scholarly journals Context-Based Inter Mode Decision Method for Fast Affine Prediction in Versatile Video Coding

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1243
Author(s):  
Seongwon Jung ◽  
Dongsan Jun
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
Translational Motion ◽  
Rate Distortion ◽  
Previous Method ◽  
Mode Decision ◽  
Test Model ◽  
High Efficiency Video Coding ◽  
Affine Motion ◽  
Decision Method

Versatile Video Coding (VVC) is the most recent video coding standard developed by Joint Video Experts Team (JVET) that can achieve a bit-rate reduction of 50% with perceptually similar quality compared to the previous method, namely High Efficiency Video Coding (HEVC). Although VVC can support the significant coding performance, it leads to the tremendous computational complexity of VVC encoder. In particular, VVC has newly adopted an affine motion estimation (AME) method to overcome the limitations of the translational motion model at the expense of higher encoding complexity. In this paper, we proposed a context-based inter mode decision method for fast affine prediction that determines whether the AME is performed or not in the process of rate-distortion (RD) optimization for optimal CU-mode decision. Experimental results showed that the proposed method significantly reduced the encoding complexity of AME up to 33% with unnoticeable coding loss compared to the VVC Test Model (VTM).

scholarly journals Novel Intermode Prediction Algorithm for High Efficiency Video Coding Encoder

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Chan-seob Park ◽  
Gwang-Soo Hong ◽  
Byung-Gyu Kim
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
Rate Distortion ◽  
Experimental Result ◽  
Prediction Algorithm ◽  
Basic Unit ◽  
Test Model ◽  
High Efficiency Video Coding ◽  
Time Saving ◽  
Decision Algorithm

The joint collaborative team on video coding (JCT-VC) is developing the next-generation video coding standard which is called high efficiency video coding (HEVC). In the HEVC, there are three units in block structure: coding unit (CU), prediction unit (PU), and transform unit (TU). The CU is the basic unit of region splitting like macroblock (MB). Each CU performs recursive splitting into four blocks with equal size, starting from the tree block. In this paper, we propose a fast CU depth decision algorithm for HEVC technology to reduce its computational complexity. In2N×2N PU, the proposed method compares the rate-distortion (RD) cost and determines the depth using the compared information. Moreover, in order to speed up the encoding time, the efficient merge SKIP detection method is developed additionally based on the contextual mode information of neighboring CUs. Experimental result shows that the proposed algorithm achieves the average time-saving factor of 44.84% in the random access (RA) at Main profile configuration with the HEVC test model (HM) 10.0 reference software. Compared to HM 10.0 encoder, a small BD-bitrate loss of 0.17% is also observed without significant loss of image quality.

scholarly journals Low-Complexity Texture Video Coding Based on Motion Homogeneity for 3D-HEVC

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Qiuwen Zhang ◽  
Shuaichao Wei ◽  
Rijian Su
Keyword(s):  
Video Coding ◽  
Video Compression ◽  
High Efficiency ◽  
Rate Distortion ◽  
Mode Decision ◽  
High Efficiency Video Coding ◽  
Search Range ◽  
Depth Level ◽  
Practical Applications ◽  
Coding Efficiency

Three-dimensional extension of the high efficiency video coding (3D-HEVC) is an emerging international video compression standard for multiview video system applications. Similar to HEVC, a computationally expensive mode decision is performed using all depth levels and prediction modes to select the least rate-distortion (RD) cost for each coding unit (CU). In addition, new tools and intercomponent prediction techniques have been introduced to 3D-HEVC for improving the compression efficiency of the multiview texture videos. These techniques, despite achieving the highest texture video coding efficiency, involve extremely high-complex procedures, thus limiting 3D-HEVC encoders in practical applications. In this paper, a fast texture video coding method based on motion homogeneity is proposed to reduce 3D-HEVC computational complexity. Because the multiview texture videos instantly represent the same scene at the same time (considering that the optimal CU depth level and prediction modes are highly multiview content dependent), it is not efficient to use all depth levels and prediction modes in 3D-HEVC. The motion homogeneity model of a CU is first studied according to the motion vectors and prediction modes from the corresponding CUs. Based on this model, we present three efficient texture video coding approaches, such as the fast depth level range determination, early SKIP/Merge mode decision, and adaptive motion search range adjustment. Experimental results demonstrate that the proposed overall method can save 56.6% encoding time with only trivial coding efficiency degradation.

Lagrange optimization in high efficiency video coding for SATD-based intra-mode decision

2017 ◽  
Vol 11 (6) ◽  
pp. 1163-1170 ◽  
Author(s):  
Wei Li ◽  
Fan Zhao ◽  
Erhu Zhang ◽  
Peng Ren
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
Mode Decision ◽  
High Efficiency Video Coding ◽  
Intra Mode Decision

Machine Learning-Based Coding Decision Making in H.265/HEVC CTU Division and Intra Prediction

2020 ◽  
Vol 11 (2) ◽  
pp. 41-60
Author(s):  
Wenchan Jiang ◽  
Ming Yang ◽  
Ying Xie ◽  
Zhigang Li
Keyword(s):  
Machine Learning ◽  
Video Coding ◽  
High Efficiency ◽  
Main Tool ◽  
Decision Time ◽  
Mode Decision ◽  
Supervised Machine Learning ◽  
High Efficiency Video Coding ◽  
Neuron Network ◽  
Prediction Mode

High efficiency video coding (HEVC) has been deemed as the newest video coding standard of the ITU-T Video Coding Experts Group and the ISO/IEC Moving Picture Experts Group. In this research project, in compliance with H.265 standard, the authors focused on improving the performance of encode/decode by optimizing the partition of prediction block in coding unit with the help of supervised machine learning. The authors used Keras library as the main tool to implement the experiments. Key parameters were tuned for the model in the convolution neuron network. The coding tree unit mode decision time produced in the model was compared with that produced in the reference software for HEVC, and it was proven to have improved significantly. The intra-picture prediction mode decision was also investigated with modified model and yielded satisfactory results.

scholarly journals Adaptive CU Split Decision Based on Deep Learning and Multifeature Fusion for H.266/VVC

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jinchao Zhao ◽  
Yihan Wang ◽  
Qiuwen Zhang
Keyword(s):  
Deep Learning ◽  
Video Coding ◽  
High Efficiency ◽  
Texture Classification ◽  
Rate Distortion ◽  
Classification Model ◽  
High Efficiency Video Coding ◽  
Fast Encoding ◽  
Training Samples ◽  
Coding Unit

With the development of technology, the hardware requirement and expectations of user for visual enjoyment are getting higher and higher. The multitype tree (MTT) architecture is proposed by the Joint Video Experts Team (JVET). Therefore, it is necessary to determine not only coding unit (CU) depth but also its split mode in the H.266/Versatile Video Coding (H.266/VVC). Although H.266/VVC achieves significant coding performance on the basis of H.265/High Efficiency Video Coding (H.265/HEVC), it causes significantly coding complexity and increases coding time, where the most time-consuming part is traversal calculation rate-distortion (RD) of CU. To solve these problems, this paper proposes an adaptive CU split decision method based on deep learning and multifeature fusion. Firstly, we develop a texture classification model based on threshold to recognize complex and homogeneous CU. Secondly, if the complex CUs belong to edge CU, a Convolutional Neural Network (CNN) structure based on multifeature fusion is utilized to classify CU. Otherwise, an adaptive CNN structure is used to classify CUs. Finally, the division of CU is determined by the trained network and the parameters of CU. When the complex CUs are split, the above two CNN schemes can successfully process the training samples and terminate the rate-distortion optimization (RDO) calculation for some CUs. The experimental results indicate that the proposed method reduces the computational complexity and saves 39.39% encoding time, thereby achieving fast encoding in H.266/VVC.

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