基于压缩感知理论的分类量化图像编码方法

摘要

针对矢量量化压缩速度慢、图像复原效果不理想等问题,根据图像小渡分解后高频子带稀疏的特点,提出了一种基于压缩感知( compressed sensing,CS)理论的分类量化图像编码算法.仿真结果表明,与LBG矢量量化编码算法相比,重构图像质量得到极大提升,在相似压缩比下,该算法取得了较好的效果,PSNR平均有1～3dB的明显提高；在相似信噪比(PSNR)下,该算法在图像压缩方面也有很大改进.%For the problem in vector quantization that the compression ratio is slow and image restoration result is not satisfactory, and according to the sparsity properties of the high frequency wavelet transform coefficients, the paper proposed a new method of categories quantization coding for image based on compressed sensing. Compared with the LBG vector quantization coding algorithm, simulation results demonstrate that the proposed algorithm improves the quality of the recovered image significantly. For the similar compression ratio, the PSNR of the proposed algorithm was improved about 1-3 Db. For the similar PSNR,the algorithm in image compression has also improved significantly.