Reading: CAR-DRN — Compression Artifacts Reduction based on Dual-Residual Network (JPEG Filtering)
In this story, Compression Artifacts Reduction based on Dual-Residual Network (CAR-DRN), by Nanjing University of Aeronautics and Astronautics, is shortly presented.
- The artifacts such as blocking and ringing are especially sharp at low bitrates.
- In this paper, a novel dual-residual network is proposed to reduce compression artifacts caused by lossy compression codecs.
This is a paper in 2020 Springer Journal of Signal, Image and Video Processing. (Sik-Ho Tsang @ Medium)
- CAR-DRN: Network Architecture
- Experimental Results
1. CAR-DRN: Network Architecture
- There are four parts as well as the residual learning to form CAR-DRN.
- In summary, there are 8 convolutional layers in the whole network.
1.1. Feature Extraction
- Feature extraction For the first layer, we use 64 filters of size 9×9×c where c stands for channels of input images (3 for color images and 1 for gray ones) to generate feature maps, followed by the activation function of leaky Relu.
- with r sets to 5.
1.2. Feature Enhancement
- Firstly, a convolutional layer with 64 filters is used to map the extracted features, followed by a residual block, to increase the nonlinearity.
- The residual block consists of three convolutional layers with batch normalization and leaky Relu activation.
- Then, we use a convolutional layer with 16 filters to generate the enhanced features.
- Dilated convolution, originated in DeepLab and DilatedNet, with rate 2 is applied to boost the receptive field of network. It is of great importance because the network will be capable of taking larger regions into consideration at a moment with relatively bigger receptive field. Dilated convolution makes it possible for the network to expand it without bringing in excessive parameters.
1.3. Nonlinear mapping
- A convolutional layer with 16 filters is taken to transform representations.
- As the last layer of the network, c filters of size 3×3×16 are used to reconstruct output image patches (color or gray) out of high-dimensional representations.
1.5. Residual Learning
- By adding the original inputs to the final outputs, the network is able to learn residuals of inputs and labels rather than directly learn the entire image.
1.6. Loss Function
- Mean squared error (MSE) is used as the loss function.
2. Experimental Results
2.2. PSNR, SSIM, PSNR-B
- The BSDS500 database is used for training.
- As for testing, 31 images are selected from commonly used dataset set5, set12 and set14.
- PSNR, SSIM and PSNR including Blocking artifacts (PSNR-B) are evaluated.
- PSNR-B is a new block-sensitive image quality index designed to measure the blocking artifacts, which takes the gray level discontinuities around block boundaries into consideration.
- CAR-DRN ranks only second to OTORRR in PSNR and SSIM while it achieves the highest scores in PSNR-B compared with all the other networks.
- Besides, it can be inferred that our model works better at extremely low bitrates and the advantage over other methods decreases with larger QF.
- JPEG decoded image is occupied with blocking artifacts and ABF and ARCNN improves PSNR, SSIM and PSNR-B slightly.
- VRCNN removes the blocking artifacts partially while the edges are still blurred to some extent.
- The recently proposed OTORRR achieves best scores in PSNR and SSIM.
- But, CAR-DRN achieves best PSNR-B among all methods.
[2020 JSIVP] [CAR-DRN]
A dual-residual network for JPEG compression artifacts reduction
JPEG [ARCNN] [RED-Net] [DnCNN] [Li ICME’17] [MemNet] [MWCNN] [CAR-DRN]
HEVC [Lin DCC’16] [IFCNN] [VRCNN] [DCAD] [MMS-net] [DRN] [Lee ICCE’18] [DS-CNN] [CNNF] [RHCNN] [VRCNN-ext] [S-CNN & C-CNN] [MLSDRN] [ARTN] [Double-Input CNN] [CNNIF & CNNMC] [B-DRRN] [Residual-VRN] [Liu PCS’19] [DIA_Net] [RRCNN] [QE-CNN] [Jia TIP’19] [EDCNN] [VRCNN-BN] [MACNN]
AVS3 [Lin PCS’19] [CNNLF]
VVC [AResNet] [Lu CVPRW’19] [Wang APSIPA ASC’19] [ADCNN] [PRN] [DRCNN] [Zhang ICME’20] [MGNLF] [RCAN+PRN+]