小波子带最优方向性选择和压缩感知的图像编解码

传统贝叶斯压缩感知方法所存在的局部优化问题导致了重构图像存在着较大的误差。本文提出一种基于小波自适应最优方向选择和压缩感知的图像编码方法，该方法依据图像小波变换的低频子带与高频子带所存在的相关性差异，对低频系数进行基于小波最优方向选择的编解码，以保证图像主要信息的重构质量；而针对高频子带的稀疏特性进行基于压缩感知的随机测量，以保证图像的编码效率。实验结果表明，与传统的压缩感知算法相比，本文所提出算法的解码图像具有更好的主观质量，同时在相同码率下解码图像的PSNR平均提高1~2dB。

Image codec research on the optimum directional selection of wavelet sub-band and compressed sensing

For the problem of large decoding errors generated by the image compression based on Bayesian compressed sensing with local optimization,a new codec uniting the optimal directional selection of wavelet sub-bands with Bayesian compressed sensing is proposed,which reduces the error of the decoded images and increases the accuracy of the codec. According to the correlation differences between the low-frequency and the high-frequency sub-bands,we use a respective codec for each of them. Low-frequency coefficients contain the images' main information. With the purpose of avoiding local optimization,we apply the correlation of the low-frequency sub-band,introducing the optimal direction selection codec to the low-frequency coefficients,and combining Bayesian compressed sensing based on the wavelet transform to deal with high-frequency coefficients containing details of images. The experimental results show that in the case without increasing the bit rate,the decoded images using the proposed method have good subjective quality,and the PSNR and SSIM of images are improved to some extent.