ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

A Comparative Framework For Blocking Artifacts Removal Of Digital Images Using Hybrid Mechanism


Affiliations
1 Department of Electronics and Communication Engineering, IK Gujral Punjab Technical University, India
2 Department of Electronics and Communication Engineering, DAV Institute of Engineering and Technology, India
     

   Subscribe/Renew Journal


The restoration of an image with blocking artifacts due to compression at low bit rates is a challenging task and blocking artifact measurement algorithms have an important role to play in the computer vision field. An artifacts removal technique is an important step towards the reliability and security of image processing area that delivers a better understanding in many applications like pattern recognition, object classification, surveillance system and many more. We know that the removal of art objects is a scientific method used to provide better image analysis and for this purpose many methods of removal of art objects were already made by researchers during the processing of images such as line, motion, pattern, and hair. But in availability of group of artifacts in an image, they do not achieve an acceptable result. In this research, we proposed a comparative framework for blocking artifacts removal of digital images using hybrid mechanism. The main contribution of this research is developing a new neuro-fuzzy systembased hybrid artifacts removal mechanism to achieve better blocking artifacts efficiency. To remove artifact from an image the proposed framework has its own impact in quality parameters such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity (SSIM) with the execution time. At last, the performance parameters of proposed framework is compare for all five techniques such as line, motion, pattern, hair and combination of all with each other and we observed that the achieved results justify the proposed hybrid artifact removal method in the field of image processing.

Keywords

Artifacts, Line, Motion, Pattern, Hair, Neuro-Fuzzy, Image processing, PSNR, MSE, SSIM, Execution Time
Subscription Login to verify subscription
User
Notifications
Font Size

  • Y. Yang , N.P. Galatsanos and A.K. Katsaggelos, “Projection-Based Spatially Adaptive Reconstruction of Block-Transform Compressed Images”, IEEE Transactions on Image Processing, Vol. 4, No. 7, pp. 896-908, 1995.

  • Y. Yang and N.P. Galatsanos, “Removal of Compression Artifacts using Projections onto Convex Sets and Line Process Modeling”, IEEE Transactions on Image Processing, Vol. 6, No. 10, pp. 1345-1357, 1997.

  • T.P. O’Rourke and R.L. Stevenson, “Improved Image Decompression for Reduced Transform Coding Artifacts”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 5, No. 6, pp. 490-499, 1994.

  • X. Zhang, R. Xiong, S. Ma and G. Wen, “Reducing Blocking Artifacts in Compressed Images via Transform-Domain Non-Local Coefficients Estimation”, Proceedings of IEEE International Conference on Multimedia and Expo, pp. 836841, 2012.

  • D. Gambhir and N. Rajpal, “Fuzzy Edge Detector based Blocking Artifacts Removal of DCT Compressed Images”, Proceedings of International Conference on Circuits, Controls and Communications, pp. 1-6, 2013.

  • D. Gambhir and N. Rajpal, “Image Coding using Fuzzy Edge Classifier and Fuzzy F-Transform: Dualfuzzy”, International Journal of Fuzzy Computation and Modelling, Vol. 1, No. 3, pp. 235-251, 2015.

  • V.T. Manu and B.M. Mehtre, “Blind Technique using Blocking Artifacts and Entropy of Histograms for Image Tampering Detection”, Proceedings of 2nd International Workshop on Pattern Recognition, pp. 1-15, 2017.

  • R. Marsh and M.N. Amin, “Removal of Blocking Artifacts from JPEG-Compressed Images using a Neural Network”, Proceedings of IEEE International Conference on Electro Information Technology, pp. 255-258, 2020.

  • C.H. Yeh, C.H. Lin, M.H., Lin and M.J. Chen, “Deep Learning-Based Compressed Image Artifacts Reduction based on Multi-Scale Image Fusion”, Information Fusion, Vol. 67, pp. 195-207, 2021.

  • Z. Wang, D. Liu, S. Chang, Q. Ling and Y. Yang, “D3: Deep Dual-Domain based Fast Restoration of JPEG-Compressed Images”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2764-2772, 2016.

  • J. Guo and H. Chao, “Building Dual-Domain Representations for Compression Artifacts Reduction”, Proceedings of European Conference on Computer Vision, pp. 628-644, 2016.

  • X. Zhang, W. Yang, Y. Hu and J. Liu, “Dmcnn: DualDomain Multi-Scale Convolutional Neural Network for Compression Artifacts Removal”, Proceedings of IEEE International Conference on Image Processing, pp. 390394, 2018.

  • K. He, X. Zhang, S. Ren and J. Sun, “Deep Residual Learning for Image Recognition”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.

  • F. Shen, R. Gan and G. Zeng, “Weighted Residuals for Very Deep Networks”, Proceedings of International Conference on Systems and Informatics, pp. 936-941, 2016.

  • C. Ledig, L. Theis, F. Huszar and J. Caballero, “PhotoRealistic Single Image Super-Resolution using a Generative Adversarial Network”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681-4690, 2017.

  • B. Lim, S. Son, H. Kim, S. Nah and K. Mu Lee, “Enhanced Deep Residual Networks for Single Image SuperResolution”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136-144, 2017.

  • J. Yu, Y. Fan, J. Yang, N. Xu, Z. Wang, X. Wang and T. Huang, “Wide Activation for Efficient and Accurate Image Super-Resolution”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1-13, 2018.

  • M. Sandler, A. Howard, M. Zhu, A. Zhmoginov and L.C. Chen, “Mobilenetv2: Inverted Residuals and Linear Bottlenecks”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510-4520, 2018.

  • Y. Fan, J. Yu and T.S. Huang, “Wide-Activated Deep Residual Networks based Restoration for BPG-Compressed Images”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2621-2624, 2018.

  • E. Agustsson and R. Timofte , “Challenge on Single Image Super-Resolution: Dataset and Study”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 126-135, 2017.

  • D. Martin, C. Fowlkes, D. Tal and J. Malik, “A Database of Human Segmented Natural Images and Its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics”, Proceedings of the IEEE International Conference on Computer Vision, pp. 416-423, 2001.

  • M. Bevilacqua, A. Roumy, C. Guillemot and M.L. AlberiMorel, “Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding”, Proceedings of the British Conference on Machine Vision, pp. 1-12, 2012.

  • A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. De Vito, Z. Lin, L. Antiga and A. Lerer, “Automatic Differentiation in PYTORCH”, Available at https://openreview.net/forum?id=BJJsrmfCZ, Accessed at 2021.

  • H. Zhao, O. Gallo, I. Frosio and J. Kautz, “Loss Functions for Image Restoration with Neural Networks”, IEEE Transactions on Computational Imaging, Vol. 3, No. 1, pp. 47-57, 2017.

  • D.P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization”, Proceedings of International Conference on nLearning Representations, pp. 1-14, 2015.

  • C. Yim and A.C. Bovik, “Quality Assessment of Deblocked Images”, IEEE Transactions on Image Processing, Vol. 20, No. 1, pp. 88-98, 2011.

  • J. Zhang, R. Xiong, C. Zhao, Y. Zhang, S. Ma and W. Gao, “Concolor: Constrained Non-Convex Low-Rank Model for Image Deblocking”, IEEE Transactions on Image Processing, Vol. 25, No. 3, pp. 1246-1259, 2016.

  • X. Liu, X. Wu, J. Zhou and D. Zhao, “Data-Driven Soft Decoding of Compressed Images in Dual Transform-Pixel Domain”, IEEE Transactions on Image Processing, Vol. 25, No. 4, pp. 1649-1659, 2016.

  • J. Zhang, S. Ma, X. Fan, Y. Zhang and W. Gao, “Reducing Image Compression Artifacts by Structural Sparse Representation and Quantization Constraint Prior”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 27, No. 10, pp. 2057-2071, 2017.

  • C. Chen, Z. Xiong, X. Tian and F. Wu, “Deep Boosting for Image Denoising”, Proceedings of the European Conference on Computer Vision, pp. 3-18, 2018.


Abstract Views: 187

PDF Views: 0




  • A Comparative Framework For Blocking Artifacts Removal Of Digital Images Using Hybrid Mechanism

Abstract Views: 187  |  PDF Views: 0

Authors

Manu Prakram
Department of Electronics and Communication Engineering, IK Gujral Punjab Technical University, India
Amanpreet Singh
Department of Electronics and Communication Engineering, IK Gujral Punjab Technical University, India
Jagroop Singh
Department of Electronics and Communication Engineering, DAV Institute of Engineering and Technology, India

Abstract


The restoration of an image with blocking artifacts due to compression at low bit rates is a challenging task and blocking artifact measurement algorithms have an important role to play in the computer vision field. An artifacts removal technique is an important step towards the reliability and security of image processing area that delivers a better understanding in many applications like pattern recognition, object classification, surveillance system and many more. We know that the removal of art objects is a scientific method used to provide better image analysis and for this purpose many methods of removal of art objects were already made by researchers during the processing of images such as line, motion, pattern, and hair. But in availability of group of artifacts in an image, they do not achieve an acceptable result. In this research, we proposed a comparative framework for blocking artifacts removal of digital images using hybrid mechanism. The main contribution of this research is developing a new neuro-fuzzy systembased hybrid artifacts removal mechanism to achieve better blocking artifacts efficiency. To remove artifact from an image the proposed framework has its own impact in quality parameters such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity (SSIM) with the execution time. At last, the performance parameters of proposed framework is compare for all five techniques such as line, motion, pattern, hair and combination of all with each other and we observed that the achieved results justify the proposed hybrid artifact removal method in the field of image processing.

Keywords


Artifacts, Line, Motion, Pattern, Hair, Neuro-Fuzzy, Image processing, PSNR, MSE, SSIM, Execution Time

References