Review of video compression techniques based on fractal transform function and swarm intelligence

Pandit, Shraddha; Shukla, Piyush Kumar; Tiwari, Akhilesh; Shukla, Prashant Kumar; Maheshwari, Manish; Dubey, Rachana

doi:10.1142/s0217979220500617

Cited by 51 publications

(39 citation statements)

References 42 publications

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“…(1) e random matrix Θ of B 2 × B 2 is constructed from the elements satisfying the standard Gaussian distribution. (2) Schmidt orthogonalization is performed on random vector Θ to make its column vectors orthogonal to each other [18,19]. B .…”

Section: Adaptive Cs Measurementmentioning

confidence: 99%

Research on Low Frame Rate Video Compression Algorithm in the Context of New Media

Yang¹,

Xiang²,

2021

Security and Communication Networks

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When using the current method to compress the low frame rate video animation video, there is no frame rate compensation for the video image, which cannot eliminate the artifacts generated in the compression process, resulting in low definition, poor quality, and low compression efficiency of the compressed low frame rate video animation video. In the context of new media, the linear function model is introduced to study the frame rate video animation video compression algorithm. In this paper, an adaptive detachable convolutional network is used to estimate the offset of low frame rate video animation using local convolution. According to the estimation results, the video frames are compensated to eliminate the artifacts of low frame rate video animation. After the frame rate compensation, the low frame rate video animation video is divided into blocks, the CS value of the image block is measured, the linear estimation of the image block is carried out by using the linear function model, and the compression of the low frame rate video animation video is completed according to the best linear estimation result. The experimental results show that the low frame rate video and animation video compressed by the proposed algorithm have high definition, high compression quality under different compression ratios, and high compression efficiency under different compression ratios.

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Section: Adaptive Cs Measurementmentioning

confidence: 99%

Research on Low Frame Rate Video Compression Algorithm in the Context of New Media

Yang¹,

Xiang²,

2021

Security and Communication Networks

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“…The basic architecture of ResNets is shown in Figure 2 . ResNets are used for dealing with the issue of vanishing/exploding gradient when increasing the number of layers in the model which leads to large error values at the time of training and testing [ 43 ]. Residual block uses a technique of skip connections which skips few layers in the neural network and connects directly to the output [ 18 , 27 , 29 , 44 ].…”

Section: Methodsmentioning

confidence: 99%

Enhancement of Patient Facial Recognition through Deep Learning Algorithm: ConvNet

Onyema

Shukla

Dalal

et al. 2021

Journal of Healthcare Engineering

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The use of machine learning algorithms for facial expression recognition and patient monitoring is a growing area of research interest. In this study, we present a technique for facial expression recognition based on deep learning algorithm: convolutional neural network (ConvNet). Data were collected from the FER2013 dataset that contains samples of seven universal facial expressions for training. The results show that the presented technique improves facial expression recognition accuracy without encoding several layers of CNN that lead to a computationally costly model. This study proffers solutions to the issues of high computational cost due to the implementation of facial expression recognition by providing a model close to the accuracy of the state-of-the-art model. The study concludes that deep l\earning-enabled facial expression recognition techniques enhance accuracy, better facial recognition, and interpretation of facial expressions and features that promote efficiency and prediction in the health sector.

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“…Subsequently, this classifier is used for the detection of motion artifacts from EEG. Once artifacts are identified, subsequently, the IMFs generated are sourced to a cascaded approach of CCA and SWT algorithm for purifying [24].…”

Section: Recommended System Modelmentioning

confidence: 99%

A Machine Learning-Based Big EEG Data Artifact Detection and Wavelet-Based Removal: An Empirical Approach

Stalin

Roy

Shukla

et al. 2021

Mathematical Problems in Engineering

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The electroencephalogram (EEG) signals are a big data which are frequently corrupted by motion artifacts. As human neural diseases, diagnosis and analysis need a robust neurological signal. Consequently, the EEG artifacts’ eradication is a vital step. In this research paper, the primary motion artifact is detected from a single-channel EEG signal using support vector machine (SVM) and preceded with further artifacts’ suppression. The signal features’ abstraction and further detection are done through ensemble empirical mode decomposition (EEMD) algorithm. Moreover, canonical correlation analysis (CCA) filtering approach is applied for motion artifact removal. Finally, leftover motion artifacts’ unpredictability is removed by applying wavelet transform (WT) algorithm. Finally, results are optimized by using Harris hawks optimization (HHO) algorithm. The results of the assessment confirm that the algorithm recommended is superior to the algorithms currently in use.

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Review of video compression techniques based on fractal transform function and swarm intelligence

Cited by 51 publications

References 42 publications

Research on Low Frame Rate Video Compression Algorithm in the Context of New Media

Research on Low Frame Rate Video Compression Algorithm in the Context of New Media

Enhancement of Patient Facial Recognition through Deep Learning Algorithm: ConvNet

A Machine Learning-Based Big EEG Data Artifact Detection and Wavelet-Based Removal: An Empirical Approach

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