Visual Perception Based Intra Coding Algorithm for H.266/VVC

Tsai, Yu-Hsiang; Lu, Chen-Rung; Chen, Mei-Juan; Hsieh, Meng-Chun; Yang, Chieh-Ming; Yeh, Chia‐Hung

doi:10.3390/electronics12092079

Cited by 5 publications

(6 citation statements)

References 29 publications

(72 reference statements)

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“…In particular, for high-resolution video sequences (A1 and A2), our method achieves a significant reduction in the number of encoded blocks by 90.14% and 88.72%, further illustrating the superior performance of our method for high-resolution video sequences. We can also verify the effectiveness of our method through many papers [36][37][38][39][40]. Figure 7 demonstrates that our solution outperforms the three algorithms in [33][34][35], achieving a favorable balance between BDBR and ΔT.…”

Section: Framework Performance Evaluationmentioning

confidence: 68%

A Low-Complexity Fast CU Partitioning Decision Method Based on Texture Features and Decision Trees

et al. 2023

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The rapid advancement of information technology, particularly in artificial intelligence and communication, is driving significant transformations in video coding. There is a steadily increasing demand for high-definition video in society. The latest video coding standard, versatile video coding (VVC), offers significant improvements in coding efficiency compared with its predecessor, high-efficiency video coding (HEVC). The improvement in coding efficiency is achieved through the introduction of a quadtree with nested multi-type tree (QTMT). However, this increase in coding efficiency also leads to a rise in coding complexity. In an effort to decrease the computational complexity of VVC coding, our proposed algorithm utilizes a decision tree (DT)-based approach for coding unit (CU) partitioning. The algorithm uses texture features and decision trees to efficiently determine CU partitioning. The algorithm can be summarized as follows: firstly, a statistical analysis of the new features of the VVC is carried out. More representative features are considered to extract to train classifiers that match the framework. Secondly, we have developed a novel framework for rapid CU decision making that is specifically designed to accommodate the distinctive characteristics of QTMT partitioning. The framework predicts in advance whether the CU needs to be partitioned and whether QT partitioning is required. The framework improves the efficiency of the decision-making process by transforming the partition decision of QTMT into multiple binary classification problems. Based on the experimental results, it can be concluded that our method significantly reduces the coding time by 55.19%, whereas BDBR increases it by only 1.64%. These findings demonstrate that our method is able to maintain efficient coding performance while significantly saving coding time.

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Section: Framework Performance Evaluationmentioning

confidence: 68%

A Low-Complexity Fast CU Partitioning Decision Method Based on Texture Features and Decision Trees

et al. 2023

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“…Compared with the methods proposed in [9,16], our proposed method achieves an additional time saving of 2.91% and 6.17% while maintaining better RD performance. The reason is that the methods in [9,16] require a large amount of data to train their models, and the performance of the method may be affected if the training data are insufficient or not comprehensive enough. The method we propose is based on an analysis of edge structure and spatial correlation, which requires lower computational complexity.…”

Section: Simulation Resultsmentioning

confidence: 96%

“…In [15], the authors present a low-complexity method, which is formed of five binary Light Gradient Boosting Machine (LightGBM) classifiers. In [16], a fast CU partitioning decision algorithm is presented based on texture complexity and convolutional neural networks (CNNs). It utilizes a symmetric convolution kernel to extract features and redesigns the loss function.…”

Section: Related Workmentioning

confidence: 99%

A Fast Algorithm for Intra-Frame Versatile Video Coding Based on Edge Features

Zhao

Shang

Wang

et al. 2023

Sensors

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Versatile Video Coding (VVC) introduces many new coding technologies, such as quadtree with nested multi-type tree (QTMT), which greatly improves the efficiency of VVC coding. However, its computational complexity is higher, which affects the application of VVC in real-time scenarios. Aiming to solve the problem of the high complexity of VVC intra coding, we propose a low-complexity partition algorithm based on edge features. Firstly, the Laplacian of Gaussian (LOG) operator was used to extract the edges in the coding frame, and the edges were divided into vertical and horizontal edges. Then, the coding unit (CU) was equally divided into four sub-blocks in the horizontal and vertical directions to calculate the feature values of the horizontal and vertical edges, respectively. Based on the feature values, we skipped unnecessary partition patterns in advance. Finally, for the CUs without edges, we decided to terminate the partition process according to the depth information of neighboring CUs. The experimental results show that compared with VTM-13.0, the proposed algorithm can save 54.08% of the encoding time on average, and the BDBR (Bjøntegaard delta bit rate) only increases by 1.61%.

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“…For coding content-based methods, in [19], a latitude-based preprocessing was introduced for the early termination of the coding unit (CU) partition in the polar region. In [20], the factor of an average background luminance for just-noticeable-distortion was applied to identify the visually distinguishable (VD) pixels within a coding unit (CU). Zhang et al [21] proposed a fast partition scheme based on adjacent sub-regions, and it skipped unnecessary partition modes in advance based on the similarity of the adjacent sub-regions in the horizontal and vertical directions.…”

Section: Related Workmentioning

confidence: 99%

A Fast Algorithm for VVC Intra Coding Based on the Most Probable Partition Pattern List

Zhao,

Shang

et al. 2023

Applied Sciences

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Compared with High-Efficiency Video Coding (HEVC), Versatile Video Coding (VVC) has more flexible division and higher compression efficiency, but it also has higher computational complexity. In order to reduce the coding complexity, a fast algorithm based on the most probable partition pattern list (MPPPL)and pixel content similarity is proposed. Firstly, the MPPPL is constructed by using the average texture complexity difference of the sub-coding unit under different partition modes. Then, the sub-block pixel mean difference is used to decide the best partition mode or shorten the MPPPL. Finally, the selection rules of the reference lines in the intra prediction process are counted and the unnecessary reference lines are skipped by using the pixel content similarity. The experimental results show that compared with VTM-13.0, the proposed algorithm can save 52.26% of the encoding time, and the BDBR (Bjontegarrd delta bit rate) only increases by 1.23%.

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Visual Perception Based Intra Coding Algorithm for H.266/VVC

Cited by 5 publications

References 29 publications

A Low-Complexity Fast CU Partitioning Decision Method Based on Texture Features and Decision Trees

A Low-Complexity Fast CU Partitioning Decision Method Based on Texture Features and Decision Trees

A Fast Algorithm for Intra-Frame Versatile Video Coding Based on Edge Features

A Fast Algorithm for VVC Intra Coding Based on the Most Probable Partition Pattern List

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