基于压缩感知的图像自适应编码算法

利用压缩感知理论对图像进行固定采样率的压缩并重构时,由于图像各个块的稀疏程度不同,低采样率很难保证图像各块都具有较高的重构质量,而高采样率又会造成资源的浪费.为了解决上述问题,提出了一种基于压缩感知的图像自适应编码算法,该算法首先判断图像各块在DCT域的稀疏度,然后根据判断结果对图像各块进行自适应的压缩采样,从而确保图像在较低采样率下能获得较高的重构质量.实验结果表明,运用所提自适应编码算法在采样率平均值为44%时,重构图像的平均PSNR值可达到35,dB以上,并且重构图像所有块的PSNR值分布比较集中,从而使得图像具有较好的主观质量.

Image Adaptive Coding Algorithm Based on Compressive Sensing

When the image is compressed and reconstructed with compressive sensing theory at the same sampling rate,the low sampling rate can＇t ensure that every block in the image obtains high reconstruction quality and the high sampling rate usually leads to resource waste because of the different sparseness degree for each block in the image.In order to solve the above problem,an image adaptive coding algorithm based on compressive sensing was proposed in this paper.The sparseness degree in DCT domain for each block in the image was estimated first,then the adaptive compression was applied to each block according to the estimated results,thus a relatively high reconstruction quality was acquired for the image at a relatively low sampling rate.Experimental results showed that the proposed adaptive coding algorithm enabled the reconstructed images to obtain average PSNR values above 35,dB at the average sam-pling rate of 44%.Moreover,the PSNR values of all the blocks were quite concentrated,giving the image a better subjective quality.