Design and Implementation of Deep Depth Decision Algorithm for Complexity Reduction in High Efficiency Video Coding (HEVC)

Joy, Helen K.; Kounte, Manjunath R; Sujatha, B.

doi:10.14569/ijacsa.2022.0130168

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…Joy [16] implemented a deep convolutional neural network (DCNN) for the reduction of complexity in high efficiency video coding (HEVC).…”

Section: Literature Reviewmentioning

confidence: 99%

Coding Tree Unit Partition Prediction Algorithm Utilizing Residual AttentionBased Long Short-Term Memory for Video Intra Coding

2024

IJIES

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High-efficiency video coding (HEVC) stands as one of the most extensively employed video coding standards, with the primary encoding challenge revolving around the searching process. To minimize coding complexity of HEVC block partition, a new algorithm is proposed for supporting the partition architecture of coding tree unit (CTU) in intra-coding. In this research, problem of partition architecture decisions of CTU is resolved through two stages. In first phase, the bagged tree method is evaluated for predicting CTU splitting, and in the second phase, the partition issue of 32 × 32 size CU is given as 17 outcome classification. To attain a huge accuracy of prediction, a residual attention-based long short-term memory (LSTM) with parametric rectified linear unit (AT-LSTM-PReLU) is proposed. The proposed method produces a structure of partition quad-tree of CTU that makes multiple decisions at variant depth levels. The performance of proposed method is estimated with a dataset gathered from the joint collaborative team and video coding (JCT-VC). The proposed approach achieved a remarkable bit rate improvement of 3.72%, a significant ∆T enhancement of 97.28%, and minimized video quality degradation by just -0.05dB. These results outperform other deep learning methods like convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM).

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“…Joy [16] implemented a deep convolutional neural network (DCNN) for the reduction of complexity in high efficiency video coding (HEVC).…”

Section: Literature Reviewmentioning

confidence: 99%

Coding Tree Unit Partition Prediction Algorithm Utilizing Residual AttentionBased Long Short-Term Memory for Video Intra Coding

2024

IJIES

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“…As the procedure needs an output of correlation between the contents within the frame. Deep learning techniques will be a good resolving this issue [7,8,12]. The deep layers help to extract the features and recover the perfect match for intra prediction.…”

Section: Figure 1 Flow Of Designing Inter Prediction With Deep Depth ...mentioning

confidence: 99%

Modelling of Depth Prediction Algorithm for Intra Prediction Complexity Reduction

Joy¹,

Kounte²

2022

ATAIML

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Video compression gained its relevance with the boon of the internet, mobile phones, variable resolution acquisition device etc. The redundant information is explored in initial stages of compression that’s is prediction. Inter prediction that is prediction within the frame generates high computational complexity when working with traditional signal processing procedures. The paper proposes the design of a deep convolutional neural network model to perform inter prediction by crossing out the flaws in the traditional method. It briefs the modeling of network, mathematics behind each stage and evaluation of the proposed model with sample dataset. The video frame’s coding tree unit (CTU) of 64x64 is the input, the model converts and store it as a 16-element vector with the goodness of CNN network. It gives an overview of deep depth decision algorithm. The evaluation process shows that the model performs better for compression with less computational complexity.

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“…Researches focus on the reduction of an artifact by the traditional [2] signal processing method. H.265/AVC, HEVC uses a de-blocking filter [3] to reduce the effect of block-based prediction. In HEVC SAO is used after the de-blocking filter to reduce the ringing effect.…”

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confidence: 99%

Deep learning-based switchable network for in-loop filtering in high efficiency video coding

Joy

Kounte

2023

IJECE

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<span lang="EN-US">The video codecs are focusing on a smart transition in this era. A future area of research that has not yet been fully investigated is the effect of deep learning on video compression. The paper’s goal is to reduce the ringing and artifacts that loop filtering causes when high-efficiency video compression is used. Even though there is a lot of research being done to lessen this effect, there are still many improvements that can be made. In This paper we have focused on an intelligent solution for improvising in-loop filtering in high efficiency video coding (HEVC) using a deep convolutional neural network (CNN). The paper proposes the design and implementation of deep CNN-based loop filtering using a series of 15 CNN networks followed by a combine and squeeze network that improves feature extraction. The resultant output is free from double enhancement and the peak signal-to-noise ratio is improved by 0.5 dB compared to existing techniques. The experiments then demonstrate that improving the coding efficiency by pipelining this network to the current network and using it for higher quantization parameters (QP) is more effective than using it separately. Coding efficiency is improved by an average of 8.3% with the switching based deep CNN in-loop filtering.</span>

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Design and Implementation of Deep Depth Decision Algorithm for Complexity Reduction in High Efficiency Video Coding (HEVC)

Cited by 9 publications

References 18 publications

Coding Tree Unit Partition Prediction Algorithm Utilizing Residual AttentionBased Long Short-Term Memory for Video Intra Coding

Coding Tree Unit Partition Prediction Algorithm Utilizing Residual AttentionBased Long Short-Term Memory for Video Intra Coding

Modelling of Depth Prediction Algorithm for Intra Prediction Complexity Reduction

Deep learning-based switchable network for in-loop filtering in high efficiency video coding

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