Deep learning-based video quality enhancement for the new versatile video coding

Bouaafia, Soulef; Khemiri, Randa; Messaoud, Seifeddine; Ahmed, Olfa Ben; Sayadi, Fatma Ezahra

doi:10.1007/s00521-021-06491-9

Cited by 18 publications

(5 citation statements)

References 36 publications

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“…By presenting this algorithm, the authors enhanced the coding performance of HEVC. Soulef Bouaafia et al [31] had presented a wide-activated squeeze-and-excitation deep CNN to enhance the quality for versatile video coding. The authors had enhanced the performance in terms of RD cost.…”

Section: Related Workmentioning

confidence: 99%

Effective and Efficient Video Compression by the Deep Learning Techniques

Panneerselvam¹,

Mahesh²,

Josephine³

et al. 2023

Computer Systems Science and Engineering

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Deep learning has reached many successes in Video Processing. Video has become a growing important part of our daily digital interactions. The advancement of better resolution content and the large volume offers serious challenges to the goal of receiving, distributing, compressing and revealing highquality video content. In this paper we propose a novel Effective and Efficient video compression by the Deep Learning framework based on the flask, which creatively combines the Deep Learning Techniques on Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN). The video compression method involves the layers are divided into different groups for data processing, using CNN to remove the duplicate frames, repeating the single image instead of the duplicate images by recognizing and detecting minute changes using GAN and recorded with Long Short-Term Memory (LSTM). Instead of the complete image, the small changes generated using GAN are substituted, which helps with frame-level compression. Pixel wise comparison is performed using K-nearest Neighbours (KNN) over the frame, clustered with K-means and Singular Value Decomposition (SVD) is applied for every frame in the video for all three colour channels [Red, Green, Blue] to decrease the dimension of the utility matrix [R, G, B] by extracting its latent factors. Video frames are packed with parameters with the aid of a codec and converted to video format and the results are compared with the original video. Repeated experiments on several videos with different sizes, duration, Frames per second (FPS), and quality results demonstrated a significant resampling rate. On normal, the outcome delivered had around a 10% deviation in quality and over half in size when contrasted, and the original video.

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Section: Related Workmentioning

confidence: 99%

Effective and Efficient Video Compression by the Deep Learning Techniques

Panneerselvam¹,

Mahesh²,

Josephine³

et al. 2023

Computer Systems Science and Engineering

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“…ML-based solutions are proposed for HTTP adaptive streaming [14], and a reinforcement learning framework is introduced for frame-level bit allocation in HEVC/H.265 [15]. A deep convolutional neural network (DCNN) is employed for enhancing video quality in versatile video coding (VVC) [16], and human vision models and ML are leveraged for H.266/VVC encoding [17]. Deep learning is used for video streaming over the next-generation network,…”

Section: ░ 2 Related Researchmentioning

confidence: 99%

Adaptive Video Coding Framework with Spatial-Temporal Fusion for Optimized Streaming in Next-Generation Networks

Charles,

Khan,

Rao

2023

IJEER

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Predicting future frames and improving inter-frame prediction are ongoing challenges in the field of video streaming. By creating a novel framework called STreamNet (Spatial-Temporal Video Coding), fusing bidirectional long short-term memory with temporal convolutional networks, this work aims to address the issue at hand. The development of STreamNet, which combines spatial hierarchies with local and global temporal dependencies in a seamless manner, along with sophisticated preprocessing, attention mechanisms, residual learning, and effective compression techniques, is the main contribution. Significantly, STreamNet claims to provide improved video coding quality and efficiency, making it suitable for next-generation networks. STreamNet has the potential to provide reliable and optimal streaming in high-demand network environments, as shown by preliminary tests that show a performance advantage over existing methods.

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“…Overall, the proportion of QT splitting is minimal. In summary, if QT partitioning is only predicted early, the reduction in coding complexity is limited [25]. Therefore, in this paper, the FSVM classifier and DAG-SVM classifier are used to determine whether CUs will continue to be divided, as well as the direction of division, which can effectively reduce coding complexity.…”

Section: Statistical Analysis Of Qtmt Division Structurementioning

confidence: 99%

FSVM- and DAG-SVM-Based Fast CU-Partitioning Algorithm for VVC Intra-Coding

Wang

Zhang

2023

Symmetry

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H.266/VVC introduces the QTMT partitioning structure, building upon the foundation laid by H.265/HEVC, which makes the partitioning more diverse and flexible but also brings huge coding complexity. To better address the problem, we propose a fast CU decision algorithm based on FSVMs and DAG-SVMs to reduce encoding time. The algorithm divides the CU-partitioning process into two stages and symmetrically extracts some of the same CU features. Firstly, CU is input into the trained FSVM model, extracting the standard deviation, directional complexity, and content difference complexity of the CUs, and it uses these features to make a judgment on whether to terminate the partitioning early. Then, the determination of the partition type of CU is regarded as a multi-classification problem, and a DAG-SVM classifier is used to classify it. The extracted features serve as input to the classifier, which predicts the partition type of the CU and thereby prevents unnecessary partitioning. The results of the experiment indicate that compared with the reference software VTM10.0 anchoring algorithm, the algorithm can save 49.38%~58.04% of coding time, and BDBR only increases by 0.76%~1.37%. The video quality and encoding performance are guaranteed while the encoding complexity is effectively reduced.

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Deep learning-based video quality enhancement for the new versatile video coding

Cited by 18 publications

References 36 publications

Effective and Efficient Video Compression by the Deep Learning Techniques

Effective and Efficient Video Compression by the Deep Learning Techniques

Adaptive Video Coding Framework with Spatial-Temporal Fusion for Optimized Streaming in Next-Generation Networks

FSVM- and DAG-SVM-Based Fast CU-Partitioning Algorithm for VVC Intra-Coding

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