Towards Quantized DCT Coefficients Restoration for Compressed Images
Tong Ouyang, Zhenzhong Chen, Shan Ting Liu · 2020
Images and videos suffer from undesirable visual artifacts at high compression ratios, which is due to the use of the discrete cosine transform and scalar quantization in the compression. To restore the quantized coefficients via producing the quantization error, we propose a coefficients restoration convolutional neural network in the frequency domain (FD-CRNet). Taking advantage of residual learning, the proposed FD-CRNet efficiently exploits the related distribution of different frequency components. The squeeze-and-excitation block (SE block) is adopted to reduce the computational complexity and better restoration performance. Experimental results show the quantized coefficients are recovered near the lossless coefficients effectively, which outperforms the existed coefficients restoration methods. In addition, the performance of methods in the spatial domain is significantly improved by FD-CRNet with more authentic details and sharper edges when removing the compression artifacts.