Fast CU Size Decision Method Based on Just Noticeable Distortion and Deep Learning

Zhao, Jinchao; Wang, Yihan; Zhang, Qiuwen

doi:10.1155/2021/3813116

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…It simultaneously determines the division decisions of QT and MT by considering the texture complexity of CUs and the contextual information of neighboring CUs. In [23], an algorithm for CU classification is introduced, which combines the principles of just noticeable distortion (JND) and SVM models. The algorithm devises a hybrid JND threshold model by considering the distortion sensitivity of visual image regions, facilitating the partitioning of CUs.…”

Section: Related Workmentioning

confidence: 99%

Fast Decision-Tree-Based Series Partitioning and Mode Prediction Termination Algorithm for H.266/VVC

Li,

He,

Zhang

2024

Electronics

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With the advancement of network technology, multimedia videos have emerged as a crucial channel for individuals to access external information, owing to their realistic and intuitive effects. In the presence of high frame rate and high dynamic range videos, the coding efficiency of high-efficiency video coding (HEVC) falls short of meeting the storage and transmission demands of the video content. Therefore, versatile video coding (VVC) introduces a nested quadtree plus multi-type tree (QTMT) segmentation structure based on the HEVC standard, while also expanding the intra-prediction modes from 35 to 67. While the new technology introduced by VVC has enhanced compression performance, it concurrently introduces a higher level of computational complexity. To enhance coding efficiency and diminish computational complexity, this paper explores two key aspects: coding unit (CU) partition decision-making and intra-frame mode selection. Firstly, to address the flexible partitioning structure of QTMT, we propose a decision-tree-based series partitioning decision algorithm for partitioning decisions. Through concatenating the quadtree (QT) partition division decision with the multi-type tree (MT) division decision, a strategy is implemented to determine whether to skip the MT division decision based on texture characteristics. If the MT partition decision is used, four decision tree classifiers are used to judge different partition types. Secondly, for intra-frame mode selection, this paper proposes an ensemble-learning-based algorithm for mode prediction termination. Through the reordering of complete candidate modes and the assessment of prediction accuracy, the termination of redundant candidate modes is accomplished. Experimental results show that compared with the VVC test model (VTM), the algorithm proposed in this paper achieves an average time saving of 54.74%, while the BDBR only increases by 1.61%.

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Section: Related Workmentioning

confidence: 99%

Fast Decision-Tree-Based Series Partitioning and Mode Prediction Termination Algorithm for H.266/VVC

Li,

He,

Zhang

2024

Electronics

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“…Ref. [16] introducing a rapid CU classification algorithm that utilizes visible distortion (JND) and SVM. The method classifies CUs into three categories: smooth, normal, and complex, employing the JND model.…”

Section: Background and Related Workmentioning

confidence: 99%

Fast CU Division Pattern Decision Based on the Combination of Spatio-Temporal Information

2023

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In order to satisfy the growing need for high-quality video, VVC comes with more efficient coding performance. According to statistical analysis, the level of coding complexity in VVC is tenfold greater compared to that of HEVC, so it is our main goal to study that what methods can be employed to decrease the time complexity of VVC. CU split in intra-frame modes requires the split mode decision by RD loss calculation, and the process of coding makes it to calculate RD calculation for all possible mode combinations, which is an important area that brings complexity to video coding, so in order to achieve our goal. Initially, we introduce an optimal depth prediction algorithm for Coding Units (CUs) by leveraging temporal combination. This algorithm collects depth information of CUs to predict the coding depth of CU blocks. Additionally, we suggest a decision tree-based method for CU split mode decision. With this method, we can make a decision on the CU split mode within the obtained split depth, reducing the time complexity of coding. This decision is based on the predictions from the first algorithm. The results demonstrate that our algorithm achieves superior performance over state-of-the-art methods in terms of computational complexity and compression quality. Compared to the VVC reference software (VTM), our method saves an average of 53.92% in coding time and improves the BDBR by 1.74%. These findings suggest that our method is highly effective in improving both computational efficiency and compression quality.

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“…In [18], an early termination hierarchical CNN (ETH-CNN) and decision flow were proposed to predict CU partitions, thereby reducing the complexity. In [19], a rapid CU partition decision algorithm based on just noticeable distortion (JND) and SVM was proposed. The method uses the JND model to classify CUs into three categories: smooth, normal, and complex.…”

Section: Fast Algorithms For Vvcmentioning

confidence: 99%

Fast CU Partition Decision Algorithm for VVC Intra Coding Using an MET-CNN

et al. 2022

Self Cite

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The newest video coding standard, the versatile video coding standard (VVC/H.266), came into effect in November 2020. Different from the previous generation standard—high-efficiency video coding (HEVC/H.265)—VVC adopts a more flexible block division structure, the quad-tree with nested multi-type tree (QTMT) structure, which improves its coding performance by 24%. However, it also causes a substantial increase in computational complexity. Therefore, this paper first proposes the concept of a stage grid map, which divides the overall division of a 32 × 32 coding unit (CU) into four stages and represents it as a structured output. Second, a multi-stage early termination convolutional neural network (MET-CNN) model is devised to predict the full partition information of a CU with a size of 32 × 32. Finally, a fast CU partition decision algorithm for VVC intra coding based on an MET-CNN is proposed. The algorithm can predict all partition information of a CU with a size of 32 × 32 and its sub-CUs in one run, completely replacing the complex rate-distortion optimization (RDO) process. It also has an early exit mechanism, thereby greatly reducing the encoding time. The experimental results illustrate that the scheme proposed in this paper reduces the encoding time by 49.24% on average, while the Bjøntegaard Delta Bit Rate (BDBR) only increases by 0.97%.

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Fast CU Size Decision Method Based on Just Noticeable Distortion and Deep Learning

Cited by 3 publications

References 28 publications

Fast Decision-Tree-Based Series Partitioning and Mode Prediction Termination Algorithm for H.266/VVC

Fast Decision-Tree-Based Series Partitioning and Mode Prediction Termination Algorithm for H.266/VVC

Fast CU Division Pattern Decision Based on the Combination of Spatio-Temporal Information

Fast CU Partition Decision Algorithm for VVC Intra Coding Using an MET-CNN

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