基于DCT域采样和重建的低码率图像压缩算法
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四川大学电子信息学院,四川大学电子信息学院

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TP391.41

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国家自然科学基金项目(面上项目,重点项目,重大项目);四川省科技计划项目;四川省教育厅2014年研究生教育改革创新项目


Low Bit Rate Image Compression via Down-sampling and Reconstruction in DCT
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The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan); The Science and Technology Planning Project of Sichuan Province; The Graduate Education Reform and Innovation Project of Educational Commission of Sichuan Province

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    摘要:

    本文构建了一种基于DCT域采样和超分辨率(Super Resolution, SR)重建的低码率图像压缩编码算法。在编码端对原始图像进行分块离散余弦变换(DCT),并提取每个DCT系数块的低频系数,然后再反变换到空间域,从而得到在DCT域下采样的低分辨率(Low Resolution,LR)图像块。用JPEG标准对下采样图像块编解码后,采用基于学习的方法恢复DCT域高频系数,重建出高分辨率(High Resolution, HR)的图像。实验结果表明,在码率较低的情况下,本文算法比JPEG编码标准具有更好的率失真性能;同时,在相同码率下,本文算法重建的解码图像视觉效果更好。

    Abstract:

    In this paper, an image compression algorithm is presented via combining the Discrete Cosine Transformation (DCT) domain down-sampling and super-resolution reconstruction.Firstly, the image is first divided into non-overlapping blocks, and each block is transformed by DCT. Secondly, low frequency coefficients of each block are extracted and inverse-transformed to generate the down-sampled low resolution image. After the down-sampled image is compressed and decompressed by the standard JPEG ,the high frequency coefficients are recovered by the learning-based method in the spatial domain. Experimental results show that the compression algorithm presented in this paper has better?rate-distortion performance than JPEG at low bit rate, and the visual quality of decompressed image by our method is also better at the same coding bit rate.

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引用本文

吴强,何小海. 基于DCT域采样和重建的低码率图像压缩算法[J]. 科学技术与工程, 2016, 16(5): .
wuqiang and. Low Bit Rate Image Compression via Down-sampling and Reconstruction in DCT[J]. Science Technology and Engineering,2016,16(5).

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历史
  • 收稿日期:2015-10-22
  • 最后修改日期:2015-10-22
  • 录用日期:2015-11-27
  • 在线发布日期: 2016-02-22
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