Deep convolutional network based image quality enhancement for low bit rate image compression
In this contribution, a novel image quality enhancement algorithm based on convolutional network is proposed for low bit rate image compression. Specifically, a downsample procedure is performed to generate lower resolution image for low bit rate compression. While the decoder side, upsample is to be performed firstly to the original resolution. Image quality is further enhanced by the proposed convolutional deep network. In particular, an optional image quality improvement network can be utilized for further enhancement after the first network. With the help of deep network, more detailed and high-frequency information can be recovered while maintaining the consistency of contour area, leading to better visual quality. Another benefit of this approach lies in that the proposed approach is fully compatible with all third-party image codec pipeline. Experimental result shows that the proposed scheme significantly outperforms JPEG in low bit rate image compression.