Dmcnn: Dual-Domain Multi-Scale Convolutional Neural Network for Compression Artifacts Removal

JPEG is one of the most commonly used standards among lossy image compression methods. However, JPEG compression inevitably introduces various kinds of artifacts, especially at high compression rates, which could greatly affect the Quality of Experience (QoE). Recently, convolutional neural network...

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Bibliographic Details
Published inProceedings - International Conference on Image Processing pp. 390 - 394
Main Authors Zhang, Xiaoshuai, Yang, Wenhan, Hu, Yueyu, Liu, Jiaying
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2018
Subjects
Compression Artifacts Removal
Convolutional Neural Network
Discrete cosine transforms
Feature extraction
Image coding
Image restoration
JPEG
Quantization (signal)
Training
Transform coding
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Summary:JPEG is one of the most commonly used standards among lossy image compression methods. However, JPEG compression inevitably introduces various kinds of artifacts, especially at high compression rates, which could greatly affect the Quality of Experience (QoE). Recently, convolutional neural network (CNN) based methods have shown excellent performance for removing the JPEG artifacts. Lots of efforts have been made to deepen the CNN s and extract deeper features, while relatively few works pay attention to the receptive field of the network. In this paper, we illustrate that the quality of output images can be significantly improved by enlarging the receptive fields in many cases. One step further, we propose a Dual-domain Multi-scale CNN (DMCNN) to take full advantage of redundancies on both the pixel and DCT domains. Experiments show that DMCNN sets a new state-of-the-art for the task of JPEG artifact removal.
ISSN:2381-8549
DOI:10.1109/ICIP.2018.8451694