混合观测压缩感知图像多描述编码

Compressive sensing multiple description image coding with hybrid sampling

提出一种混合观测压缩感知多描述编码方案,用于提升传统的该类编码系统的编码性能并保留其抗丢包能力。该方案采用二维离散余弦变换(DCT)观测矩阵和高斯矩阵分别对图像信号进行观测,并分别使用哥伦布码(Golomb code)及其改进的编码方案对两种观测系数进行熵编码,得到包含完整码字的二维DCT码流和仅包含部分码字的高斯观测系数码流。在解码端,利用二维DCT系数和高斯观测系数之间的相关性进行最大后验概率估计解码,成功估计出高斯观测系数的缺失码字。最后再将两种观测系数合并,采用1范数优化算法重构出原信号。针对自然图像和遥感图像的实验均表明:不同丢包情况下,用本文提出的编码方案获得的重构图像的峰值信噪比(PSNR)值比传统高斯观测压缩感知编码方案提高了2～4dB,该方案同时还具有鲁棒的抗丢包能力。

A Compressive Sensing（CS） multiple description coding scheme with hybrid sampling was proposed to improve the coding efficiency of the traditional CS coding system and to maintain the abili ty of resisting packet loss. In the scheme, both 2-D Discrete Cosine Transformation（DCT） matrix and sub-Gaussian matrix were used to measure the image signal simultaneously. Then, a Golomb code and its improved version were used to encode for the resulted measurements, respectively. As a re- suit, the 2-D DCT measurement bit streams with complete code words and the Gaussian measurement bit streams with incomplete code words were obtained respectively. In the decoder, these incomplete code words could be decoded successfully with a Maximum A posteriori Probability （MAP） estimator, and the deficient code words could be estimated by the relevance between 2-D DCT and Gaussian measurements. Finally, these decoded measurements were grouped together again to reconstruct the image signal by solving a 1-norm optimization problem. Experimental results on both natural and re mote sensing images show that the Peak Signal to Noise Ratio（PSNRs） proposed method can be superior to that of traditional CS coding scheme loss rates, meanwhile, it has a robust resisting packet loss ability. of the images r by 2~4 dB at econstructed by different packet