Fast Intra Mode Selection in HEVC Using Statistical Model

Tariq, Junaid; Alfalou, Ayman; Ijaz, Aamir; Ali, Hashim; Ashraf, Imran; Armghan, Ammar; Mashood, Inzamam; Rehman, Saad

doi:10.32604/cmc.2022.019541

Cited by 12 publications

(8 citation statements)

References 33 publications

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“…It can be observed that our method possess a bit rate drop of about 3.35%. But the loss is acceptable, since our method possess a good encoding time reduction of about 19.67%, 36.32%, 12.56%, 27.07%, 25.03% and 23.27% over the methods in [23], [24], [25], [4], [28] and [29], respectively.…”

Section: ∆Psnr = Psnr New − Psnr Anchormentioning

confidence: 89%

“…These enabled the encoders to deliver a better output visual quality by using only one half of the bit-rate than those existed. For the past few decades, a variety of complexity reduction algorithms -(heuristic methods [26], [27], [2], [3], [4]), machine learning methods ( [5], [6]) [7], [8]), deep learning methods( [9], [10], [11]) -are used to improve the quality of HEVC video coding with reduced computational complexities in terms of bit-rate, time and visual quality. This letter proposes a learned model to predict the intra mode effectively using a non-linear kernel based SVM classifier for classifying the optimal PU mode.…”

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confidence: 99%

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KSVM-Based Fast Intra Mode Prediction in HEVC Using Statistical Features and Sparse Autoencoder

Nair,

Nair

2024

IEEE Access

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High Efficiency Video Coding (HEVC) is designed to deliver a video communication with better quality at reduced bit rate. For intra coding, HEVC employs an effective hierarchical quad tree partitioning and an exhaustive optimal mode search which increases the time complexity. Aiming this issue, we propose a Support Vector Machine (SVM)-based method to effectively predict the intra mode. Compared to the standard HEVC encoder HM-15.0, the proposed method could reduce 57.6 % of encoding time at a bit-rate penalty of 3.3% at an average PSNR decline of only around 0.09 dB.

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Section: ∆Psnr = Psnr New − Psnr Anchormentioning

confidence: 89%

mentioning

confidence: 99%

KSVM-Based Fast Intra Mode Prediction in HEVC Using Statistical Features and Sparse Autoencoder

Nair,

Nair

2024

IEEE Access

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“…Since the ML algorithm does not work for textual data, the preprocessing needs to be applied to convert textual data into numerical vectors. The preprocessing is carried out with the help of the NLTK library in python [54][55][56][57][58][59][60][61][62]. The preprocessing module as shown in Fig.…”

Section: Preprocessing Modulementioning

confidence: 99%

Multi-Tier Sentiment Analysis of Social Media Text Using Supervised Machine Learning

Tariq¹,

Masood²,

Subahi³

et al. 2023

Computers, Materials &Amp; Continua

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Sentiment Analysis (SA) is often referred to as opinion mining. It is defined as the extraction, identification, or characterization of the sentiment from text. Generally, the sentiment of a textual document is classified into binary classes i.e., positive and negative. However, fine-grained classification provides a better insight into the sentiments. The downside is that fine-grained classification is more challenging as compared to binary. On the contrary, performance deteriorates significantly in the case of multi-class classification. In this study, pre-processing techniques and machine learning models for the multi-class classification of sentiments were explored. To augment the performance, a multi-layer classification model has been proposed. Owing to similitude with social media text, the movie reviews dataset has been used for the implementation. Supervised machine learning models namely Decision Tree, Support Vector Machine, and Naïve Bayes models have been implemented for the task of sentiment classification. We have compared the models of single-layer architecture with multi-tier model. The results of Multi-tier model have slight improvement over the single-layer architecture. Moreover, multi-tier models have better recall which allow our proposed model to learn more context. We have discussed certain shortcomings of the model that will help researchers to design multi-tier models with more contextual information.

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“…The purpose of developing such a domain which focus the forensic based on IoT architecture utilizing node-2-node connections. In this scenario, Various attacks are performed on IoT machines [41][42][43]. The function of our proposed architecture is to alter the coming traffic for IoT towards snort.…”

Section: Reportingmentioning

confidence: 99%

Digital Forensics Analysis of IoT Nodes using Machine Learning

Arshad

Tariq

Riaz

et al. 2022

JCBI

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With the versatility and exponential growth of IoT solutions, the probability of being attacked has increased. Resource constraint IoT devices raised a challenge for the security handler to track logs of different variety of attacks generated on them while performing the forensic analysis. Commonly forensic analysis is performed on the devices that calculate how much loss has occurred to the device due to the diversity of attacks. The main objective of this paper to develop a framework through which secueity can perfrom the forensic analysis on resource contraint IoT devices. In this paper, we have proposed a framework that intelligently performs forensic analysis and detects the different types of attacks performed on the endpoint (IoT device) using a node to node (N2N) framework. Furthermore, this proposed solution is a blend of different forensic tools and Machine learning techniques to identify different types of attacks. Using a third-party log server, the problem of evidence recovery from the endpoint under attack is addressed. To determine the nature and effect of the attack we have used the logs by using the security onion (forensic server). Additionally, this framework is equipped to automatically detect attacks by using the different machine learning algorithms. The efficiency of machine learning models is measured upon the values of (1) Accuracy, (2) Precision, (3) Recall, and (4) F-Measure. The results show that the decision tree algorithm stands out with the optimum performance compared to other ML models. Overall this framework can be used for the secuirty of IoT devices as well as the evidence collection from the IoT endpoint. For the validation of the proposed framework more detailed results and performance, analysis is presented in this paper.

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Fast Intra Mode Selection in HEVC Using Statistical Model

Cited by 12 publications

References 33 publications

KSVM-Based Fast Intra Mode Prediction in HEVC Using Statistical Features and Sparse Autoencoder

KSVM-Based Fast Intra Mode Prediction in HEVC Using Statistical Features and Sparse Autoencoder

Multi-Tier Sentiment Analysis of Social Media Text Using Supervised Machine Learning

Digital Forensics Analysis of IoT Nodes using Machine Learning

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