An End-to-End Compression Framework Based on Convolutional Neural Networks
Traditional image coding standards (such as JPEG and JPEG2000) make the decoded image suffer from many blocking artifacts or noises since the use of big quantization steps. To overcome this problem, we proposed an end-to-end compression framework based on two CNNs, as shown in Figure 1, which produce a compact representation for encoding using a third party coding standard and reconstruct the decoded image, respectively. To make two CNNs effectively collaborate, we develop a unified end-to-end learning framework to simultaneously learn CrCNN and ReCNN such that the compact representation obtained by CrCNN preserves the structural information of the image, which facilitates to accurately reconstruct the decoded image using ReCNN and also makes the proposed compression framework compatible with existing image coding standards.