1109/BigData.2016.7841075.
    SHEN Hongda, PAN W David, and WU Dongsheng. Predictive lossless compression of regions of interest in hyper spectral images with no-data Regions[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(1): 173-182. doi: 10.1109/TGRS.2016.2603527.
    FAN Y, SHANG Q, and ZENG X. In-block prediction-based mixed lossy and losssless reference frame recompression for next generation video encoding[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2015, 25(1): 112-124. doi: 10.1109/TCSVT.2014.2329353.
    SILVERIRA D, POVALA G, AMARAL L, et al. A low complexity and lossless reference frame encoder algorithm for video coding [C]. IEEE International Conference on Acoustic Speech and Signal Processing, Danvers, 2014: 7408-7412. doi: 10.1109/ICASSP.2014.6855029.
    GUPTE A D, AMRUTUR B, MEHENDALE M M, et al. Memory bandwidth and power reduction using lossy reference frame compression in video encoding[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2015, 21(20): 225-230. doi: 10.1109/TCSVT.2011.2105599.
    MA Y and KANG L. Adaptive granularity selection in reference picture memory compression[C]. International Conference on Mechatronics, Electronic, Industrial and Control Engineering, Shenyang, China, 2015: 1158-1161. doi: 10.2991/meic-15.2015.263.
    LEE Y. A new frame recompression algorithm integrated with h.264 video compression[C]. International Symposium on Circuits and Systems, Nagoya, 2007: 1621-1624. doi: 10.1109/ ISCAS.2007.378829.
    SAMPAIO F, ZATT B, SHAFIQUE M, et al. Content- adaptive reference frame compression based on intra-frame prediction for multi view video coding[C]. IEEE International Conference on Image Processing, Melboume, 2013: 1831-1835. doi: 10.1109.ICIP.2013.6738377.
    LIAN X, LIU Z, ZHOU W, et al. Lossless frame memory compression using pixel-grain prediction and dynamic order entropy coding for video technology[J]. IEEE Transactions on Circuits Systems for Video Technology, 2016, 26(1): 223-235. doi: 10.1109/TCSVT.2015.2469572.
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出版历程
  • 收稿日期:  2017-04-07
  • 修回日期:  2017-07-07
  • 刊出日期:  2018-01-19

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