集成超分辨率重建的图像压缩编码新型框架及其实现

提出一种集成超分辨率重建的图像压缩编码新型框架。在编码端对输入图像以因子2进行下采样，对下采样图像用JPEG标准编解码，而后采用事先通过外部训练库训练得到的字典，对解码后的图像进行基于学习的超分辨率重建。为了进一步提高解码重建图像质量，在算法框架中设计了反馈环节，即在编码端用原始图像减去超分辨率重建图像得到残差辅助图像，在解码端用该残差辅助图像弥补在超分辨率图像重建环节中损失的高频细节信息，在保证残差辅助图像较低编码比特率的情况下，大幅度提高了解码重建图像质量。此外，还实现了框架图像编码控制量化参数的单一化，实用性较强。实验结果表明，算法较JPEG标准在相同峰值信噪比的情况下，编码比特率大幅度降低，压缩倍数提高较多。

New Image Compression Framework with Super-Resolution Technique

In this work, a novel video compression framework with super-resolution technique is proposed. The input image is first down sampled by down sampling factor 2. Then the down sampled image is coded by JPEG standard. A novel hybrid super-resolution (SR) method is applied to decoded down sampled image. Meanwhile, feedback is designed to further improve the quality of final decoded video. Specifically, by original image subtracts super-resolution image is residual assistance image at encoder side. Then, this residual assistance image can be compensated for the loss of high-frequency details in SR process at decoder side. Moreover, only one quantization parameter (QP) to control the quality of coding image is needed for the whole framework. Evaluations have been made in comparison with JPEG standard coding scheme. Experimental results show that proposed coding framework achieves significant bitrate saving and compression ratio increase at similar objective quality levels.