A Method to Reduce the Intra-Frame Prediction Complexity of HEVC Based on D-CNN

Wang, Ting; Wang, Geng; Li, Huayu; Bui, ThiOanh; Qian, Zhendong; Wang, Ruliang

doi:10.3390/electronics12092091

Cited by 5 publications

(2 citation statements)

References 46 publications

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“…In [30], a bagged tree model was employed to predict the splitting of a CTU, and the partition problem of a 32 × 32-sized CU was modeled as a 17-output classification task. Wang et al [31] proposed a densely connected convolution neural network to predict the partition of coding units (CUs), which significantly reduces the coding complexity. Zan et al proposed a redesigned U-NET with a quality parameter fusion network to accelerate the QTMT partition process [32].…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Fast Versatile Video Coding (VVC) Intra Coding for Power-Constrained Applications

Chen,

Cheng,

Zhu

et al. 2024

Electronics

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Versatile Video Coding (VVC) achieves impressive coding gain improvement (about 40%+) over the preceding High-Efficiency Video Coding (HEVC) technology at the cost of extremely high computational complexity. Such an extremely high complexity increase is a great challenge for power-constrained applications, such as Internet of video things. In the case of intra coding, VVC utilizes the brute-force recursive search for both the partition structure of the coding unit (CU), which is based on the quadtree with nested multi-type tree (QTMT), and 67 intra prediction modes, compared to 35 in HEVC. As a result, we offer optimization strategies for CU partition decision and intra coding modes to lessen the computational overhead. Regarding the high complexity of the CU partition process, first, CUs are categorized as simple, fuzzy, and complex based on their texture characteristics. Then, we train two random forest classifiers to speed up the RDO-based brute-force recursive search process. One of the classifiers directly predicts the optimal partition modes for simple and complex CUs, while another classifier determines the early termination of the partition process for fuzzy CUs. Meanwhile, to reduce the complexity of intra mode prediction, a fast hierarchical intra mode search method is designed based on the texture features of CUs, including texture complexity, texture direction, and texture context information. Extensive experimental findings demonstrate that the proposed approach reduces complexity by up to 77% compared to the latest VVC reference software (VTM-23.1). Additionally, an average coding time saving of 70% is achieved with only a 1.65% increase in BDBR. Furthermore, when compared to state-of-the-art methods, the proposed method also achieves the largest time saving with comparable BDBR loss. These findings indicate that our method is superior to other up-to-date methods in terms of lowering VVC intra coding complexity, which provides an elective solution for power-constrained applications.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Fast Versatile Video Coding (VVC) Intra Coding for Power-Constrained Applications

Chen,

Cheng,

Zhu

et al. 2024

Electronics

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“…For transmitting a huge quantity of video information effectively at a better bit rate, the standards of video transmission are introduced like multiple function video coding standard (VVC/H.266), advanced video coding (AVC/H.264), and high-efficiency video coding (HEVC/H.265) [3]. The video coding standards mentioned in the text employ flexible block coding techniques and incorporate effective coding tools, thereby maximizing complexity of different video coding standards [4]. By considering VVC and HEVC, the HEVC takes a quad-tree-based CU recursive partition technique for CU [5].…”

Section: Introductionmentioning

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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Fcd-cnn: FPGA-based CU depth decision for HEVC intra encoder using CNN

Dehnavi,

Klidbary

2024

J Real-Time Image Proc

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A Method to Reduce the Intra-Frame Prediction Complexity of HEVC Based on D-CNN

Cited by 5 publications

References 46 publications

Fast Versatile Video Coding (VVC) Intra Coding for Power-Constrained Applications

Fast Versatile Video Coding (VVC) Intra Coding for Power-Constrained Applications

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

Fcd-cnn: FPGA-based CU depth decision for HEVC intra encoder using CNN

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