| 引用本文: | 张力戈,秦小林,杨涌,黄东.结合二进制烟花算法的单位图块截断编码[J].哈尔滨工业大学学报,2020,52(5):82.DOI:10.11918/201909075 |
| ZHANG Lige,QIN Xiaolin,YANG Yong,HUANG Dong.Single bitmap block truncation coding using binary fireworks algorithm[J].Journal of Harbin Institute of Technology,2020,52(5):82.DOI:10.11918/201909075 |
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| 结合二进制烟花算法的单位图块截断编码 |
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张力戈1,2,秦小林1,2,杨涌1,2,黄东3,4
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(1.中国科学院 成都计算机应用研究所,成都 610041; 2.中国科学院大学,北京 100049; 3.重庆机电职业技术大学,重庆 400036; 4.贵州大学,贵阳 550025)
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| 摘要: |
| 图像压缩应用领域中,在保证压缩比不变的前提下,为生成有效的公共位图,并降低单位图块截断编码压缩彩色图像时的失真风险,本文提出了一种结合二进制烟花算法的单位图块截断编码方法.该方法首先将彩色图像分成不重叠的子图像块,使用权重平面法生成每个子图像块的初始位图；然后,通过局部优化和全局优化两种不同的策略确定每个子图像块初始位图需要优化的位置,将这些位置的值作为烟花算法初始值；在此基础上将烟花算法改为二进制形式并进行优化,生成每个子图像块的公共位图与6个量化值；最后,根据公共位图与6个量化值恢复每个子图像块,根据恢复的子图像块重构压缩图像.通过在测试图像上进行实验验证,并与3种参考方法从压缩图片的细节视觉效果、压缩图片与原图间的均方误差、结构相似性指数3个角度进行对比,结果表明,本文所提方法生成的公共位图有效,且全局优化策略压缩图像效果优于局部优化策略,其中全局优化策略生成的压缩图像与原图间的均方误差均值在分块大小为4×4和8×8时分别为56.939 7与106.317 4,低于3种对比方法,结构相似性指数均值分别为0.968 2与0.943 1,高于3种对比方法,通过分析对比表明,所提方法生成的压缩图像与原图间的相似度更高,在保持压缩比的同时有效提升了压缩图像的精度. |
| 关键词: 图像压缩 块截断编码 公共位图 二进制烟花算法 优化算法 |
| DOI：10.11918/201909075 |
| 分类号:TP391 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(61402537)； 四川省科技计划(2018GZDZX1,9ZDZX5,9ZDZX0006)； 重庆市教委科研重点项目(KJZD-K201803701) |
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| Single bitmap block truncation coding using binary fireworks algorithm |
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ZHANG Lige1,2,QIN Xiaolin1,2,YANG Yong1,2,HUANG Dong3,4
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(1.Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu 610041, China; 2.University of Chinese Academy of Sciences, Beijing 100049, China; 3.Chongqing Vocational and Technical University of Mechatronics, Chongqing 400036, China; 4.Guizhou University, Guiyang 550025, China)
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| Abstract: |
| In the application of image compression, to generate effective common bitmap and reduce the distortion risk of color image compressed by single bitmap block truncation coding while remaining compression ratio, a single bitmap block truncation coding method based on binary fireworks algorithm is proposed. First, the color image was divided into non-overlapping blocks, and the initial bitmap of each block was generated by the weight plane method. Then, the positions that need to be optimized in the initial bitmap of each sub-image block were determined by two different strategies, and the values of these positions were used as the initial values of the fireworks algorithm. Next, the fireworks algorithm was changed to binary form and optimized to generate a common bitmap and six quantization values for each block. Finally, each block was restored according to the common bitmap and the quantization values, and the color image was reconstructed by the restored blocks. Through the experiments on the test images, the proposed method was compared with three reference methods from the aspects of the detailed visual effects of the compressed images, the mean square error, and the structural similarity between the compressed images and the original images. Results show that the common bitmap generated by the proposed method was effective, and the global optimization strategy was better than the local optimization strategy. The mean values of the mean square errors between the compressed images generated by the global optimization strategy and the original images were 56.939 7 and 106.317 4 when the block size was 4×4 and 8×8, which were lower than those of the three reference methods. The mean values of the structural similarity index values between the compressed images generated by the global optimization strategy and the original images were 0.968 2 and 0.943 1 when the block size was 4×4 and 8×8, which were higher than those of the three reference methods. It indicates that the similarity between the compressed images generated by the proposed method and the original images is higher, and the accuracy of the compressed images is effectively improved while maintaining the compression ratio. |
| Key words: image compression block truncation coding common bitmap binary fireworks algorithm optimization algorithm |
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