Fast HEVC Encoding Decisions Using Data Mining

Abstract-Determining the best partitioning structure of a Coding Tree Unit (CTU) is one of the most time consuming operations in HEVC encoding. Specifically, it is the evaluation of the quadtree hierarchy using the Rate-Distortion (RD) optimization that has the most significant impact on the encoding time, especially in the cases of High Definition (HD) and Ultra High Definition (UHD) videos. In order to expedite the encoding for low delay applications, this paper proposes a Coding Unit (CU) size selection and encoding algorithm for inter-prediction in the HEVC. To this end, it describes (i) two CU classification models based on Inter N×N mode motion features and RD cost thresholds to predict the CU split decision, (ii) an online training scheme for dynamic content adaptation, (iii) a motion vector reuse mechanism to expedite the motion estimation process, and finally introduces (iv) a computational complexity to coding efficiency trade-off process to enable flexible control of the algorithm. The experimental results reveal that the proposed algorithm achieves a consistent average encoding time performance ranging from 55% -58% and 57% -61% with average Bjøntegaard Delta Bit Rate (BDBR) increases of 1.93% -2.26% and 2.14% -2.33% compared to the HEVC 16.0 reference software for the low delay P and low delay B configurations, respectively, across a wide range of content types and bit rates.

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“…Correa et al [19] Early CU termination using the offline trained decision trees and threshold values.…”

Section: B Related Workmentioning

confidence: 99%

Content-Adaptive Feature-Based CU Size Prediction for Fast Low-Delay Video Encoding in HEVC

Mallikarachchi

Talagala

Arachchi

et al. 2018

IEEE Trans. Circuits Syst. Video Technol.

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“…A set of procedures for partition structure determination is designed in Ref. [18] to early terminate exhaustive RDO process using data mining techniques. An early terminating fast intermode decision algorithm, namely hierarchical intermode decision (HIMD), is presented in Ref.…”

Section: The Related Previous Workmentioning

confidence: 99%

Efficient AMP decision and search range adjustment algorithm for HEVC

Zhang

2017

J Image Video Proc.

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The advanced video encoder High Efficiency Video Coding (HEVC) utilizes several novel coding tools so that it can obtain improvement in coding performance for a huge number of video data. However, these tools increase the computational complexity greatly specially in the interprediction phase. Therefore, optimization for interprediction plays an important role in accelerating the whole HEVC encoding process. To this end, this paper proposes an efficient prediction algorithm for improving HEVC intercoding. According to the spatial correlation between the current block and its co-located block in the previous frame, we design a fast decision method so as to perform asymmetric partitions directly without the complicated calculation. Furthermore, based on the motion degree function we defined, the motion search range of motion estimation can be adaptively adjusted. Experimental results demonstrate that the proposed algorithm is able to save coding time significantly both under RandomAccess and Lowdelay P configurations, with a negligible drawback in term of Bjontegaard delta-rate increase.

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“…Support vector machines have been used in [18]. More recently, a decision tree based method has been proposed to reduce the complexity of the HEVC encoder [19] . Although it is reported that the complexity of the HEVC encoder is reduced by the aforementioned techniques, the reduction is still limited.…”

Section: A Fast Transcoding Techniquesmentioning

confidence: 99%

“…However, the significant performance improvement of our proposed meth- ods implies that the proposed methods are notably efficient in reducing the complexity of an HEVC transcoder. Since each reference work yields different values of BDBR and time saving, we obtain the ratio between BDBR and time saving (B/T) [19]. This parameter shows the amount of BDBR loss per time saving.…”

Section: Performance Comparison With the State-of-the-artmentioning

confidence: 99%

Efficient Bit Rate Transcoding for High Efficiency Video Coding

Van

Praeter

Wallendael

et al. 2016

IEEE Trans. Multimedia

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Abstract-High efficiency video coding (HEVC) shows a significant advance in compression efficiency and is considered to be the successor of H.264/AVC. To incorporate the HEVC standard into real-life network applications and a diversity of other applications, efficient bit rate adaptation (transrating) algorithms are required. A current problem of transrating for HEVC is the high computational complexity associated with the encoder part of such a cascaded pixel domain transcoder. This paper focuses on deriving an optimal strategy for reducing the transcoding complexity with a complexity-scalable scheme. We propose different transcoding techniques which are able to reduce the transcoding complexity in both CU and PU optimization levels. At the CU level, CUs can be evaluated in top-to-bottom or bottom-to-top flows, in which the coding information of the input video stream is utilized to reduce the number of evaluations or to early terminate certain evaluations. At the PU level, the PU candidates are adaptively selected based on the probability of PU sizes and the co-located input PU partitioning. Moreover, with the use of different proposed methods, a complexity-scalable transrating scheme can be achieved. Furthermore, the transcoding complexity can be effectively controlled by the machine learning based approach. Simulations show that the proposed techniques provide a superior transcoding performance compared to the state-of-the-art related works. Additionally, the proposed methods can achieve a range of trade-offs between transrating complexity and coding performance. From the proposed schemes, the fastest approach is able to reduce the complexity by 82% while keeping the bitrate loss below 3%.Index Terms-Bit rate adaptation, complexity scalable transcoding, high efficiency video coding (HEVC), machine learning.

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Fast HEVC Encoding Decisions Using Data Mining

Cited by 170 publications

References 26 publications

Content-Adaptive Feature-Based CU Size Prediction for Fast Low-Delay Video Encoding in HEVC

Content-Adaptive Feature-Based CU Size Prediction for Fast Low-Delay Video Encoding in HEVC

Efficient AMP decision and search range adjustment algorithm for HEVC

Efficient Bit Rate Transcoding for High Efficiency Video Coding

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