Nature inspired algorithm based fast intra mode decision in HEVC

Tariq, Junaid; Javed, Mubashar; Ayub, Bushra; Armghan, Ammar; Ijaz, Aamir; Alenezi, Fayadh; Zulfiqar, Adil

doi:10.1007/s11042-023-14999-6

Cited by 4 publications

(2 citation statements)

References 20 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%

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%

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

Nair,

Nair

2024

IEEE Access

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“…CNNs [101] analyze the input data in the context of computed tomography reconstruction by applying several trainable filters that convolve across the input's structural dimensions. Local patterns and characteristics may be extracted from the computed tomography data using this convolution procedure [102,103]. CNNs may learn representations that are especially suited to added tomography reconstruction tasks by stacking many convolutional layers, which allows them to capture increasingly complicated and abstract aspects from the input data.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction

Rahman,

Khan,

Sadiq

et al. 2023

Tomography

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Computed tomography (CT) is used in a wide range of medical imaging diagnoses. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy. In order to address these challenges, deep learning developments have the potential to improve the reconstruction of computed tomography images. In this regard, our research aim is to determine the techniques that are used for 3D deep learning in CT reconstruction and to identify the training and validation datasets that are accessible. This research was performed on five databases. After a careful assessment of each record based on the objective and scope of the study, we selected 60 research articles for this review. This systematic literature review revealed that convolutional neural networks (CNNs), 3D convolutional neural networks (3D CNNs), and deep learning reconstruction (DLR) were the most suitable deep learning algorithms for CT reconstruction. Additionally, two major datasets appropriate for training and developing deep learning systems were identified: 2016 NIH-AAPM-Mayo and MSCT. These datasets are important resources for the creation and assessment of CT reconstruction models. According to the results, 3D deep learning may increase the effectiveness of CT image reconstruction, boost image quality, and lower radiation exposure. By using these deep learning approaches, CT image reconstruction may be made more precise and effective, improving patient outcomes, diagnostic accuracy, and healthcare system productivity.

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AI application in video: spiral optimizer based fast intra mode selection in HEVC

Tariq,

Javed,

Rahman

et al. 2024

Multimed Tools Appl

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Nature inspired algorithm based fast intra mode decision in HEVC

Cited by 4 publications

References 20 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

A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction

AI application in video: spiral optimizer based fast intra mode selection in HEVC

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