 Research
 Open Access
 Published:
Compression artifacts reduction by improved generative adversarial networks
EURASIP Journal on Image and Video Processing volume 2019, Article number: 62 (2019)
Abstract
In this paper, we propose an improved generative adversarial network (GAN) for image compression artifacts reduction task (artifacts reduction by GANs, ARGAN). The lossy compression leads to quite complicated compression artifacts, especially blocking artifacts and ringing effects. To handle this problem, we choose generative adversarial networks as an effective solution to reduce diverse compression artifacts. The structure of “UNET” style is adopted as the generative network in the GAN. A discriminator network is designed in a convolutional manner to differentiate the restored images from the ground truth distribution. This approach can help improve the performance because the adversarial loss aggressively encourages the output image to be close to the distribution of the ground truth. Our method not only learns an endtoend mapping from input degraded image to corresponding restored image, but also learns a loss function to train this mapping. Benefit from the improved GANs, we can achieve desired results without handengineering the loss functions. The experiments show that our method achieves better performance than the stateoftheart methods.
Introduction
Image restoration technology has become one of the most important applications in computer vision and computer graphics and attracted increasing attention in the field of digital image processing, such as image haze removal [1], image superresolution [2,3,4], image deblur [5, 6], and image understanding [7]. Image compression artifacts reduction aims at recovering a sharp image from the degraded image which is formed by JPEG compression or other causes. JPEG compression is a kind of lossy compression method that uses inaccurate approximations for representing the encoded content. Although JPEG compression is very common in our daily life, it may lead to quite complicated compression artifacts, especially blocking artifacts and ringing effects which not only decrease the perceptual visual quality, but also introduce obstruction to other lowlevel image processing routines.
In this paper, we use a deep learningbased approach for image compression artifacts reduction. More specifically, we propose a principled and efficient generative adversarial network (GAN) for this task. We denote the proposed networks as artifacts reduction by GANs (ARGAN) which was inspired from the GANs [8]. Similar to the standard GANs, ARGAN also consists of two feedforward convolutional neural networks (CNNs), the generative network G and the discriminative network D. The purpose of the generative network G is to generate reasonable results from the input degraded images. The goal of the discriminative network D is to discover the discrepancy between the generated image and the corresponding groundtruth image. Our proposed method differs from the existing traditional [9] or other deep learningbased approaches [10]. The traditional approaches need to extract the features of the images manually. The deep learningbased approaches are usually based on CNN. We are the first to use (GANs) for image compression artifacts reduction.
There are two main contributions in our work:

(1)
We are the first to use an endtoend generative adversarial network (GAN) for image compression artifacts reduction. The experiments show that our method achieves better performance than the stateoftheart methods [9,10,11]. In this paper, we focus on the restoration of the luminance channel (in YCrCb space) as in [10], and the network is specially designed for this task.

(2)
We demonstrate that generative adversarial networks are useful in the image compression artifacts reduction task and can achieve better quality than the traditional or other deep learningbased methods. Our method directly learns an endtoend mapping which can effectively estimate the reasonable results from input degraded images and make the restored image more real.
Related works
Various methods have been proposed to reduce image compression artifacts. Early works, such as sparsitybased image restoration approaches [12, 13], are proposed to produce sharpened images. Now in the field of image compression artifacts reduction, the existing popular methods can be roughly divided into two categories including deblockingoriented and deep learningbased approaches. The goal of the deblockingoriented algorithms is to eliminate ringing and blocking artifacts. Shapeadaptive discrete cosine transform (SADCT) [9] is widely considered as the stateoftheart deblockingoriented algorithm, but like many other deblockingoriented algorithms, it may produce vague effects and is not able to maintain the sharp edges of the original images. Recently, Wu [14] proposed wavelet transform for blocking artifact reduction based on Meyer algorithm.
Neural networks and deep learning currently provide the best solutions to many problems [10, 11, 15,16,17]. In recent years, deep learning has been increasingly improved in its ability to provide accurate recognition and prediction. The image compression artifacts reduction technology thus made a breakthrough making use of the recent progresses in deep learning. Dong et al. [10] applied deep learning to the task of image restoration, where a simple model consisting of four convolution layers was designed for image compression artifacts reduction. With the improvement in GPU performance and optimization algorithms, researchers started to train larger and deeper neural networks.
Wang et al. [11] proposed a Deep DualDomain based fast restoration model to remove artifacts of JPEGcompressed images. It leverages the large learning capacity of deep networks. Extensive experiments verify the superiority of the proposed D3 model over several stateoftheart methods. Soon after, Wang et al. [15] proposed an intensityguided CNN (IGNet) model, which learns an endtoend mapping between the intensity image and distorted depth map to the uncompressed depth map.
Convolutional neural networks are one of the most important methods in deep learning, which are widely used in the field of computer vision. In recent years, the convolutional neural networks (CNNs) are trained in the supervised manner for various imagerelated tasks, such as object detection [18, 19] By penalizing the discrepancy between the output image and groundtruth image, optimal CNNs can be trained to discover the mapping from the input image to the reasonable output image. These various CNN models mainly differ in the network construction and loss function design. One of the most straightforward methods is to pixelwisely evaluate the output images [2, 3, 10], e.g., using L2 (or L1) norm to calculate the distance between the output and groundtruth images in the pixel space. However, this method may generate blurred results which could make the output images look unsatisfactory.
Fortunately, there are a large body of successful applications based on generative adversarial networks (GANs) (e.g., SRGAN [4], DCGAN [20], Pix2Pix [21]) since Goodfellow et al. [8] first officially proposed GANs in 2014. GANs perform an adversarial process alternating between identifying and faking, and the generative adversarial losses are formulated to evaluate the discrepancy between the generated distribution and the real data distribution. A lot of researches show that generative adversarial losses are beneficial for generating more “realistic” images. Inspired by the success of generative adversarial networks (GANs) on imagetoimage translation [21, 22], we designed an efficient GAN network for compression artifacts reduction. In this paper, we show that the proposed networks are effective on our image task. Experiments show that our method outperforms current stateoftheart methods [9,10,11] both perceptually and quantitatively.
Method
In this section, we will introduce the proposed generative adversarial networks for image compression artifacts reduction. First, the generative adversarial losses of ARGAN are described. Then, an overview of the proposed method and the details of our networks are illustrated.
Generative adversarial loss
GANbased models have been widely used in learning generative model due to their success in image generation. GAN was proposed to solve the disadvantages of other generative models. Instead of maximizing the possibility, GAN introduces the theory of adversarial learning between the generator and the discriminator. This adversarial process gives GAN obvious advantages over the other generative models. Moreover, GAN can sample the generated data in a simple way unlike other models in which the sampling is computationally slow and not accurate. For these advantages, GAN gained our attention, and this is the original intention for us to use the framework of GAN. We therefore adapt the GAN learning strategy to tackle the problem of image compression artifacts. More specifically, the proposed ARGAN consists of two feedforward convolutional neural networks (CNNs): the generative network G and the discriminative network D. The reason why we use CNN is that it can greatly stabilize GAN training. ARGAN suggests an architecture guideline in which the generator is composed of a CNN and a transposed CNN, and the discriminator is composed of a CNN with an output dimension 1. Batch normalization, rectified linear unit (ReLU) and leaky rectified linear unit (LeakyReLU) activation functions are utilized for the generator and the discriminator to help stabilize the GAN training. The purpose of the generative network G is to generate reasonable result G(x) from input image x. Meanwhile, each input image x has a corresponding groundtruth image y. G(x) is encouraged to have the same data distribution with the groundtruth image y. The goal of the discriminative network D is to discover the discrepancy between the data distribution of generated image and the corresponding groundtruth image. G and D compete with each other to achieve their respective purposes, thus generating the term adversarial. The generative adversarial loss can be expressed as:
The loss function is a binary cross entropy function that is commonly used in binary classification problems. Where in Eq. 1, x is the input degraded image which has a corresponding groundtruth image y. G tries to minimize the loss, whereas the adversarial D tries to maximize it.
Some recent works have found that it is desirable to mix the generative adversarial loss with other traditional loss, such as L1 [21] or L2 [23] distance. We also consider this trick, but here, L2 distance is adopted rather than L1 distance because L2 distance encourages G to explore the mapping from the input image to its ground truth and therefore make images more realistic:
Above all, the loss function of generative network L_{G} and the loss function of discriminative network L_{D} are formally defined as:
The purpose of the discriminative network D is to distinguish real or fake data. From D’s perspective, if a sample comes from real data, D will maximize its output. While, if a sample comes from G, D will minimize its output. Thus, the overall aim is to minimize Eq. 4. Simultaneously, G wants to confuse D, so it tries to maximize D’s output when a fake sample is presented to D, that is, to minimize Eq. 3 where x is the inputdegraded image whose corresponding groundtruth image is y and λ is the hyper parameter.
Architecture of networks
The architecture of the proposed ARGAN is based on two deep convolutional neural networks, namely the generative network G and discriminative network D, whose combined efforts aim at obtaining a sharp image for a given input image. Figure 1 shows the architecture of the proposed ARGAN.
Generative network
The generative network G is designed for generating a sharp image by reducing the image compression artifacts given the input image. The structure of generative network is inspired by the configuration of “UNet” [24] which is an encoderdecoder with skip connections between mirrored layers in the encoder and decoder stacks. This kind of structure allows the same size of the input and output image, and the local and global information of the image can be taken into account at the same time. This is the reason why we adopted the structure of “UNET” as the generative network in the ARGAN. The network G firstly encodes the input image into highdimensional representation, utilizing a stack of convolutionbatch normalizationLeakyReLU layers, and then the rest of deconvolutionbatch normalizationReLU layers will decode the output image. The details of the generative network G are demonstrated in the Table 1.
Discriminative network
The discriminative network D is proposed to compute the discrepancy between the data distribution of the generated images and the groundtruth images. The output of the discriminative network D represents the possibility that the input image comes from the realworld dataset (true) rather than from the generative network (fake). All the convolution layers use LeakyReLU activations, with the exception of the final layer, which adopts a sigmoid activation. The details of the network D are listed in the Table 2.
Results and discussion experiments
Experiment settings
We use the VOC2012 dataset [25], which includes 16,700 images, as our training set. We trained our network models (in 1,500,000 iterations with a batch size of 64) on one NVIDIA GTX970 GPU using Pytorch. The weights of the networks are trained from scratch. Training images are random cropped into 64 × 64 subimages during each batch. In the training phase, we follow [21] and use an Adam solver with a learning rate of 0.0002 and an initial momentum of 0.5. After one update of the discriminative network D, the generative network G will also be updated one time.
We compare our method with several stateoftheart algorithms, including the deblocking oriented method SADCT [9] and the deep model ARCNN [10], D3 [11], on restoring JPEGcompressed images. Following [8], we adopt standard JPEG compression and set JPEG quality q = 10, 20, 30, 40 (from low quality to high quality) in JPEG encoder. With the test datasets LIVE1 [26] and the 5 test images in [9], the PSNR, SSIM [27] measurements are applied for quality assessment. In this paper, our method is only applied on the luminance channel as same as [10] (Y channel in YcbCr color space), and PSNR is evaluated on the Y channel. The PSNR can be defined as:
where I is the groundtruth image and K is the restored image. The size of the I and K are both m × n, and the MAX_{I} is the gray level of the image. Generally speaking, the better the image quality, the larger the value of PSNR.
The SSIM can be defined as:
where I is the groundtruth image and K is the restored image and μ_{I} and μ_{K} are the average value of I and K, respectively. σ_{I} and σ_{K} are the standard deviation of I and K, respectively. σ_{IK} is the covariance, and c_{1} and c_{2} are constants. When I = K, the SSIM usually approximately to be 1.
Intuitive visual comparison
Figure 2 gives the comparative results of our method and the stateoftheart algorithm ARCNN [10]. As shown in Fig. 2, our method obtains the highest image recovery quality (q = 10) and the best PSNR and SSIM scores.
Quantitative comparisons
In order to prove the efficiency of our proposed method, we compare ARGAN with three methods: SADCT [9], which is widely considered as the stateoftheart deblockingoriented algorithm; ARCNN [10], an efficient deep learningbased method for image compression artifacts reduction; D3 (Deep DualDomain based fast restoration) model [11]. Using the exactly the same dataset, we directly use the results of ARCNN in the original paper. We trained and tested D3 model according to their paper [11]. As shown in Table 3, our method always yields the highest scores. The results show that the results of the proposed ARGAN are superior to the other algorithms. We have also conducted an evaluation on five test images used in [9]. The results also show that ARGAN achieves the highest performance. The results are listed in Table 4.
Discussion
We have presented corresponding experimental results in the above section. The proposed method is compared with several state of the arts, e.g., JPEG, SADCT [9], and the deep model ARCNN [10], D3 (Deep DualDomain based fast restoration) model [11]. We can see that the proposed method is extremely effective in dealing with various compression artifacts.
Subjectively speaking, the performance improvement owes to the following aspects. Firstly, we modified the generative model to be the “UNet” [24] instead of the standard CNN within the inner structure of GANs. Accordingly, utilizing a stack of convolutionbatch normalizationLeakyReLU layers makes the model more effective.
This paper provides the research theory for the image compression artifacts reduction by modified GANs. Even with complicated working conditions on the scene, the performance is improved consistently and greatly. Moreover, we designed a customized network to further improve the PSNR and SSIM. In the further work, we hope to reduce the computational load and increase the efficiency of the model. A structureoptimized GANs can improved neural network and may help us to solve this problem. This issue will be our future work.
Conclusion
In this paper, image compression artifacts reduction is achieved by generative adversarial networks, and we make sufficient comparisons with SADCT [9], ARCNN [10], and D3 [11], respectively. The results show that the proposed ARGAN is effective in removing various compression artifacts. The detail information maintains better, making the images look clearer.
Abbreviations
 ARGAN:

Artifacts reduction by GANs
 CNNs:

Convolutional neural networks
 GANs:

Generative adversarial networks
 PSNR:

Peak signaltonoise ratio
 SSIM:

Structural similarity index
References
 1.
K. He, J. Sun, X. Tang, Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)
 2.
C. Dong, C.C. Loy, K. He, et al., Image superresolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2014)
 3.
C. Dong, C.L. Chen, X. Tang, in European Conference on Computer Vision. Accelerating the superresolution convolutional neural network (Springer, Cham, 2016a), pp. 391–407
 4.
Ledig C, Wang Z, Shi W, et al. PhotoRealistic Single Image SuperResolution Using a Generative Adversarial Network 2016 arXiv.org.
 5.
J. Sun, W. Cao, Z. Xu, et al., Learning a Convolutional Neural Network for NonUniform Motion Blur Removal (IEEE Conference on Computer Vision & Pattern Recognition, 2015), pp. 769–777
 6.
C.J. Schuler, M. Hirsch, S. Harmeling, et al., Learning to deblur. IEEE Trans. Pattern Anal. Mach. Intell. 38(7), 1439–1451 (2016)
 7.
C. Yan, L. Li, C. Zhang, et al., Crossmodality bridging and knowledge transferring for image understanding. IEEE Trans. Multimedia (2019) https://doi.org/10.1109/TMM.2019.2903448
 8.
I.J. Goodfellow, J. PougetAbadie, M. Mirza, et al., Generative adversarial networks. Adv. Neural Inf. Proces. Syst. 3, 2672–2680 (2014)
 9.
A. Foi, V. Katkovnik, K. Egiazarian, Pointwise shapeadaptive DCT for highquality denoising and deblocking of grayscale and color images. IEEE Trans. Image Process. 16(5), 1395 (2007)
 10.
C. Dong, Y. Deng, C.L. Chen, et al., in IEEE International Conference on Computer Vision. Compression artifacts reduction by a deep convolutional network (IEEE International Conference on Computer Vision, 2016), pp. 576–584
 11.
Z. Wang, L. Ding, S. Chang, et al., in IEEE Conference on Computer Vision & Pattern Recognition. D3: Deep dualdomain based fast restoration of JPEGcompressed images (2016)
 12.
H. Chang, M.K. Ng, T. Zeng, Reducing artifacts in JPEG decompression via a learned dictionary. IEEE Trans. Signal Process. 62(3), 718–728 (2014)
 13.
R. Rothe, R. Timofte, L. Van, in IEEE International Conference on Image Processing. Efficient regression priors for reducing image compression artifacts (IEEE International Conference on Image Processing, 2015), pp. 769777
 14.
M.T. Wu, Wavelet transform based on Meyer algorithm for image edge and blocking artifact reduction. Inf. Sci. 474, 125–135 (2019)
 15.
X. Wang, P. Zhang, Y. Zhang, et al., Deep Intensity Guidance Based Compression Artifacts Reduction for Depth Map. J. Vis. Commun. Image Represent. 57, 234242 (2018)
 16.
R. Shan, Z.S. Zhao, P.F. Chen, W.J. Liu, S.Y. Xiao, Y.H. Hou, Z. Wang, Network modeling and assessment of ecosystem health by a multipopulation swarm optimized neural network ensemble. Appl. Sci. 6, 175 (2016) doi:10.3390
 17.
Z.G. Wang, Z.S. Zhao, C.S. Zhang, Incremental multiple instance outlier detection. Neural Comput. & Applic. 26(4), 957–968 (2015)
 18.
K. He, G. Gkioxari, P. Dollar, et al., Mask RCNN. IEEE Trans. Pattern Anal. Mach. Intell. (2017) https://doi.org/10.1109/TPAMI.2018.2844175
 19.
Y. Chenggang, X. Hongtao, C. Jianjun, et al., An effective Uyghur text detector for complex background images. IEEE Trans. Multimedia. 20(12), 33893398 (2018) https://doi.org/10.1109/TMM.2018.2838320
 20.
A. Radford, L. Metz, S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks. Comput. Sci. (2015) https://arxiv.org/pdf/1511.06434.pdf
 21.
P. Isola, J. Zhu, T. Zhou, A.A. Efros, Imagetoimage translation with conditional adversarial networks, 2017 (IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 2017), pp. 5967–5976
 22.
J.Y. Zhu, T. Park, P. Isola, A.A. Efros, in IEEE International Conference on Computer Vision (ICCV). Unpaired imagetoimage translation using cycleconsistent adversarial networks (2017)
 23.
D. Pathak, P. Krähenbühl, J. Donahue, et al., Context encoders: Feature learning by Inpainting (IEEE Conference on Computer Vision and Pattern Recognition, 2016), pp. 2536–2544
 24.
O. Ronneberger, P. Fischer, T. Brox, in International Conference On Medical Image Computing And ComputerAssisted Intervention. Unet: Convolutional networks for biomedical image segmentation (Springer, Cham, 2015), pp. 234–241
 25.
M. Everingham, L.V. Gool, C.K.I. Williams, et al., The Pascal Visual Object Classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)
 26.
Z. Wang, A.C. Bovik, H.R. Sheikh, et al., Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
 27.
H.R. Sheikh, Z. Wang, L. Cormack, A.C. Bovik, Live Image Quality Assessment Database Release 2 (2005)
Acknowledgments
Not applicable.
Funding
This work was supported in part by the National Natural Science Foundation of China (Grant No. 61403281), the Natural Science Foundation of Shandong Province (ZR2014FM002), China Postdoctoral Science Special Foundation Funded Project (2015T80717), and Youth Teachers’ Growth Plan of Shandong Province.
Availability of data and materials
The dataset used during the current study is VOC2012 dataset [25] and are available online or from the corresponding author on reasonable request.
Author information
Affiliations
Contributions
ZZ was a major contributor in writing the manuscript. And he analyzed and interpreted the entire framework of GANS with the help of ZW and DW. QS, HY, and HQ performed the coding and experiments. All authors read and approved the final manuscript.
Corresponding author
Correspondence to Zengshun Zhao.
Ethics declarations
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
About this article
Received
Accepted
Published
DOI
Keywords
 GANs
 CNN
 Compression artifacts
 JPEG compression