DPW-SDNet: Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images
This addresses image quality improvement for users of JPEG compression without changing codecs, but it is incremental as it builds on existing deep learning approaches.
The paper tackled the problem of compression artifacts like blocking and blurring in JPEG-compressed images at low bit-rates by developing a dual pixel-wavelet domain deep CNN network, resulting in superior performance over state-of-the-art methods.
JPEG is one of the widely used lossy compression methods. JPEG-compressed images usually suffer from compression artifacts including blocking and blurring, especially at low bit-rates. Soft decoding is an effective solution to improve the quality of compressed images without changing codec or introducing extra coding bits. Inspired by the excellent performance of the deep convolutional neural networks (CNNs) on both low-level and high-level computer vision problems, we develop a dual pixel-wavelet domain deep CNNs-based soft decoding network for JPEG-compressed images, namely DPW-SDNet. The pixel domain deep network takes the four downsampled versions of the compressed image to form a 4-channel input and outputs a pixel domain prediction, while the wavelet domain deep network uses the 1-level discrete wavelet transformation (DWT) coefficients to form a 4-channel input to produce a DWT domain prediction. The pixel domain and wavelet domain estimates are combined to generate the final soft decoded result. Experimental results demonstrate the superiority of the proposed DPW-SDNet over several state-of-the-art compression artifacts reduction algorithms.