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Relevant bibliographies by topics / Video Compression Artifact Removal, Video Denoising

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Journal articles
Dissertations / Theses
Conference papers

Academic literature on the topic 'Video Compression Artifact Removal, Video Denoising'

Author: Grafiati

Published: 20 February 2023

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Journal articles on the topic "Video Compression Artifact Removal, Video Denoising"

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Gandam, Anudeep, Jagroop Singh Sidhu, Sahil Verma, N. Z. Jhanjhi, Anand Nayyar, Mohamed Abouhawwash, and Yunyoung Nam. "An efficient post-processing adaptive filtering technique to rectifying the flickering effects." PLOS ONE 16, no. 5 (May 10, 2021): e0250959. http://dx.doi.org/10.1371/journal.pone.0250959.

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Compression at a very low bit rate(≤0.5bpp) causes degradation in video frames with standard decoding algorithms like H.261, H.262, H.264, and MPEG-1 and MPEG-4, which itself produces lots of artifacts. This paper focuses on an efficient pre-and post-processing technique (PP-AFT) to address and rectify the problems of quantization error, ringing, blocking artifact, and flickering effect, which significantly degrade the visual quality of video frames. The PP-AFT method differentiates the blocked images or frames using activity function into different regions and developed adaptive filters as per the classified region. The designed process also introduces an adaptive flicker extraction and removal method and a 2-D filter to remove ringing effects in edge regions. The PP-AFT technique is implemented on various videos, and results are compared with different existing techniques using performance metrics like PSNR-B, MSSIM, and GBIM. Simulation results show significant improvement in the subjective quality of different video frames. The proposed method outperforms state-of-the-art de-blocking methods in terms of PSNR-B with average value lying between (0.7–1.9db) while (35.83–47.7%) reduced average GBIM keeping MSSIM values very close to the original sequence statistically 0.978.

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Thakur, Vikrant Singh, Kavita Thakur, and Shubhrata Gupta. "A Novel Type-2 Fuzzy Directed Hybrid Post-Filtering Technique for Efficient JPEG Image Artifact Reduction." DESIGN, CONSTRUCTION, MAINTENANCE 1 (January 11, 2021): 8–17. http://dx.doi.org/10.37394/232022.2021.1.2.

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Image and video compression becomes very popular and intense field in recent decades, due to their fast and quality communication demand. To cope up with the high speed communication demand of image and videos, over the communication channel, higher compression (ie. low bit rate compression) is now preferred at the cost of quality degradation. Currently JPEG and JPEG2000 are the most popularly used codec’s for achieving quality image compression. Practically, on the low bit rate compression, it has been observed that, the JPEG standard compressed images suffer from multifarious visual distortions. To address this problem effectively, this paper proposed an innovative type-2 fuzzy directed hybrid post filtering technique, which is framed to suppress the artifacts generated in JPEG compressed images at low bit rate compression. The proposed technique addresses all three types of JPEG compressed image artifacts: blocking, edges blurring, and aliasing. Furthermore the proposed technique is structured with two stages, to enhance the quality of JPEG compressed images. The first stage, removes blocking artifacts using boundary smoothing and guided filtering techniques. The second stage reduces blurring and aliasing around the edges through type-2 fuzzy directed local edge regeneration. To prove higher efficiency of proposed work, a complete comparative performance evaluation with other existing JPEG artifact removal techniques, has also presented. This extensive performance analysis includes visual quality assessment of post-filtered images along with subjective quality assessment on the basis of, Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). Performance evaluation illustrates that the, proposed approach is efficient, provides PSNR improvement of approximately 1 dB along with higher reduction in MSE values as compared to state of the art algorithms for all the bit-rate compressions.

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Akhtar, Anique, Wen Gao, Li Li, Zhu Li, Wei Jia, and Shan Liu. "Video-based Point Cloud Compression Artifact Removal." IEEE Transactions on Multimedia, 2021, 1. http://dx.doi.org/10.1109/tmm.2021.3090148.

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Dissertations / Theses on the topic "Video Compression Artifact Removal, Video Denoising"

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Zhou, Yi. "Graph-based Mix-out Networks for Video Restoration." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/21487.

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The video restoration task aims to generate clear and high-quality videos from those noisy or blurry low-quality videos. Different from image restoration tasks, the temporal information is an additional data dimension in videos and it plays a key role in video restoration task. Therefore, how to effectively take the full advantage of the inter-frame temporal information is a challenging problem for recovering video quality. Current approaches attempt to either warp the features based on the estimated motion modality before post-processing or fuse the features (e.g. concatenation) only once at a specific place during the process. In the former solution, it relies on the accuracy of estimating motion representation very much, but the existing algorithms for estimating motion have problems of being either too slow or inaccurate. As for the latter solution, too few times (usually only once) of feature fusion will lead to the loss of the intra-frame information after fusion and insufficient utilization of the inter-frame information. Therefore, to fully utilize the inter-frame information, we design a network with the mix-out mechanism. Our Mix-out network can simultaneously refine the inter-frame and intra-frame information instead of fully relying on only one of them. Besides, we devise a novel Graph-based Pixel Associating module (GPA module) to better infer the pixel-wise correlation of adjacency frames, which to our knowledge is the first pixel-wise GCN module that can be practically applied on low-level tasks. Extensive experiments show that our method can outperform the state-of-the-art methods on video compression artifact removal task (codec JPEG2000 and codec HEVC (x265)) by 0.74dB (q=20), 0.83dB (q=40) for codec JPEG2000 and 0.46dB (qp=37), 0.48dB (qp=32) for codec HEVC(x265) in terms of PSNR. Besides, we further extend our method to other video restoration tasks like video super resolution and video Gaussian denoising, and also achieve the state-of-the-art performance.

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Conference papers on the topic "Video Compression Artifact Removal, Video Denoising"

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Ramsook, Darren, Anil Kokaram, Neil Birkbeck, Yeping Su, and Balu Adsumilli. "Perceptually motivated deep neural network for video compression artifact removal." In Applications of Digital Image Processing XLV, edited by Andrew G. Tescher and Touradj Ebrahimi. SPIE, 2022. http://dx.doi.org/10.1117/12.2633552.

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Mameli, Filippo, Marco Bertini, Leonardo Galteri, and Alberto Del Bimbo. "A NoGAN approach for image and video restoration and compression artifact removal." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9413095.

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Ramsook, D., A. Kokaram, N. Birkbeck, Y. Su, and B. Adsumilli. "A Deep Learning post-processor with a perceptual loss function for video compression artifact removal." In 2022 Picture Coding Symposium (PCS). IEEE, 2022. http://dx.doi.org/10.1109/pcs56426.2022.10018047.

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Aqqa, Miloud, and Shishir Shah. "CAR-DCGAN: A Deep Convolutional Generative Adversarial Network for Compression Artifact Removal in Video Surveillance Systems." In 16th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010312304550464.

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Aqqa, Miloud, and Shishir Shah. "CAR-CNN: A Deep Residual Convolutional Neural Network for Compression Artifact Removal in Video Surveillance Systems." In 15th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009184405690575.

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