基于局部DCT系数的图像压缩感知编码与重构

引入了压缩感知(Compressed sensing, CS)理论, 给出了在获取局部二维 离散余弦变换(Discrete cosine transform, DCT)系数的基础上高质量地编码与重构图像的新方法. 研究了在无量化和有量化情况下, 基于局部DCT系数的图像CS最小全变差重构算法. 在对DCT系数进行量化的过程中得到含噪的局部DCT系数, 在此基础上设计了能完成CS重构的图像编解码一般流程, 并构建了实际应用系统. 实验结果表明, 对于稀疏性较强的图像, 在图像编解码系统中结合CS理论与方法能得到高质量的重构图像, 与传统的直接反离散余弦变换(Inverse DCT, IDCT)方法相比, 峰值信噪比(Peak signal to noise ratio, PSNR)最大能提高5dB以上, 对于一般图像, PSNR也有较大提高.

Image Coding and Reconstruction via Compressed Sensing Based on Partial DCT Coefficients

This paper introduced the CS(compressed sensing)theory and proposed a new method to encode and re- construct images after acquiring the partial two-dimensional DCT(discrete cosine transform)coefficients.The CS total variation reconstruction algorithms based on partial DCT coefficients with or without quantization were studied in this paper.In the quantization step,partial noisy DCT coefficients were obtained,based on which the general image cod- ing/decoding process able to realize the CS reconstruction was...