摘要
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.
出处
《中国图象图形学报》
CSCD
北大核心
2013年第12期1551-1558,共8页
Journal of Image and Graphics
基金
国家自然科学基金项目(41271422)
辽宁省自然科学基金项目(20102123)
计算机软件新技术国家重点实验室开放基金项目(KFKT2011B11
KFKT2011B09)
南京邮电大学图像处理与图像通信江苏省重点实验室开放基金项目(LBEK2010003
LBEK20110001)
智能计算与信息处理教育部重点实验室(湘潭大学)开放课题(2011ICIP06)
关键词
小波子带
最优方向
压缩感知
稀疏表示
图像编解码
wavelet sub-band
optimum direction
compressed sensing
sparse representation
image codec