• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of visual communication & image representation >Image representation using block compressive sensing for compression applications
【24h】

Image representation using block compressive sensing for compression applications

机译：使用块压缩感测进行压缩应用的图像表示

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The emerging compressive sensing (CS) theory has pointed us a promising way of developing novel efficient data compression techniques, although it is proposed with original intention to achieve dimension-reduced sampling for saving data sampling cost. However, the non-adaptive projection representation for the natural images by conventional CS (CCS) framework may lead to an inefficient compression performance when comparing to the classical image compression standards such as JPEG and JPEG 2000. In this paper, two simple methods are investigated for the block CS (BCS) with discrete cosine transform (DCT) based image representation for compression applications. One is called coefficient random permutation (CRP), and the other is termed adaptive sampling (AS). The CRP method can be effective in balancing the sparsity of sampled vectors in DCT domain of image, and then in improving the CS sampling efficiency. The AS is achieved by designing an adaptive measurement matrix used in CS based on the energy distribution characteristics of image in DCT domain, which has a good effect in enhancing the CS performance. Experimental results demonstrate that our proposed methods are efficacious in reducing the dimension of the BCS-based image representation and/or improving the recovered image quality. The proposed BCS based image representation scheme could be an efficient alternative for applications of encrypted image compression and/or robust image compression.
机译：新兴的压缩感测（CS）理论为我们提供了一种开发新颖有效的数据压缩技术的有前途的方法，尽管其初衷是为了实现降维采样以节省数据采样成本而提出的。但是，与经典的图像压缩标准（例如JPEG和JPEG 2000）相比，常规CS（CCS）框架对自然图像的非自适应投影表示可能导致效率低下。本文研究了两种简单的方法用于具有压缩应用的基于离散余弦变换（DCT）的图像表示的块CS（BCS）。一种称为系数随机置换（CRP），另一种称为自适应采样（AS）。 CRP方法可以有效地平衡图像DCT域中采样向量的稀疏性，从而提高CS采样效率。通过基于DCT域中图像的能量分布特性设计CS中使用的自适应测量矩阵来实现AS，这对增强CS性能具有很好的效果。实验结果表明，我们提出的方法在减小基于BCS的图像表示的尺寸和/或提高恢复的图像质量方面是有效的。所提出的基于BCS的图像表示方案可以是加密图像压缩和/或鲁棒图像压缩的应用的有效替代。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号