小波子带最优方向性选择和压缩感知的图像编解码

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

针对贝叶斯压缩感知应用到图像编解码中所存在的局部优化导致图像解码误差较大的问题,提出一种小波子带最优方向性选择结合贝叶斯压缩感知的编解码方法,以降低解码误差,进而提高编解码的精确度。根据图像小波变换的高、低频子带系数间所存在的相关性及差异,对其分别进行编解码;为使含图像主要信息的低频系数避免局部优化问题,利用低频系数内部存在的相关性,引入了小波最优方向选择方法对低频系数编解码;并结合基于小波变换的贝叶斯压缩感知对含细节信息的高频系数进行编解码。实验结果表明,该方法在不增加码率的情况下,解码图像具有较好的主观质量,且解码图像的峰值信噪比(PSNR)和结构相似指数度量(SSIM)均有一定程度提高。

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.