基于分块压缩感知与平均分组的图像多描述编码

Image Multi-Description Coding Method Based on Equally Grouping and Block Compressive Sensing Strategy

提出了一种基于压缩感知理论的图像多描述编码的方法。该方法在编码端利用分块压缩感知技术对图像进行随机测量,将得到的测量结果矩阵按行平分形成多个描述;在解码端,利用所收到的若干描述重建出相应的图像,且收到的描述个数越多,重建图像的质量越好。直接对测量后的矩阵进行行分割,可以随意产生多个描述,同时也使得编码端的计算复杂度大大降低。实验表明,在相同条件下,本文方法的编码时间更短,图像的重构质量也明显优于其他多描述编码的方法。

An image multiple description coding method is proposed based on compressive sensing theory. It measures the image by block compressive sensing technology on the encoding side, and then divides the result matrix into many descriptions by grouping equally in the rows. In the decoding side, it reconstructs the image by received descriptions. The more the descriptions are received, the better the quality of the reconstructed image is. Dividing the result matrix into groups directly can generate many descriptions easily and reduce computational complexity on the encoding side. Experimental results show that, in the same experimental conditions, the proposed method exhibits faster encoding speed and higher reconstruction image quality than other methods.