CtuNet: A Deep Learning-based Framework for Fast CTU Partitioning of H265/HEVC Intra- coding

Zaki, F.; Mohamed, Amr E.; Sayed, Samir G.

doi:10.1016/j.asej.2021.01.001

Cited by 23 publications

(15 citation statements)

References 26 publications

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“…Because of the proposed scheme, the authors had enhanced the performance of quad-tree binary-tree (QTBT) for intra-mode coding. Zaki et al [33] had presented CtuNet to partition the coding tree unit by approximating its functionality using deep learning methods. For predicting the coding tree unit partition of the HEVC standard, ResNet18-CNN model was adopted.…”

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

View full text Add to dashboard Cite

show abstract

“…Supervised learning algorithms such as Support Vector Machines (SVM) have been used predominantly in recent literature due to their less complexity and ability to handle binary classification effectively [154], [159]. In addition, techniques such as random forests [160], decision trees for data mining [161], and various deep learning-based methods [162], [163] have been attempted to predict coding parameters at various stages in the encoding…”

Section: B Impact Of the Encoder Optimization On Qoementioning

confidence: 99%

Measuring, Modeling and Integrating Time-Varying Video Quality in End-to-End Multimedia Service Delivery: A Review and Open Challenges

et al. 2022

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The multimedia delivery chain consists of multiple stages such as content preparation, content delivery via Over-The-Top delivery network and Internet Service Providers network. Within the multimedia service chain, each stage influences the Quality of Experience (QoE) of the end user. The objective of this work is to provide a comprehensive literature survey with future research challenges and opportunities in the field of time-varying video quality in multimedia service delivery. The contribution of this work is two fold: 1) Survey -we provide a review of state-of-the-art works for video quality models to quantify multiple artifacts into a single QoE metric, pooling strategies for global quality measurements, and Continuous Time-Varying Quality (CTVQ) models; 2) Future Challenges and Directions -we investigate ten major research challenges and future directions based on the state-of-the-art for QoE modelling, QoE-aware encoding/decoding and QoE monitoring/management of multimedia streaming in next-generation networks.

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“…Zhao et al [35] developed an adaptive CU split decision method with the deep learning and multi-featured fusion. Zaki et al [36] also proposed a CtuNet framework to support CTU partitioning using deep learning techniques. As the aforementioned methods are mainly focused on speeding up QT or BT, fast encoding schemes for the TT split are still needed in VVC.…”

Section: Related Workmentioning

confidence: 99%

Medical Image Compression Method Using Lightweight Multi-Layer Perceptron for Mobile Healthcare Applications

Lee¹,

Jun²,

Park³

et al. 2022

Computers, Materials &Amp; Continua

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As video compression is one of the core technologies required to enable seamless medical data streaming in mobile healthcare applications, there is a need to develop powerful media codecs that can achieve minimum bitrates while maintaining high perceptual quality. Versatile Video Coding (VVC) is the latest video coding standard that can provide powerful coding performance with a similar visual quality compared to the previously developed method that is High Efficiency Video Coding (HEVC). In order to achieve this improved coding performance, VVC adopted various advanced coding tools, such as flexible Multi-type Tree (MTT) block structure which uses Binary Tree (BT) split and Ternary Tree (TT) split. However, VVC encoder requires heavy computational complexity due to the excessive Ratedistortion Optimization (RDO) processes used to determine the optimal MTT block mode. In this paper, we propose a fast MTT decision method with two Lightweight Neural Networks (LNNs) using Multi-layer Perceptron (MLP), which are applied to determine the early termination of the TT split within the encoding process. Experimental results show that the proposed method significantly reduced the encoding complexity up to 26% with unnoticeable coding loss compared to the VVC Test Model (VTM).

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CtuNet: A Deep Learning-based Framework for Fast CTU Partitioning of H265/HEVC Intra- coding

Cited by 23 publications

References 26 publications

Effective and Efficient Video Compression by the Deep Learning Techniques

Effective and Efficient Video Compression by the Deep Learning Techniques

Measuring, Modeling and Integrating Time-Varying Video Quality in End-to-End Multimedia Service Delivery: A Review and Open Challenges

Medical Image Compression Method Using Lightweight Multi-Layer Perceptron for Mobile Healthcare Applications

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