Fast Coding Unit Size Decision Based on Probabilistic Graphical Model in High Efficiency Video Coding Inter Prediction

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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Feature-based fast coding unit partition algorithm for high efficiency video coding

Journal of Applied Research and Technology ◽

10.1016/j.jart.2015.06.019 ◽

2015 ◽

Vol 13 (2) ◽

pp. 205-219 ◽

Cited By ~ 8

Author(s):

Yih-Chuan Lin ◽

Jian-Cheng Lai

Keyword(s):

Video Coding ◽

High Efficiency ◽

High Efficiency Video Coding ◽

Feature Based ◽

Coding Unit

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Complexity reduction method for High Efficiency Video Coding encoding based on scene-change detection and image texture information

International Journal of Distributed Sensor Networks ◽

10.1177/1550147719892562 ◽

2019 ◽

Vol 15 (12) ◽

pp. 155014771989256

Author(s):

Hong-rae Lee ◽

Eun-bin Ahn ◽

A-young Kim ◽

Kwang-deok Seo

Keyword(s):

Computational Complexity ◽

Video Coding ◽

High Efficiency ◽

Image Texture ◽

Inter Prediction ◽

Scene Change ◽

High Efficiency Video Coding ◽

Scene Change Detection ◽

Real Time Processing ◽

Coding Efficiency

Recently, as demand for high-quality video and realistic media has increased, High Efficiency Video Coding has been standardized. However, High Efficiency Video Coding requires heavy cost in terms of computational complexity to achieve high coding efficiency, which causes problems in fast coding processing and real-time processing. In particular, High Efficiency Video Coding inter-coding has heavy computational complexity, and the High Efficiency Video Coding inter prediction uses reference pictures to improve coding efficiency. The reference pictures are typically signaled in two independent lists according to the display order, to be used for forward and backward prediction. If an event occurs in the input video, such as a scene change, the inter prediction performs unnecessary computations. Therefore, the reference picture list should be reconfigured to improve the inter prediction performance and reduce computational complexity. To address this problem, this article proposes a method to reduce computational complexity for fast High Efficiency Video Coding encoding using information such as scene changes obtained from the input video through preprocessing. Furthermore, reference picture lists are reconstructed by sorting the reference pictures by similarity to the current coded picture using Angular Second Moment, Contrast, Entropy, and Correlation, which are image texture parameters from the input video. Simulations are used to show that both the encoding time and coding efficiency could be improved simultaneously by applying the proposed algorithms.

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A Fast Mode Decision Algorithm for Intra Prediction in High Efficiency Video Coding

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.2896 ◽

2020 ◽

Vol 10 (2) ◽

pp. 496-501

Author(s):

Wen Si ◽

Qian Zhang ◽

Zhengcheng Shi ◽

Bin Wang ◽

Tao Yan ◽

...

Keyword(s):

Video Coding ◽

High Efficiency ◽

Rate Increase ◽

Intra Prediction ◽

High Efficiency Video Coding ◽

Candidate List ◽

Decision Algorithm ◽

Coding Efficiency ◽

Gradient Based ◽

Coding Unit

High Efficiency Video Coding (HEVC) is the next generation video coding standard. In HEVC, 35 intra prediction modes are defined to improve coding efficiency, which result in huge computational complexity, as a large number of prediction modes and a flexible coding unit (CU) structure is adopted in CU coding. To reduce this computational burden, this paper presents a gradient-based candidate list clipping algorithm for Intra mode prediction. Experimental results show that the proposed algorithm can reduce 29.16% total encoding time with just 1.34% BD-rate increase and –0.07 dB decrease of BD-PSNR.

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