Efficient In-Loop Filtering Based on Enhanced Deep Convolutional Neural Networks for HEVC

Pan, Zhaoqing; Yi, Xiaokai; Zhang, Yun; Jeon, Byeungwoo; Kwong, Sam

doi:10.1109/tip.2020.2982534

Cited by 78 publications

(40 citation statements)

References 26 publications

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“…A graph is an abstract graph formed by a number of nodes and the edges connecting each node [31], [32]. It is usually used to describe a specific relationship between things.…”

Section: Graph Representation Learning With Node2vecmentioning

confidence: 99%

Internet Financial Fraud Detection Based on a Distributed Big Data Approach With Node2vec

et al. 2021

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The rapid development of information technologies like Internet of Things, Big Data, Artificial Intelligence, Blockchain, etc., has profoundly affected people's consumption behaviors and changed the development model of the financial industry. The financial services on Internet and IoT with new technologies has provided convenience and efficiency for consumers, but new hidden fraud risks are generated also. Fraud, arbitrage, vicious collection, etc., have caused bad effects and huge losses to the development of finance on Internet and IoT. However, as the scale of financial data continues to increase dramatically, it is more and more difficult for existing rule-based expert systems and traditional machine learning model systems to detect financial frauds from large-scale historical data. In the meantime, as the degree of specialization of financial fraud continues to increase, fraudsters can evade fraud detection by frequently changing their fraud methods. In this article, an intelligent and distributed Big Data approach for Internet financial fraud detections is proposed to implement graph embedding algorithm Node2Vec to learn and represent the topological features in the financial network graph into low-dimensional dense vectors, so as to intelligently and efficiently classify and predict the data samples of the large-scale dataset with the deep neural network. The approach is distributedly performed on the clusters of Apache Spark GraphX and Hadoop to process the large dataset in parallel. The groups of experimental results demonstrate that the proposed approach can improve the efficiency of Internet financial fraud detections with better precision rate, recall rate, F1-Score and F2-Score.

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“…A graph is an abstract graph formed by a number of nodes and the edges connecting each node [31], [32]. It is usually used to describe a specific relationship between things.…”

Section: Graph Representation Learning With Node2vecmentioning

confidence: 99%

Internet Financial Fraud Detection Based on a Distributed Big Data Approach With Node2vec

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In RHCNN, inter and intra coded frames are also enhanced with dedicated networks. In [46], residual blocks in the network are enhanced by splitting the input frame and processing each part with a different CNN branches. The output of each branch is then concatenated and fed to the next block.…”

Section: A Single-frame Quality Enhancementmentioning

confidence: 99%

A CNN-Based Prediction-Aware Quality Enhancement Framework for VVC

Nasiri

Hamidouche

Morin

et al. 2021

IEEE Open J. Signal Process.

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This paper presents a framework for Convolutional Neural Network (CNN)-based quality enhancement task, by taking advantage of coding information in the compressed video signal. The motivation is that normative decisions made by the encoder can significantly impact the type and strength of artifacts in the decoded images. In this paper, the main focus has been put on decisions defining the prediction signal in intra and inter frames. This information has been used in the training phase as well as input to help the process of learning artifacts that are specific to each coding type. Furthermore, to retain a low memory requirement for the proposed method, one model is used for all Quantization Parameters (QPs) with a QP-map, which is also shared between luma and chroma components. In addition to the Post Processing (PP) approach, the In-Loop Filtering (ILF) codec integration has also been considered, where the characteristics of the Group of Pictures (GoP) are taken into account to boost the performance. The proposed CNN-based Quality Enhancement (QE) framework has been implemented on top of the Versatile Video Coding (VVC) Test Model (VTM-10). Experiments show that the prediction-aware aspect of the proposed method improves the coding efficiency gain of the default CNN-based QE method by 1.52%, in terms of BD-BR, at the same network complexity compared to the default CNN-based QE filter.

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“…e SRM and its variants are used to process the image, mainly to suppress the relevance of image content. We propose a feature information fusion block inspired by [30].…”

Section: Feature Extraction and Fusion Layermentioning

confidence: 99%

SFRNet: Feature Extraction-Fusion Steganalysis Network Based on Squeeze-and-Excitation Block and RepVgg Block

Yang

Sicong

et al. 2021

Security and Communication Networks

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In the era of big data, convolutional neural network (CNN) has been widely used in the field of image classification and has achieved excellent performance. More and more researchers are beginning to combine deep neural networks with steganalysis to improve performance in recent years. However, most of the steganalysis algorithm based on the convolutional neural network has only run test against the WOW and S-UNIWARD algorithms; meanwhile, their versatility is insufficient due to long training time and the limit of image size. This paper proposes a new network architecture, called SFRNet, to solve these problems. The feature extraction and fusion layer can extract more features from the digital image. The RepVgg block is used to accelerate the inference and increase memory utilization. The SE block improves the detection accuracy rate because it can learn feature weights to make effective feature maps with significant weights and invalid or ineffective feature maps with small weights. Experimental results show that the SFRNet has achieved excellent performance in the detection accuracy rate against four state-of-the-art steganography algorithms in the spatial domain, e.g., HUGO, WOW, S-UNIWARD, and MiPOD, under different payloads. The SFRNet detection accuracy rate achieves 89.6% against S-UNIWARD algorithm with the payload of 0.4bpp and 72.5% at 0.2bpp. As the same time, the training time of our network is greatly reduced by 35% compared with Yedroudj-Net.

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Efficient In-Loop Filtering Based on Enhanced Deep Convolutional Neural Networks for HEVC

Cited by 78 publications

References 26 publications

Internet Financial Fraud Detection Based on a Distributed Big Data Approach With Node2vec

Internet Financial Fraud Detection Based on a Distributed Big Data Approach With Node2vec

A CNN-Based Prediction-Aware Quality Enhancement Framework for VVC

SFRNet: Feature Extraction-Fusion Steganalysis Network Based on Squeeze-and-Excitation Block and RepVgg Block

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